main.py

import streamlit as st
from sklearn import datasets
import numpy as np
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
import matplotlib.pyplot as plt

st.title("Stramlit App")

st.write("""
         # Different classifier
          """)

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

classifier_name = st.sidebar.selectbox(
    "Select Classifier", ("KNN", "SVM", "Random forest"))


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


x, y = get_dataset(dataset_name)
st.write("Shape of dataset", x.shape)
st.write("Shape of classes", len(np.unique(y)))


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", 0.01, 10.0)
        params["C"] = C
    else:
        max_depth = st.sidebar.slider("max_depth", 2, 15)
        no_of_estimators = st.sidebar.slider("no_of_estimators", 1, 100)
        params["max_depth"] = max_depth
        params["no_of_estimators"] = no_of_estimators
    return params


params = add_parameter_ui(classifier_name)


def get_classiffier(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["no_of_estimators"])

    return clf


clf = get_classiffier(classifier_name, params)

x_train, x_test, y_train, y_test = train_test_split(
    x, y, test_size=0.2, random_state=1234)

clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
acc = accuracy_score(y_test, y_pred)

st.write(f"classifier = {classifier_name}")
st.write(f"accuracy = {acc}")

# Plot

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=0.8, cmap="viridis")
plt.xlabel("Principal component 1")
plt.ylabel("Principal component 2")
plt.colorbar()

# plt.show()
st.pyplot()