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| import streamlit as st | |
| import numpy as np | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.datasets import load_iris | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.metrics import accuracy_score | |
| import matplotlib.pyplot as plt | |
| import os | |
| from os import system | |
| from graphviz import Source | |
| import dtreeviz | |
| import base64 | |
| # Load the Iris dataset from scikit-learn | |
| iris = load_iris() | |
| X = iris.data[:, :2] # Using only two features for visualization purposes | |
| y = iris.target | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) | |
| st.sidebar.markdown("# Decision Tree Classifier") | |
| criterion = st.sidebar.selectbox( | |
| 'Criterion', | |
| ('gini', 'entropy') | |
| ) | |
| splitter = st.sidebar.selectbox( | |
| 'Splitter', | |
| ('best', 'random') | |
| ) | |
| max_depth = st.sidebar.slider('Max Depth', 1, 20) | |
| min_samples_split = st.sidebar.slider('Min Samples Split', 2, 100, 2) | |
| min_samples_leaf = st.sidebar.slider('Min Samples Leaf', 1, 100, 1) | |
| max_features = st.sidebar.slider('Max Features', 1, 4, 2) | |
| max_leaf_nodes = st.sidebar.slider('Max Leaf Nodes', 2, 50) | |
| min_impurity_decrease = st.sidebar.number_input('Min Impurity Decrease') | |
| # Rest of your sidebar inputs... | |
| # Load initial graph | |
| fig, ax = plt.subplots() | |
| # Plot initial graph | |
| ax.scatter(X[:, 0], X[:, 1], c=y, cmap='rainbow') | |
| orig = st.pyplot(fig) | |
| if st.sidebar.button('Run Algorithm'): | |
| orig.empty() | |
| clf = DecisionTreeClassifier(criterion=criterion, splitter=splitter, max_depth=max_depth, random_state=42, | |
| min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, | |
| max_features=max_features, max_leaf_nodes=max_leaf_nodes, | |
| min_impurity_decrease=min_impurity_decrease) | |
| clf.fit(X_train, y_train) | |
| y_pred = clf.predict(X_test) | |
| viz_model = dtreeviz(clf, | |
| X_train=X, y_train=y, | |
| feature_names=iris.feature_names, | |
| target_name='iris', | |
| class_names=iris.target_names) | |
| st.graphviz_chart(viz_model) # render as SVG into internal object | |
| # v.show() # pop up window | |
| # v.save("/tmp/iris.svg") | |
| def svg_write(svg, center=True): | |
| """ | |
| Disable center to left-margin align like other objects. | |
| """ | |
| # Encode as base 64 | |
| b64 = base64.b64encode(svg.encode("utf-8")).decode("utf-8") | |
| # Add some CSS on top | |
| css_justify = "center" if center else "left" | |
| css = f'<p style="text-align:center; display: flex; justify-content: {css_justify};">' | |
| html = f'{css}<img src="data:image/svg+xml;base64,{b64}"/>' | |
| # Write the HTML | |
| st.write(html, unsafe_allow_html=True) | |
| st.write(v) | |
| svg=v.svg() | |
| # svg_write(svg) | |
| st.subheader("Accuracy for Decision Tree: " + str(round(accuracy_score(y_test, y_pred), 2))) |