ml_utils.py

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score

# define a Gaussain NB classifier
clf = GaussianNB()
clf1 = KNeighborsClassifier(n_neighbors=5)
#clf1 = MLPClassifier(max_iter=500)
clf_better = clf

# define the class encodings and reverse encodings
classes = {0: "Iris Setosa", 1: "Iris Versicolour", 2: "Iris Virginica"}
r_classes = {y: x for x, y in classes.items()}

# function to train and load the model during startup
def load_model():
    # load the dataset from the official sklearn datasets
    X, y = datasets.load_iris(return_X_y=True)

    # do the test-train split and train the model
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    clf.fit(X_train, y_train)     #GaussianNB
    #clf1.fit(X_train, y_train)    #DecisionTreeClassifier
    #clf2.fit(X_train, y_train)    #KNeighborsClassifier
    #clf3.fit(X_train, y_train)    #RandomForestClassifier
    clf1.fit(X_train, y_train)    #MLPClassifier

    # calculate the print the accuracy score
    acc = accuracy_score(y_test, clf.predict(X_test))
    print(f" GaussianNB Model trained with accuracy: {round(acc, 3)}")
    acc1 = accuracy_score(y_test, clf1.predict(X_test))
    print(f" MLP Model trained with accuracy: {round(acc1, 3)}")
    #clf=clf1
    #acc = accuracy_score(y_test, clf1.predict(X_test))
    #print(f" DecisionTreeClassifier Model trained with accuracy: {round(acc, 3)}")
    #clf=clf2
    #acc = accuracy_score(y_test, clf.predict(X_test))
    #clf=clf3
    #print(f" KNeighborsClassifier Model trained with accuracy: {round(acc, 3)}")
    #acc = accuracy_score(y_test, clf.predict(X_test))
    #clf=clf4
    #print(f" RandomForestClassifier Model trained with accuracy: {round(acc, 3)}")
    #acc = accuracy_score(y_test, clf.predict(X_test))
    #print(f" MLPClassifier Model trained with accuracy: {round(acc, 3)}")
# function to predict the flower using the model
    if acc1 > acc:
        clf_better = clf1
    else:
        clf_better = clf

def predict(query_data):
    x = list(query_data.dict().values())
    prediction = clf_better.predict([x])[0]
    print(f"Model prediction: {classes[prediction]}")
    return classes[prediction]

# function to retrain the model as part of the feedback loop
def retrain(data):
    # pull out the relevant X and y from the FeedbackIn object
    X = [list(d.dict().values())[:-1] for d in data]
    y = [r_classes[d.flower_class] for d in data]

    # fit the classifier again based on the new data obtained
    clf.fit(X, y)