Support Vector in scikit a Pythonic Way [Object Oriented]¶

Soumil Nitin Shah¶

Bachelor in Electronic Engineering | Masters in Electrical Engineering | Master in Computer Engineering |

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Hello! I’m Soumil Nitin Shah, a Software and Hardware Developer based in New York City. I have completed by Bachelor in Electronic Engineering and my Double master’s in Computer and Electrical Engineering. I Develop Python Based Cross Platform Desktop Application , Webpages , Software, REST API, Database and much more I have more than 2 Years of Experience in Python

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import pandas as pd
import seaborn as sns

from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.model_selection import learning_curve, GridSearchCV

from sklearn.metrics import classification_report, confusion_matrix
import scikitplot as skplt

class NN():
    
    def __init__(self, max_iter=150):
        self.max_iter=max_iter
        self.X_Train, self.X_Test, self.Y_Train, self.Y_Test = self.preprocess
        self.model = self.create_model
    
    @property
    def preprocess(self):
        
        df = pd.DataFrame(data=load_breast_cancer()["data"], columns=load_breast_cancer()["feature_names"])
        df["target"] = load_breast_cancer()["target"]
        X_Data = df[['mean radius', 'mean texture', 'mean perimeter', 'mean area',
                     'mean smoothness', 'mean compactness', 'mean concavity',
                       'mean concave points', 'mean symmetry', 'mean fractal dimension',
                       'radius error', 'texture error', 'perimeter error', 'area error',
                       'smoothness error', 'compactness error', 'concavity error',
                       'concave points error', 'symmetry error', 'fractal dimension error',
                       'worst radius', 'worst texture', 'worst perimeter', 'worst area',
                       'worst smoothness', 'worst compactness', 'worst concavity',
                       'worst concave points', 'worst symmetry', 'worst fractal dimension']] 
        
        Y_Data = df["target"]
        
        
        X_Train, X_Test, Y_Train, Y_Test = train_test_split(X_Data, Y_Data, test_size=0.4, random_state=101)
        return X_Train, X_Test, Y_Train, Y_Test 
    

    @property
    def create_model(self):
        """
        return : Model Object 
        """
        param_grid= {'C':[0.1,1,10,100,1000],'gamma':[1,0.1,0.01,0.01,0.0001]}
        model =GridSearchCV(SVC(), param_grid=param_grid , verbose=3)
        return model
        
    
    @property
    def train(self):
        """
        return None Train the Model
        """
        self.model.fit(self.X_Train, self.Y_Train)
        
    @property   
    def test(self):
        """
        return pred [Array ]
        return coef_ [array]
        return intercept_ [array]
        """
        pred = self.model.predict(self.X_Test)
        return pred
    
    @property
    def download_report(self):
        """
        return confusion matrix 
        return classification report
        return plots the confusion matrix 
        """
        pred = self.test
        report = classification_report(self.Y_Test, pred)
        matrix = confusion_matrix(self.Y_Test, pred)
        
        skplt.metrics.plot_confusion_matrix(self.Y_Test, 
                                            pred,
                                           figsize=(6,6),
                                           title="Confusion Matrix")
        return report, matrix
    
    @property
    def plot(self):
        pass

neural = NN()
neural.train
pred = neural.test
report, matrix = neural.download_report
print(report)
print(matrix)

/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.
  warnings.warn(CV_WARNING, FutureWarning)
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s

