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توضیحات

A Python package built with sklearn for running multiple classification algorithms to observe their behaviour in as little as 4 lines. This package drastically makes the work of Data Scientists, AI and ML engineers very easy and fast by saving them the physical stress of writing close to 300 lines of code as they would if not for this package.

ویژگی	مقدار
سیستم عامل	-
نام فایل	fastML-1.0
نام	fastML
نسخه کتابخانه	1.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Jerry Buaba
ایمیل نویسنده	buabajerry@gmail.com
آدرس صفحه اصلی	https://github.com/buabaj
آدرس اینترنتی	https://pypi.org/project/fastML/
مجوز	MIT

# fastML -------- A Python package built with sklearn for running multiple classification algorithms in as little as 4 lines. This package drastically makes the work of Data Scientists, AI and ML engineers very easy and fast by saving them the physical stress of writing close to 200 lines of code as they would if not for this package. # Algorithms ------------ - ### Logistic Regression - ### Support Vector Machine - ### Decision Tree Classifier - ### Random Forest Classifier - ### K-Nearest Neighbors - ### NeuralNet Classifier -------------------------- # Getting started ----------------- ## Install the package ```bash pip install fastML ``` Navigate to folder and install requirements: ```bash pip install -r requirements.txt ``` ## Usage Assign the variables X and Y to the desired columns and assign the variable size to the desired test_size. ```python X = < df.features > Y = < df.target > size = < test_size > ``` ## Encoding Categorical Data Encode target variable if non-numerical: ```python from fastML import EncodeCategorical Y = EncodeCategorical(Y) ``` ## Using the Neural Net Classifier ``` from nnclassifier import neuralnet ``` ## Running fastML ```python fastML(X, Y, size, RandonForestClassifier(), DecisionTreeClassifier(), KNeighborsClassifier(), SVC(), include_special_classifier = True, # to include the neural net classifier special_classifier_epochs=200, special_classifier_nature ='fixed' ) ``` You may also check the test.py file to see the use case. ## Example output ```python Using TensorFlow backend. __ _ __ __ _ / _| | | | \/ | | | |_ __ _ ___| |_| \ / | | | _/ _` / __| __| |\/| | | | || (_| \__ \ |_| | | | |____ |_| \__,_|___/\__|_| |_|______| ____________________________________________________ ____________________________________________________ Accuracy Score for SVC is 0.9811320754716981 Confusion Matrix for SVC is [[16 0 0] [ 0 20 1] [ 0 0 16]] Classification Report for SVC is precision recall f1-score support 0 1.00 1.00 1.00 16 1 1.00 0.95 0.98 21 2 0.94 1.00 0.97 16 accuracy 0.98 53 macro avg 0.98 0.98 0.98 53 weighted avg 0.98 0.98 0.98 53 ____________________________________________________ ____________________________________________________ ____________________________________________________ ____________________________________________________ Accuracy Score for RandomForestClassifier is 0.9622641509433962 Confusion Matrix for RandomForestClassifier is [[16 0 0] [ 0 20 1] [ 0 1 15]] Classification Report for RandomForestClassifier is precision recall f1-score support 0 1.00 1.00 1.00 16 1 0.95 0.95 0.95 21 2 0.94 0.94 0.94 16 accuracy 0.96 53 macro avg 0.96 0.96 0.96 53 weighted avg 0.96 0.96 0.96 53 ____________________________________________________ ____________________________________________________ ____________________________________________________ ____________________________________________________ Accuracy Score for DecisionTreeClassifier is 0.9622641509433962 Confusion Matrix for DecisionTreeClassifier is [[16 0 0] [ 0 20 1] [ 0 1 15]] Classification Report for DecisionTreeClassifier is precision recall f1-score support 0 1.00 1.00 1.00 16 1 0.95 0.95 0.95 21 2 0.94 0.94 0.94 16 accuracy 0.96 53 macro avg 0.96 0.96 0.96 53 weighted avg 0.96 0.96 0.96 53 ____________________________________________________ ____________________________________________________ ____________________________________________________ ____________________________________________________ Accuracy Score for KNeighborsClassifier is 0.9811320754716981 Confusion Matrix for KNeighborsClassifier is [[16 0 0] [ 0 20 1] [ 0 0 16]] Classification Report for KNeighborsClassifier is precision recall f1-score support 0 1.00 1.00 1.00 16 1 1.00 0.95 0.98 21 2 0.94 1.00 0.97 16 accuracy 0.98 53 macro avg 0.98 0.98 0.98 53 weighted avg 0.98 0.98 0.98 53 ____________________________________________________ ____________________________________________________ ____________________________________________________ ____________________________________________________ Accuracy Score for LogisticRegression is 0.9811320754716981 Confusion Matrix for LogisticRegression is [[16 0 0] [ 0 20 1] [ 0 0 16]] Classification Report for LogisticRegression is precision recall f1-score support 0 1.00 1.00 1.00 16 1 1.00 0.95 0.98 21 2 0.94 1.00 0.97 16 accuracy 0.98 53 macro avg 0.98 0.98 0.98 53 weighted avg 0.98 0.98 0.98 53 ____________________________________________________ ____________________________________________________ Included special classifier with fixed nature Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 4) 20 _________________________________________________________________ dense_2 (Dense) (None, 16) 80 _________________________________________________________________ dense_3 (Dense) (None, 3) 51 ================================================================= Total params: 151 Trainable params: 151 Non-trainable params: 0 _________________________________________________________________ Train on 97 samples, validate on 53 samples Epoch 1/200 97/97 [==============================] - 0s 1ms/step - loss: 1.0995 - accuracy: 0.1443 - val_loss: 1.1011 - val_accuracy: 0.3019 97/97 [==============================] - 0s 63us/step - loss: 0.5166 - accuracy: 0.7010 - val_loss: 0.5706 - val_accuracy: 0.6038 Epoch 100/200 97/97 [==============================] - 0s 88us/step - loss: 0.5128 - accuracy: 0.7010 - val_loss: 0.5675 - val_accuracy: 0.6038 Epoch 200/200 97/97 [==============================] - 0s 79us/step - loss: 0.3375 - accuracy: 0.8969 - val_loss: 0.3619 - val_accuracy: 0.9057 97/97 [==============================] - 0s 36us/step ____________________________________________________ ____________________________________________________ Accuracy Score for neuralnet is 0.8969072103500366 Confusion Matrix for neuralnet is [[16 0 0] [ 0 16 5] [ 0 0 16]] Classification Report for neuralnet is precision recall f1-score support 0 1.00 1.00 1.00 16 1 1.00 0.76 0.86 21 2 0.76 1.00 0.86 16 accuracy 0.91 53 macro avg 0.92 0.92 0.91 53 weighted avg 0.93 0.91 0.91 53 ____________________________________________________ ____________________________________________________ Model Accuracy 0 SVC 0.9811320754716981 1 RandomForestClassifier 0.9622641509433962 2 DecisionTreeClassifier 0.9622641509433962 3 KNeighborsClassifier 0.9811320754716981 4 LogisticRegression 0.9811320754716981 5 neuralnet 0.8969072103500366 ``` ## Author: [Jerry Buaba](https://linkedin.com/in/jerry-buaba-768351172) ## Acknowledgements Thanks to [Vincent Njonge](https://linkedin.com/in/vincent-njonge-528070178), [Emmanuel Amoaku](https://linkedin.com/in/emmanuel-amoaku), [Divine Alorvor](https://www.linkedin.com/in/divine-kofi-alorvor-86775117b), [Philemon Johnson](https://linkedin.com/in/philemon-johnson-b95009171), [William Akuffo](https://linkedin.com/in/william-akuffo-26b430159), [Labaran Mohammed](https://linkedin.com/in/adam-labaran-111358181), [Benjamin Acquaah](https://linkedin.com/in/benjamin-acquaah-9294aa14b), [Silas Bempong](https://www.linkedin.com/in/silas-bempong-604916120) and [Gal Giacomelli](https://linkedin.com/in/gal-giacomelli-221679136) for making this project a success.

نحوه نصب

نصب پکیج whl fastML-1.0:

pip install fastML-1.0.whl

نصب پکیج tar.gz fastML-1.0:

pip install fastML-1.0.tar.gz