Create decision_tree_steps.py

decision_tree_steps.py

@@ -0,0 +1,85 @@
+import matplotlib.pyplot as plt
+import streamlit as st
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import make_moons
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.tree import plot_tree
+from sklearn.tree import export_graphviz
+from os import system
+from graphviz import Source
+from sklearn import tree
+
+def draw_meshgrid():
+    a = np.arange(start=X[:, 0].min() - 1, stop=X[:, 0].max() + 1, step=0.01)
+    b = np.arange(start=X[:, 1].min() - 1, stop=X[:, 1].max() + 1, step=0.01)
+
+    XX, YY = np.meshgrid(a, b)
+
+    input_array = np.array([XX.ravel(), YY.ravel()]).T
+
+    return XX, YY, input_array
+
+X, y = make_moons(n_samples=500, noise=0.30, random_state=42)
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+
+plt.style.use('fivethirtyeight')
+
+st.sidebar.markdown("# Decision Tree Classifier")
+
+criterion = st.sidebar.selectbox(
+    'Criterion',
+    ('gini', 'entropy')
+)
+
+splitter = st.sidebar.selectbox(
+    'Splitter',
+    ('best', 'random')
+)
+
+max_depth = int(st.sidebar.number_input('Max Depth'))
+
+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=1235)
+
+max_features = st.sidebar.slider('Max Features', 1, 2, 2,key=1236)
+
+max_leaf_nodes = int(st.sidebar.number_input('Max Leaf Nodes'))
+
+min_impurity_decrease = st.sidebar.number_input('Min Impurity Decrease')
+
+# Load initial graph
+fig, ax = plt.subplots()
+
+# Plot initial graph
+ax.scatter(X.T[0], X.T[1], c=y, cmap='rainbow')
+orig = st.pyplot(fig)
+
+if st.sidebar.button('Run Algorithm'):
+
+    orig.empty()
+
+    if max_depth == 0:
+        max_depth = None
+
+    if max_leaf_nodes == 0:
+        max_leaf_nodes = None
+
+    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)
+
+    XX, YY, input_array = draw_meshgrid()
+    labels = clf.predict(input_array)
+
+    ax.contourf(XX, YY, labels.reshape(XX.shape), alpha=0.5, cmap='rainbow')
+    plt.xlabel("Col1")
+    plt.ylabel("Col2")
+    orig = st.pyplot(fig)
+    st.subheader("Accuracy for Decision Tree  " + str(round(accuracy_score(y_test, y_pred), 2)))
+
+    tree = export_graphviz(clf,feature_names=["Col1","Col2"])
+
+    st.graphviz_chart(tree)