Fitting 3 folds for each of 25 candidates, totalling 75 fits
[CV] C=0.1, gamma=1 ..................................................
[CV] ......... C=0.1, gamma=1, score=0.6228070175438597, total=   0.0s
[CV] C=0.1, gamma=1 ..................................................
[CV] ......... C=0.1, gamma=1, score=0.6228070175438597, total=   0.0s
[CV] C=0.1, gamma=1 ..................................................
[CV] ......... C=0.1, gamma=1, score=0.6194690265486725, total=   0.0s
[CV] C=0.1, gamma=0.1 ................................................
[CV] ....... C=0.1, gamma=0.1, score=0.6228070175438597, total=   0.0s
[CV] C=0.1, gamma=0.1 ................................................
[CV] ....... C=0.1, gamma=0.1, score=0.6228070175438597, total=   0.0s
[CV] C=0.1, gamma=0.1 ................................................
[CV] ....... C=0.1, gamma=0.1, score=0.6194690265486725, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[CV] ...... C=0.1, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[CV] ...... C=0.1, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[CV] ...... C=0.1, gamma=0.01, score=0.6194690265486725, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[CV] ...... C=0.1, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[CV] ...... C=0.1, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[CV] ...... C=0.1, gamma=0.01, score=0.6194690265486725, total=   0.0s
[CV] C=0.1, gamma=0.0001 .............................................
[CV] .... C=0.1, gamma=0.0001, score=0.9122807017543859, total=   0.0s
[CV] C=0.1, gamma=0.0001 .............................................
[CV] .... C=0.1, gamma=0.0001, score=0.9473684210526315, total=   0.0s
[CV] C=0.1, gamma=0.0001 .............................................
[CV] ..... C=0.1, gamma=0.0001, score=0.911504424778761, total=   0.0s
[CV] C=1, gamma=1 ....................................................
[CV] ........... C=1, gamma=1, score=0.6228070175438597, total=   0.0s
[CV] C=1, gamma=1 ....................................................
[CV] ........... C=1, gamma=1, score=0.6228070175438597, total=   0.0s
[CV] C=1, gamma=1 ....................................................
[CV] ........... C=1, gamma=1, score=0.6194690265486725, total=   0.0s
[CV] C=1, gamma=0.1 ..................................................
[CV] ......... C=1, gamma=0.1, score=0.6228070175438597, total=   0.0s
[CV] C=1, gamma=0.1 ..................................................
[CV] ......... C=1, gamma=0.1, score=0.6228070175438597, total=   0.0s
[CV] C=1, gamma=0.1 ..................................................
[CV] ......... C=1, gamma=0.1, score=0.6194690265486725, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ........ C=1, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ........ C=1, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ........ C=1, gamma=0.01, score=0.6194690265486725, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ........ C=1, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ........ C=1, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ........ C=1, gamma=0.01, score=0.6194690265486725, total=   0.0s
[CV] C=1, gamma=0.0001 ...............................................
[CV] ...... C=1, gamma=0.0001, score=0.9473684210526315, total=   0.0s
[CV] C=1, gamma=0.0001 ...............................................
[CV] ...... C=1, gamma=0.0001, score=0.9649122807017544, total=   0.0s
[CV] C=1, gamma=0.0001 ...............................................
[CV] ...... C=1, gamma=0.0001, score=0.9469026548672567, total=   0.0s
[CV] C=10, gamma=1 ...................................................
[CV] .......... C=10, gamma=1, score=0.6228070175438597, total=   0.0s
[CV] C=10, gamma=1 ...................................................
[CV] .......... C=10, gamma=1, score=0.6228070175438597, total=   0.0s
[CV] C=10, gamma=1 ...................................................
[CV] .......... C=10, gamma=1, score=0.6194690265486725, total=   0.0s
[CV] C=10, gamma=0.1 .................................................
[CV] ........ C=10, gamma=0.1, score=0.6228070175438597, total=   0.0s
[CV] C=10, gamma=0.1 .................................................
[CV] ........ C=10, gamma=0.1, score=0.6228070175438597, total=   0.0s
[CV] C=10, gamma=0.1 .................................................
[CV] ........ C=10, gamma=0.1, score=0.6194690265486725, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] ....... C=10, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] ....... C=10, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] ....... C=10, gamma=0.01, score=0.6194690265486725, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] ....... C=10, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] ....... C=10, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] ....... C=10, gamma=0.01, score=0.6194690265486725, total=   0.0s
