Module Diabetes-Detection-Web-Application.testing.src.modelbuilding.model

Classes

class ModelBuilding

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    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.model_selection import cross_val_score, KFold
    from sklearn.metrics import mean_squared_error
    import matplotlib.pyplot as plt
    from src.preprocessing.plots import Plot 
    
    class ModelBuilding:
    
        def __init__(self, df, path=None, X_train=None, X_test=None,
        y_train=None, y_test=None):
            self.data = df
            self.X_train = X_train
            self.X_test = X_test
            self.y_train = y_train
            self.y_test = y_test
            self.y_pred = None
            Plot(df, path=path)
            self.split()
            self.train()
            self.predict()
    
    
        def split(self):
            X = self.data[['Pregnancies', 'Glucose', 'BloodPressure',
           'SkinThickness', 'Insulin',
           'BMI', 'DiabetesPedigreeFunction', 'Age']]
            y = self.data['Outcome']
            self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y,
            test_size=0.3)
        
        def train(self):
            self.rcb = RandomForestClassifier()
            self.rcb.fit(self.X_train, self.y_train)
            score = self.rcb.score(self.X_train, self.y_train)
            print("Training score: ", score)
        
        def predict(self):
            self.y_pred = self.rcb.predict(self.X_test)
            mse = mean_squared_error(self.y_test, self.y_pred)
            print(mse)
    
        def visualize(self, path):
            fig, ax = plt.subplots()
            x_ax = range(len(self.y_test))
            plt.plot(x_ax, self.y_test, label="original")
            plt.plot(x_ax, self.y_pred, label="predicted")
            plt.title("test and predicted data")
            plt.legend()
            plt.savefig(path)
    

Functions

def split(self)

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def split(self):
    X = self.data[['Pregnancies', 'Glucose', 'BloodPressure',
   'SkinThickness', 'Insulin',
   'BMI', 'DiabetesPedigreeFunction', 'Age']]
    y = self.data['Outcome']
    self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y,
    test_size=0.3)

def train(self)

Expand source code

def train(self):
    self.rcb = RandomForestClassifier()
    self.rcb.fit(self.X_train, self.y_train)
    score = self.rcb.score(self.X_train, self.y_train)
    print("Training score: ", score)

def predict(self)

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def predict(self):
    self.y_pred = self.rcb.predict(self.X_test)
    mse = mean_squared_error(self.y_test, self.y_pred)
    print(mse)

def visualize(self, path)

Expand source code

def visualize(self, path):
        fig, ax = plt.subplots()
        x_ax = range(len(self.y_test))
        plt.plot(x_ax, self.y_test, label="original")
        plt.plot(x_ax, self.y_pred, label="predicted")
        plt.title("test and predicted data")
        plt.legend()
        plt.savefig(path)