fastML 1.0

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.

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

pip install fastML

Navigate to folder and install requirements:

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.

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 neuralnet

Running 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.