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watermelon-prediction
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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()