Repository: MLEveryday/100-Days-Of-ML-Code
Branch: master
Commit: 04e7076df2c8
Files: 45
Total size: 11.5 MB
Directory structure:
gitextract_grz_jkps/
├── .gitignore
├── Code/
│ ├── Day 11_K-NN.ipynb
│ ├── Day 11_K-NN.md
│ ├── Day 11_k-NN.py
│ ├── Day 13_SVM.ipynb
│ ├── Day 13_SVM.md
│ ├── Day 13_SVM.py
│ ├── Day 1_Data_Preprocessing.ipynb
│ ├── Day 1_Data_Preprocessing.md
│ ├── Day 1_Data_Preprocessing.py
│ ├── Day 25_Decision_Tree.ipynb
│ ├── Day 25_Decision_Tree.md
│ ├── Day 25_Decision_Tree.py
│ ├── Day 2_Simple_Linear_Regression.ipynb
│ ├── Day 2_Simple_Linear_Regression.md
│ ├── Day 2_Simple_Linear_Regression.py
│ ├── Day 34_Random_Forests.ipynb
│ ├── Day 34_Random_Forests.md
│ ├── Day 34_Random_Forests.py
│ ├── Day 39.ipynb
│ ├── Day 3_Multiple_Linear_Regression.ipynb
│ ├── Day 3_Multiple_Linear_Regression.md
│ ├── Day 3_Multiple_Linear_Regression.py
│ ├── Day 40.ipynb
│ ├── Day 41.ipynb
│ ├── Day 42.ipynb
│ ├── Day 6_Logistic_Regression.ipynb
│ ├── Day 6_Logistic_Regression.md
│ ├── Day 6_Logistic_Regression.py
│ ├── KafkaProducer.py
│ ├── TestKafka.py
│ └── my/
│ ├── Data_age_salary.csv
│ └── LinerTest.py
├── FAQ.MD
├── LICENSE
├── Other Docs/
│ ├── README.md
│ └── 速查手册/
│ └── README.md
├── README.md
├── Translation specification.MD
└── datasets/
├── 50_Startups.csv
├── Data.csv
├── Social_Network_Ads.csv
├── mnist.npz
├── readme.md
└── studentscores.csv
================================================
FILE CONTENTS
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================================================
FILE: .gitignore
================================================
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================================================
FILE: Code/Day 11_K-NN.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#机器学习100天——第十一天：K近邻法（K-NN）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第一步：导入相关库"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第二步：导入数据集"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" User ID Gender Age EstimatedSalary Purchased\n",
"0 15624510 Male 19 19000 0\n",
"1 15810944 Male 35 20000 0\n",
"2 15668575 Female 26 43000 0\n",
"3 15603246 Female 27 57000 0\n",
"4 15804002 Male 19 76000 0\n",
"5 15728773 Male 27 58000 0\n",
"6 15598044 Female 27 84000 0\n",
"7 15694829 Female 32 150000 1\n",
"8 15600575 Male 25 33000 0\n",
"9 15727311 Female 35 65000 0\n",
"10 15570769 Female 26 80000 0\n",
"11 15606274 Female 26 52000 0\n",
"12 15746139 Male 20 86000 0\n",
"13 15704987 Male 32 18000 0\n",
"14 15628972 Male 18 82000 0\n",
"15 15697686 Male 29 80000 0\n",
"16 15733883 Male 47 25000 1\n",
"17 15617482 Male 45 26000 1\n",
"18 15704583 Male 46 28000 1\n",
"19 15621083 Female 48 29000 1\n",
"20 15649487 Male 45 22000 1\n",
"21 15736760 Female 47 49000 1\n",
"22 15714658 Male 48 41000 1\n",
"23 15599081 Female 45 22000 1\n",
"24 15705113 Male 46 23000 1\n",
"25 15631159 Male 47 20000 1\n",
"26 15792818 Male 49 28000 1\n",
"27 15633531 Female 47 30000 1\n",
"28 15744529 Male 29 43000 0\n",
"29 15669656 Male 31 18000 0\n",
".. ... ... ... ... ...\n",
"370 15611430 Female 60 46000 1\n",
"371 15774744 Male 60 83000 1\n",
"372 15629885 Female 39 73000 0\n",
"373 15708791 Male 59 130000 1\n",
"374 15793890 Female 37 80000 0\n",
"375 15646091 Female 46 32000 1\n",
"376 15596984 Female 46 74000 0\n",
"377 15800215 Female 42 53000 0\n",
"378 15577806 Male 41 87000 1\n",
"379 15749381 Female 58 23000 1\n",
"380 15683758 Male 42 64000 0\n",
"381 15670615 Male 48 33000 1\n",
"382 15715622 Female 44 139000 1\n",
"383 15707634 Male 49 28000 1\n",
"384 15806901 Female 57 33000 1\n",
"385 15775335 Male 56 60000 1\n",
"386 15724150 Female 49 39000 1\n",
"387 15627220 Male 39 71000 0\n",
"388 15672330 Male 47 34000 1\n",
"389 15668521 Female 48 35000 1\n",
"390 15807837 Male 48 33000 1\n",
"391 15592570 Male 47 23000 1\n",
"392 15748589 Female 45 45000 1\n",
"393 15635893 Male 60 42000 1\n",
"394 15757632 Female 39 59000 0\n",
"395 15691863 Female 46 41000 1\n",
"396 15706071 Male 51 23000 1\n",
"397 15654296 Female 50 20000 1\n",
"398 15755018 Male 36 33000 0\n",
"399 15594041 Female 49 36000 1\n",
"\n",
"[400 rows x 5 columns]\n"
]
}
],
"source": [
"dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
"print(dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"为了方便理解，这里我们只取Age年龄和EstimatedSalary估计工资作为特征"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"X = dataset.iloc[:, [2, 3]].values\n",
