main.py

#import libraries
import streamlit as st
import numpy as np
#import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.decomposition import PCA

#header
st.title('Streamlit Example')

#subheader
st.write("""
# Explore different classifier
Which one is the best?
""")

#sidebar dataset selection
dataset_name = st.sidebar.selectbox("Select Dataset", ("Iris", "Breast Cancer", "Wine"))

#sidebar classifier selection
classifier_name = st.sidebar.selectbox("Select Classifier", ("KNN", "SVM", "Random Forest"))

#get_dataset function
def get_dataset(dataset_name):
    if dataset_name == "Iris":
        data = datasets.load_iris()
    elif dataset_name == "Breast Cancer":
        data = datasets.load_breast_cancer()
    else:
        data = datasets.load_wine()

    X = data.data
    y = data.target
    return X, y

#load dataset based on user selection
X,y = get_dataset(dataset_name)

#display dataset details
st.write("Shape of dataset:", X.shape)
st.write("Number of classes in dataset:", len(np.unique(y)))

#add_parameter_ui function
def add_parameter_ui(clf_name):
    params = dict()
    if clf_name == "KNN":
        K = st.sidebar.slider("K", 1,15)
        params["K"] = K
    
    elif clf_name == "SVM":
        C = st.sidebar.slider("C", .01, 10.0)
        params["C"] = C
    
    else:
        max_depth = st.sidebar.slider("max_depth", 2, 15)
        n_estimators = st.sidebar.slider("n_estimators", 2, 15)
        params["max_depth"] = max_depth
        params["n_estimators"] = n_estimators

    return params
    
#display the slider
params = add_parameter_ui(classifier_name)

#get_classifier_function
def get_classifier(clf_name, params):
    if clf_name == "KNN":
        clf = KNeighborsClassifier(n_neighbors=params["K"])

    elif clf_name == "SVM":
        clf = SVC(C=params["C"])
    else:
        clf = RandomForestClassifier(max_depth=params["max_depth"],
                                     n_estimators=params["n_estimators"], random_state = 1234
                                    )
    
    return clf

#retrieve selected classification model
clf = get_classifier(classifier_name, params)

#modelling
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state=1234)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)

#display results
st.write(f"Classifier: {classifier_name}")
st.write(f"Accuracy: {acc}")

#plot results
pca = PCA(2)
X_projected = pca.fit_transform(X)

x1 = X_projected[:,0]
x2 = X_projected[:,1]

#fig = plt.figure()
#plt.scatter(x1, x2, c = y, alpha = .8, cmap = "viridis")
#plt.xlabel("Principal Component 1")
#plt.ylabel("Principal Component 2")
#plt.colorbar()

#plt.show()
#st.set_option('deprecation.showPyplotGlobalUse', False)
#st.pyplot()

# TODO
# - add more parameters
# - add other classifiers
#- improve UI