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
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Logistic Regression
-
Support Vector Machine
-
Decision Tree Classifier
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Random Forest Classifier
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K-Nearest Neighbors
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NeuralNet Classifier
Getting started
Install the package
pip install fastMLNavigate to folder and install requirements:
pip install -r requirements.txtUsage
Assign the variables X and Y to the desired columns and assign the variable size to the desired test_size.
X = < df.features >
Y = < df.target >
size = < test_size >Encoding Categorical Data
Encode target variable if non-numerical:
from fastML import EncodeCategorical
Y = EncodeCategorical(Y)Using the Neural Net Classifier
from nnclassifier import neuralnetRunning fastML
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
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
Acknowledgements
Thanks to Vincent Njonge, Emmanuel Amoaku, Divine Alorvor, Philemon Johnson, William Akuffo, Labaran Mohammed, Benjamin Acquaah, Silas Bempong and Gal Giacomelli for making this project a success.