"y = dataset.iloc[:, 4].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第三步：将数据划分成训练集和测试集"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第四步：特征缩放"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, _DataConversionWarning)\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"##第五步：使用K-NN对训练集数据进行训练"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"从sklearn的neighbors类中导入KNeighborsClassifier学习器"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"设置好相关的参数\n",
"n_neighbors = 5(K值的选择，默认选择5)、\n",
"metric = 'minkowski'(距离度量的选择，这里选择的是闵氏距离(默认参数))、\n",
"p = 2 (距离度量metric的附属参数，只用于闵氏距离和带权重闵氏距离中p值的选择，p=1为曼哈顿距离， p=2为欧式距离。默认为2)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
"classifier.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第六步：对测试集进行预测"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0\n",
" 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1\n",
" 0 0 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1]\n"
]
}
],
"source": [
"y_pred = classifier.predict(X_test)\n",
"print(y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第七步：生成混淆矩阵"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"混淆矩阵可以对一个分类器性能进行分析，由此可以计算出许多指标，例如：ROC曲线、正确率等"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[64 4]\n",
" [ 3 29]]\n"
]
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix(y_test, y_pred)\n",
"print(cm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" 预测值\n",
" 0 1\n",
" 实0 64 4 \n",
" 际1 3 29\n",
" 值\n",
"\n",
"预测集中的0总共有68个，1总共有32个。\n",
"在这个混淆矩阵中，实际有68个0，但K-NN预测出有67(64+3)个0，其中有3个实际上是1。\n",
"同时K-NN预测出有33(4+29)个1，其中4个实际上是0。"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 11_K-NN.md
================================================
# K近邻法 (K-NN)
## 数据集 | 社交网络
## 导入相关库
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
## 导入数据集
```python
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
```
## 将数据划分成训练集和测试集
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
```
## 特征缩放
```python
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
```
## 使用K-NN对训练集数据进行训练
```python
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)
```
## 对测试集进行预测
```python
y_pred = classifier.predict(X_test)
```
## 生成混淆矩阵
```python
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
```
================================================
FILE: Code/Day 11_k-NN.py
================================================
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting K-NN to the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm)
print(classification_report(y_test, y_pred))
================================================
FILE: Code/Day 13_SVM.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 机器学习100天——第13天：支持向量机 (SVM)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第一步：导入库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第二步：导入数据"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
"X = dataset.iloc[:, [2, 3]].values\n",
"y = dataset.iloc[:, 4].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第三步：拆分数据集为训练集合和测试集合"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第四步：特征量化"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.fit_transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第五步：适配SVM到训练集合"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
" max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.svm import SVC\n",
"classifier = SVC(kernel = 'linear', random_state = 0)\n",
"classifier.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第六步：预测测试集合结果"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_pred = classifier.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第七步：创建混淆矩阵"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第八步：训练集合结果可视化"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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qZTO7lWMO2kLSD1GcOibV3991tpaJblQ8FYKIvKfaB1X1k9GLM4twM5dEYBoJ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\nt6x0hV8K2cx/vWyQbf+3O/D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diSgEEfk7EdktIg+JyLdFZEkSchiGkU5yG5cwOTnBruFd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\nAZhCMAzDMPL8f2I4Lp2zH4gCAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set, y_set = X_train, y_train\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('SVM (Training set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第九步：测试集合结果可视化"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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InwD+E/gpEGEGL0lVJb2U1KMpXBxTkEettpvBZIbFy3BKsDa1lsCsXpSEMMvd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\n1pPjhAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set, y_set = X_test, y_test\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('SVM (Test set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"完整的项目请前往Github项目100-Days-Of-ML-Code查看。有任何的建议或者意见欢迎在issue中提出~"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 13_SVM.md
