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393 lines
13 KiB
Plaintext
393 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "c653c8dc-4405-4665-ad7d-d6efb4f8c4ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.datasets import load_iris"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4460cd03-9a31-48bb-804a-1b6abaa1355d",
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"metadata": {},
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"outputs": [],
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"source": [
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"iris = load_iris()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "95a05439-f1d0-48f3-832a-25e3d7045341",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['DESCR',\n",
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" 'data',\n",
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" 'data_module',\n",
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" 'feature_names',\n",
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" 'filename',\n",
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" 'frame',\n",
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" 'target',\n",
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" 'target_names']"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dir(iris)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "fa4945b9-1ad4-41f3-ad03-9047953fdfc6",
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"metadata": {},
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"outputs": [],
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"source": [
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"iris_data = iris[\"data\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "97d82f62-5653-4b8d-a57d-af3e43e3793c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(150, 4)"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"iris_data.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "7deb6bbe-c7ab-460f-ace9-eabe33d08c24",
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"metadata": {},
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"outputs": [],
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"source": [
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"iris_target = iris[\"target\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "327beb9b-e0b7-43ad-9703-f2a6239d47e9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(150,)"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"iris_target.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "a7649a22-7433-4d01-a237-01b8aa9ab85f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "4f5b6339-0d0b-4ed8-8320-0796ea0193ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train, x_test, y_train, y_test = train_test_split(iris_data, iris_target, test_size=0.2, random_state=10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "97a6b729-80ac-4a0e-8220-e9af7f997581",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(150, 120, 30)"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(iris_data), len(x_train), len(x_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "986f2787-c3c1-4f49-8f21-561aa5b9d880",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "9bc3d836-e866-4c81-9197-e1b3afc6ddc5",
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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_euclidean_distance(x, dataset):\n",
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" distance = np.sum(np.sqrt((dataset - x) ** 2), axis=1, keepdims=True)\n",
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" return distance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "e3d5836d-a5bc-4098-a915-d493bce4630a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"120"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(x_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "2f221d57-3fbb-45e2-90dd-ad3ca935da05",
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"metadata": {},
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"outputs": [],
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"source": [
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"def knn(x_test, x_train, labels, k):\n",
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" y_preds = []\n",
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" if len(x_test.shape) == 1:#测试单个数据\n",
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" x_test = np.expand_dims(x_test, axis=0)#扩展维度\n",
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" for i in range(x_test.shape[0]):\n",
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" distances = compute_euclidean_distance(x_test[i], x_train)#计算欧式距离\n",
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" indexes = distances.argsort(axis=0)#距离排序\n",
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" k_most = indexes[:k]#取前k个最短距离\n",
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" labels_most = labels[k_most].flatten()#二维转一维\n",
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" y_preds.append(np.argmax(np.bincount(labels_most)))#选出出现次数最多的标签，类似于投票\n",
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" return np.array(y_preds)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "3ee80916-e5ae-4b7b-b83d-2019b86baba6",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 2, 0, 1, 0, 1, 2, 1, 0, 1, 1, 2, 1, 0, 0, 2, 1, 0, 0, 0, 2, 2,\n",
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" 2, 0, 1, 0, 1, 1, 1, 2], dtype=int64)"
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"knn(x_test, x_train, y_train, 3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"id": "90b3ce57-aeed-489a-9135-edbe32943c61",
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"metadata": {},
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"outputs": [],
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"source": [
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"def accuracy(y_preds, y_true):\n",
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" return (y_preds == y_true).sum() / len(y_true)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"id": "031c3693-e54a-458a-9596-86775ca785ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"y_preds = knn(x_test, x_train, y_train, 8)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "75db42e7-1060-462f-b3be-0a52a491d34a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.0"
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]
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},
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"execution_count": 38,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"accuracy(y_preds, y_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"id": "23d91fd3-e21e-4987-b710-b95f90bfb1f9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.neighbors import KNeighborsClassifier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"id": "c9db8922-65a3-45cd-89d5-9e70349c7570",
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"metadata": {},
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"outputs": [],
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"source": [
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"nb = KNeighborsClassifier(n_neighbors=8, p=0.5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"id": "cf6f1f7d-ce1e-4f55-a8d9-67d27a1e91e2",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"D:\\envs\\stark\\lib\\site-packages\\sklearn\\neighbors\\_base.py:632: UserWarning: Mind that for 0 < p < 1, Minkowski metrics are not distance metrics. Continuing the execution with `algorithm='brute'`.\n",
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" warnings.warn(\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=8, p=0.5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=8, p=0.5)</pre></div></div></div></div></div>"
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],
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"text/plain": [
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"KNeighborsClassifier(n_neighbors=8, p=0.5)"
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]
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},
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"execution_count": 61,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"nb.fit(x_train, y_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"id": "6a2cd011-6d8c-4fa9-a5cd-863142341a02",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.0"
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]
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},
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"execution_count": 62,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"nb.score(x_test, y_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ce164fb4-b5eb-45e7-a8cf-46799589f88c",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "64e44923-7989-45ba-9ac8-a0b259568ae9",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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|
"pygments_lexer": "ipython3",
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"version": "3.8.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|