[CV] C=10, gamma=0.0001 ..............................................
[CV] ..... C=10, gamma=0.0001, score=0.9385964912280702, total=   0.0s
[CV] C=10, gamma=0.0001 ..............................................
[CV] ..... C=10, gamma=0.0001, score=0.9473684210526315, total=   0.0s
[CV] C=10, gamma=0.0001 ..............................................
[CV] ..... C=10, gamma=0.0001, score=0.9469026548672567, total=   0.0s
[CV] C=100, gamma=1 ..................................................
[CV] ......... C=100, gamma=1, score=0.6228070175438597, total=   0.0s
[CV] C=100, gamma=1 ..................................................
[CV] ......... C=100, gamma=1, score=0.6228070175438597, total=   0.0s
[CV] C=100, gamma=1 ..................................................
[CV] ......... C=100, gamma=1, score=0.6194690265486725, total=   0.0s
[CV] C=100, gamma=0.1 ................................................
[CV] ....... C=100, gamma=0.1, score=0.6228070175438597, total=   0.0s
[CV] C=100, gamma=0.1 ................................................
[CV] ....... C=100, gamma=0.1, score=0.6228070175438597, total=   0.0s
[CV] C=100, gamma=0.1 ................................................
[CV] ....... C=100, gamma=0.1, score=0.6194690265486725, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ...... C=100, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ...... C=100, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ...... C=100, gamma=0.01, score=0.6194690265486725, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ...... C=100, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ...... C=100, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ...... C=100, gamma=0.01, score=0.6194690265486725, total=   0.0s
[CV] C=100, gamma=0.0001 .............................................
[CV] .... C=100, gamma=0.0001, score=0.9298245614035088, total=   0.0s
[CV] C=100, gamma=0.0001 .............................................
[CV] .... C=100, gamma=0.0001, score=0.9473684210526315, total=   0.0s
[CV] C=100, gamma=0.0001 .............................................
[CV] .... C=100, gamma=0.0001, score=0.9203539823008849, total=   0.0s
[CV] C=1000, gamma=1 .................................................
[CV] ........ C=1000, gamma=1, score=0.6228070175438597, total=   0.0s
[CV] C=1000, gamma=1 .................................................
[CV] ........ C=1000, gamma=1, score=0.6228070175438597, total=   0.0s
[CV] C=1000, gamma=1 .................................................
[CV] ........ C=1000, gamma=1, score=0.6194690265486725, total=   0.0s
[CV] C=1000, gamma=0.1 ...............................................
[CV] ...... C=1000, gamma=0.1, score=0.6228070175438597, total=   0.0s
[CV] C=1000, gamma=0.1 ...............................................
[CV] ...... C=1000, gamma=0.1, score=0.6228070175438597, total=   0.0s
[CV] C=1000, gamma=0.1 ...............................................
[CV] ...... C=1000, gamma=0.1, score=0.6194690265486725, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] ..... C=1000, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] ..... C=1000, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] ..... C=1000, gamma=0.01, score=0.6194690265486725, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] ..... C=1000, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] ..... C=1000, gamma=0.01, score=0.6228070175438597, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] ..... C=1000, gamma=0.01, score=0.6194690265486725, total=   0.0s
[CV] C=1000, gamma=0.0001 ............................................
[CV] ... C=1000, gamma=0.0001, score=0.9035087719298246, total=   0.0s
[CV] C=1000, gamma=0.0001 ............................................
[CV] ... C=1000, gamma=0.0001, score=0.9473684210526315, total=   0.0s
[CV] C=1000, gamma=0.0001 ............................................
[CV] ... C=1000, gamma=0.0001, score=0.9292035398230089, total=   0.0s

[Parallel(n_jobs=1)]: Done  75 out of  75 | elapsed:    0.8s finished

              precision    recall  f1-score   support

           0       0.90      0.89      0.90        83
           1       0.94      0.94      0.94       145

   micro avg       0.93      0.93      0.93       228
   macro avg       0.92      0.92      0.92       228
weighted avg       0.93      0.93      0.93       228

[[ 74   9]
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