Upload visualize.py

visualize.py

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+# 本文用到的库
+import numpy as np
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+import base64
+import streamlit as st
+from sklearn import preprocessing
+from dtreeviz.trees import *
+from data import getDataSetOrigin,dataPreprocessing
+import joblib
+from DecisionTree import dt_param_selector
+import numpy as np
+import matplotlib.pyplot as plt
+from data import dataPreprocessing
+from sklearn.tree import DecisionTreeClassifier
+import streamlit as st
+
+def decisionTreeViz(clf):
+    df = dataPreprocessing()   
+    X, y = df[df.columns[:-1]], df["label"]
+    viz = dtreeviz(
+        clf,
+        X,
+        y,
+        orientation="LR",
+        target_name="label",
+        feature_names=df.columns[:-1],
+        class_names=["good", "bad"],  # need class_names for classifier
+    )
+
+    return viz
+
+
+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, width=800, caption="决策树")
+
+def plotSurface():
+    st.set_option('deprecation.showPyplotGlobalUse', False)
+    # Parameters
+    n_classes = 2
+    plot_colors = "ryb"
+    plot_step = 0.02
+
+    # Load data
+    df = dataPreprocessing()
+    plt.figure(figsize=(8,4))
+    for pairidx, pair in enumerate([[1, 0], [1, 3], [1, 4], [1, 5],
+    [3, 0], [3, 2], [3, 4], [3, 5]]):
+        # We only take the two corresponding features
+        X, y = df[df.columns[:-1]].values[:, pair], df["label"]
+
+        # Train
+        clf = DecisionTreeClassifier().fit(X, y)
+
+        # Plot the decision boundary
+        fig=plt.subplot(2, 4, pairidx + 1)
+        
+        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, plot_step), np.arange(y_min, y_max, plot_step)
+        )
+        plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
+
+        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+        Z = Z.reshape(xx.shape)
+        cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)
+
+        plt.xlabel(df.columns[pair[0]])
+        plt.ylabel(df.columns[pair[1]])
+
+        # Plot the training points
+        for i, color in zip(range(n_classes), plot_colors):
+            idx = np.where(y == i)
+            plt.scatter(
+                X[idx, 0],
+                X[idx, 1],
+                c=color,
+                label=df["label"][i],
+                cmap=plt.cm.RdYlBu,
+                edgecolor="black",
+                s=15,
+            )
+    plt.suptitle("Decision surface of a decision tree using paired features")
+    plt.legend(loc="lower right", borderpad=0, handletextpad=0)
+    plt.axis("tight")
+    # plt.show()
+    plt.tight_layout()
+    st.pyplot()