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| import pandas as pd | |
| import numpy as np | |
| import streamlit as st | |
| import time | |
| from sklearn import datasets | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn import tree | |
| from sklearn.metrics import accuracy_score | |
| import matplotlib.pyplot as plt | |
| st.set_page_config( | |
| page_title="Decision Tree Visualizer", | |
| page_icon=":chart_with_upwards_trend:", | |
| layout="wide", | |
| initial_sidebar_state="expanded") | |
| # load dataset | |
| iris=datasets.load_iris() | |
| x = iris.data | |
| y = iris.target | |
| x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2,random_state=42) | |
| # constants | |
| min_weight_fraction_leaf=0.0 | |
| max_features = None | |
| max_leaf_nodes = None | |
| min_impurity_decrease=0.0 | |
| ccp_alpha = 0.0 | |
| # Load initial graph | |
| fig, ax = plt.subplots() | |
| # Plot initial graph | |
| scatter = ax.scatter(x.T[0], x.T[1], c=y, cmap='rainbow') | |
| ax.set_xlabel(iris.feature_names[0], fontsize=10) | |
| ax.set_ylabel(iris.feature_names[1],fontsize=10) | |
| ax.set_title('Sepal Length vs Sepal Width', fontsize=15) | |
| legend1 = ax.legend(*scatter.legend_elements(), | |
| title="Classes",loc="upper right") | |
| ax.add_artist(legend1) | |
| ax.legend() | |
| orig = st.pyplot(fig) | |
| # sidebar elements | |
| st.sidebar.header(':blue[_Decision Tree_] Algo Visualizer', divider='rainbow') | |
| criterion = st.sidebar.selectbox("Criterion", | |
| ("gini", "entropy", "log_loss"), | |
| help="""The function to measure the quality of a split. | |
| Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” | |
| both for the Shannon information gain""") | |
| max_depth = st.sidebar.number_input("Max Depth", | |
| min_value=0, | |
| max_value=30, | |
| step=1, | |
| value=0, | |
| help="""The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure""") | |
| if max_depth == 0: | |
| max_depth=None | |
| min_samples_split = st.sidebar.number_input("Min Sample Split", | |
| min_value=0, | |
| max_value=x_train.shape[0], | |
| value=2, | |
| help="""The minimum number of samples required to split an internal node. | |
| If float, enter between 0 and 1""") | |
| min_samples_leaf = st.sidebar.number_input("Min sample Leaf", | |
| min_value=0, | |
| max_value=x_train.shape[0], | |
| value=1, | |
| help="""The minimum number of samples required to be at a leaf node. | |
| If float, enter between 0 and 1""") | |
| random_state = st.sidebar.number_input("Random State", | |
| min_value=0, | |
| value=42) | |
| # advance features | |
| toggle = st.sidebar.toggle("Advance Features") | |
| if toggle: | |
| min_weight_fraction_leaf = st.sidebar.number_input("Min Weight Fraction Leaf", | |
| min_value=0.0, | |
| max_value=1.0, | |
| value=0.0, | |
| help="""The minimum weighted fraction of the sum total of weights | |
| (of all the input samples) required to be at a leaf node. """) | |
| max_features = st.sidebar.selectbox("Max Features", | |
| (None,"sqrt", "log2","Custom"), | |
| help="""The number of features to consider when looking for the best split""") | |
| if max_features == "Custom": | |
| max_features = st.sidebar.number_input("Enter Max Features", | |
| value=None, | |
| step=1) | |
| max_leaf_nodes = st.sidebar.number_input("Max Leaf Nodes", | |
| min_value=0, | |
| help="""Grow a tree with max_leaf_nodes in best-first fashion. """) | |
| if max_leaf_nodes==0: | |
| max_leaf_nodes=None | |
| min_impurity_decrease = st.sidebar.number_input("Min Impurity Decrase", | |
| min_value=0.0, | |
| help="""A node will be split if this split induces a decrease of the | |
| impurity greater than or equal to this value.""") | |
| ccp_alpha = st.sidebar.number_input("ccp_alpha", | |
| min_value=0, | |
| max_value=30, | |
| step=0.1, | |
| value=0, | |
| help="""Complexity parameter used for Minimal Cost-Complexity Pruning. | |
| The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. | |
| By default, no pruning is performed.""") | |
| train = st.sidebar.button("Train Model", type="primary") | |
| if st.sidebar.button("Reset"): | |
| st.experimental_rerun() | |
| if train: | |
| orig.empty() | |
| msg = st.toast('Running', icon='🫸🏼') | |
| # building model | |
| clf = DecisionTreeClassifier(criterion=criterion,max_depth=max_depth, | |
| min_samples_split=min_samples_split, | |
| min_samples_leaf=min_samples_leaf, | |
| min_weight_fraction_leaf=min_weight_fraction_leaf, | |
| max_features=max_features, | |
| random_state=random_state, | |
| max_leaf_nodes=max_leaf_nodes, | |
| min_impurity_decrease=min_impurity_decrease, | |
| ccp_alpha = ccp_alpha) | |
| clf.fit(x_train[:, :2], y_train) | |
| x_pred = clf.predict(x_train[:,:2]) | |
| y_pred = clf.predict(x_test[:, :2]) | |
| st.subheader("Train Accuracy " + str(round(accuracy_score(y_train, x_pred), 2)) + ", "+ "Test Accuracy " + str(round(accuracy_score(y_test, y_pred), 2))) | |
| st.write("Total Depth: " + str(clf.tree_.max_depth)) | |
| # # define ranges for meshgrid | |
| x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1 | |
| y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1 | |
| xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01), | |
| np.arange(y_min, y_max, 0.01)) | |
| Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) | |
| Z = Z.reshape(xx.shape) | |
| # Plot the decision boundaries | |
| plt.figure(figsize=(8, 6)) | |
| plt.contourf(xx, yy, Z, alpha=0.8) | |
| plt.scatter(x[:, 0], x[:, 1], c=y, edgecolors='k', s=20) | |
| plt.xlabel('Sepal length') | |
| plt.ylabel('Sepal width') | |
| plt.title('Decision Boundaries') | |
| plt.tight_layout() | |
| plt.savefig('decision_boundary_plot.png') | |
| plt.close() | |
| # Display decision boundary plot | |
| st.image("decision_boundary_plot.png") | |
| # Plot decision tree | |
| plt.figure(figsize=(25, 20)) | |
| tree.plot_tree(clf, feature_names=iris.feature_names, class_names=iris.target_names, filled=True) | |
| plt.xlim(plt.xlim()[0] * 2, plt.xlim()[1] * 2) | |
| plt.ylim(plt.ylim()[0] * 2, plt.ylim()[1] * 2) | |
| plt.savefig("decision_tree.png") | |
| plt.close() | |
| # Display decision tree plot | |
| st.image("decision_tree.png") | |
| msg.toast('Model run successfully!', icon='😎') | |