Upload Sklearn-Decision Tree_gradio.py

Sklearn-Decision Tree_gradio.py

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+
+
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+from six import StringIO
+from sklearn import tree
+import pandas as pd
+import numpy as np
+import pydotplus
+import gradio as gr
+
+
+def Tree_Detection(sample):
+	with open('lenses.txt', 'r') as fr:										# 加载文件
+		lenses = [inst.strip().split('\t') for inst in fr.readlines()]		# 处理文件
+	lenses_target = []														# 提取每组数据的类别，保存在列表里
+	# print(lenses)
+	for each in lenses:
+		lenses_target.append(each[-1])
+	# print(lenses_target)
+
+	lensesLabels = ['noise', 'rotation', 'power-up', 'temp']			# 特征标签
+	lenses_list = []														# 保存lenses数据的临时列表
+	lenses_dict = {}														# 保存lenses数据的字典，用于生成pandas
+	for each_label in lensesLabels:											# 提取信息，生成字典
+		for each in lenses:
+			lenses_list.append(each[lensesLabels.index(each_label)])
+		lenses_dict[each_label] = lenses_list
+		lenses_list = []
+	# print(lenses_dict)														# 打印字典信息
+	lenses_pd = pd.DataFrame(lenses_dict)									# 生成pandas.DataFrame
+	# print(lenses_pd)														# 打印pandas.DataFrame
+	le = LabelEncoder()														# 创建LabelEncoder()对象，用于序列化
+	for col in lenses_pd.columns:											# 序列化
+		lenses_pd[col] = le.fit_transform(lenses_pd[col])
+	# print(lenses_pd)														# 打印编码信息
+
+	clf = tree.DecisionTreeClassifier(max_depth=None)						# 创建DecisionTreeClassifier()类
+	clf = clf.fit(lenses_pd.values.tolist(), lenses_target)					# 使用数据，构建决策树
+
+	dot_data = StringIO()
+	tree.export_graphviz(clf, out_file=dot_data,							# 绘制决策树
+						feature_names=lenses_pd.keys(),
+						class_names=clf.classes_,
+						filled=True, rounded=True,
+						special_characters=True)
+
+	graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
+	# img = graph.write_jpg("tree.jpg")												# 保存绘制好的决策树，以JPG的形式存储。
+
+	result = clf.predict([sample])										# 预测
+
+	return result
+# print(Tree_Detection([2, 1, 1, 0]))
+
+
+demo = gr.Interface(
+	fn=Tree_Detection,
+	inputs='text',
+	outputs='text'
+)
+demo.launch()
+