dtree_regression_hyperparameter.py

import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeRegressor,export_graphviz
from sklearn.metrics import r2_score

n_train=150
n_test = 100
noise=0.1

np.random.seed(0)

def data(x):
    x=x.ravel()
    return np.exp(-x**2)+1.5*np.exp(-(x-2)**2)

def generate_data(n_samples,noise):
    X = np.random.rand(n_samples)*10-5
    X = np.sort(X).ravel()
    y = np.exp(-X**2)+1.5*np.exp(-(X-2)**2)+np.random.normal(0.0,noise,n_samples)
    X = X.reshape((n_samples,1))

    return X,y

X_train,y_train = generate_data(n_samples=n_train,noise=noise)
X_test,y_test = generate_data(n_samples=n_test,noise=noise)

plt.style.use("fivethirtyeight")

st.sidebar.markdown("# Decision Tree Regressor",unsafe_allow_html=True)

criterion = st.sidebar.selectbox("Criterion",("mse",'friedman_mse','mae'))
splitter = st.sidebar.selectbox("Splitter",("best",'random'))
max_depth = int(st.sidebar.number_input("Max Depth",min_value=5,step=5))
min_samples_split = st.sidebar.slider("Min Samples Split",1,X_train.shape[0],2,key=1234)
min_samples_leaf = st.sidebar.slider("Min Samples Leaf",1,X_train.shape[0],1,key=1345)
max_leaf_nodes = int(st.sidebar.number_input("Max Leaf Nodes"))
min_impurity_decrease = st.sidebar.number_input("Min Impurity Decrease")

fig,ax = plt.subplots()

ax.scatter(X_train,y_train,color='yellow',edgecolors='black')
orig = st.pyplot(fig)

if st.sidebar.button("Run Algorithm"):
    if max_depth==0:
        max_depth=None
    if max_leaf_nodes==0:
        max_leaf_nodes=None

    reg = DecisionTreeRegressor(criterion=criterion,splitter=splitter,max_depth=max_depth,min_samples_split=min_samples_split,min_samples_leaf=min_samples_leaf,max_leaf_nodes=max_leaf_nodes,min_impurity_decrease=min_impurity_decrease).fit(X_train, y_train)
    reg_predict = reg.predict(X_test)

    reg_r2 = r2_score(y_test,reg_predict)
    orig.empty()

    st.subheader(f"R2 Score:{round(reg_r2,2)}")
    ax.scatter(X_train,y_train,color='yellow',edgecolors='black')
    ax.plot(X_test,reg_predict,linewidth=1,color='blue')
    ax.legend()    
    orig = st.pyplot(fig)

    tree = export_graphviz(reg,feature_names=['Col1'])
    st.graphviz_chart(tree)