================================================
# Day 13 | 支持向量机 (SVM)
## 导入库
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
## 导入数据
```python
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
```
## 拆分数据集为训练集合和测试集合
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
```
## 特征量化
```python
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)
```
## 适配SVM到训练集合
```python
from sklearn.svm import SVC
classifier = SVC(kernel = 'linear', random_state = 0)
classifier.fit(X_train, y_train)
```
## 预测测试集合结果
```python
y_pred = classifier.predict(X_test)
```
## 创建混淆矩阵
```python
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
```
## 训练集合结果可视化
```python
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('SVM (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```
## 测试集合结果可视化
```python
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('SVM (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```
================================================
FILE: Code/Day 13_SVM.py
================================================
#Day13: Support Vector Machine (SVM)
#Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#Importing the dataset
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
#Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
#Fitting SVM to the Training set
from sklearn.svm import SVC
classifier = SVC(kernel = 'linear', random_state = 0)
classifier.fit(X_train, y_train)
#Predicting the Test set results
y_pred = classifier.predict(X_test)
#Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm)
print(classification_report(y_test, y_pred))
#Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('SVM (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
#Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('SVM (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
================================================
FILE: Code/Day 1_Data_Preprocessing.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 机器学习100天——第1天：数据预处理（Data Preprocessing）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"搭建anaconda环境，参考 https://zhuanlan.zhihu.com/p/33358809\n",
"\n",
"## 第一步：导入需要的库\n",
"这两个是我们每次都需要导入的库。NumPy包含数学计算函数。Pandas用于导入和管理数据集。"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[ 7. 2. 3. ]\n [ 4. 3.5 6. ]\n [10. 3.5 9. ]]\nSklearn verion is 0.23.1\n"
]
}
],
"source": [
"import sklearn\n",
"from sklearn.impute import SimpleImputer\n",
"#This block is an example used to learn SimpleImputer\n",
"imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
"imp_mean.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])\n",
"X = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]\n",
"print(imp_mean.transform(X))\n",
"print(\"Sklearn verion is {}\".format(sklearn.__version__))"
]
},
{
"source": [
"from sklearn.preprocessing import OneHotEncoder\n",
"enc = OneHotEncoder(handle_unknown='ignore')\n",
"X = [['Male', 1], ['Female', 3], ['Female', 2]]\n",
">>> enc.fit(X)\n",
"OneHotEncoder(handle_unknown='ignore')\n",
">>> enc.categories_\n",
"[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]\n",
">>> enc.transform([['Female', 1], ['Male', 4]]).toarray()\n",
"array([[1., 0., 1., 0., 0.],\n",
" [0., 1., 0., 0., 0.]])\n",
">>> enc.inverse_transform([[0, 1, 1, 0, 0], [0, 0, 0, 1, 0]])\n",
"array([['Male', 1],\n",
" [None, 2]], dtype=object)\n",
">>> enc.get_feature_names(['gender', 'group'])\n",
"array(['gender_Female', 'gender_Male', 'group_1', 'group_2', 'group_3'],\n",
" dtype=object)"
],
"cell_type": "code",
"metadata": {},
"execution_count": 4,
"outputs": [
{
"output_type": "error",
"ename": "SyntaxError",
"evalue": "invalid syntax (, line 4)",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m >>> enc.fit(X)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第二步：导入数据集\n",
"数据集通常是.csv格式。CSV文件以文本形式保存表格数据。文件的每一行是一条数据记录。我们使用Pandas的read_csv方法读取本地csv文件为一个数据帧。然后，从数据帧中制作自变量和因变量的矩阵和向量。"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Step 2: Importing dataset\nX\n[['France' 44.0 72000.0]\n ['Spain' 27.0 48000.0]\n ['Germany' 30.0 54000.0]\n ['Spain' 38.0 61000.0]\n ['Germany' 40.0 nan]\n ['France' 35.0 58000.0]\n ['Spain' nan 52000.0]\n ['France' 48.0 79000.0]\n ['Germany' 50.0 83000.0]\n ['France' 37.0 67000.0]]\nY\n['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']\n[[44.0 72000.0]\n [27.0 48000.0]\n [30.0 54000.0]\n [38.0 61000.0]\n [40.0 nan]\n [35.0 58000.0]\n [nan 52000.0]\n [48.0 79000.0]\n [50.0 83000.0]\n [37.0 67000.0]]\n"
]
}
],
"source": [
"dataset = pd.read_csv('../datasets/Data.csv')\n",
"# 不包括最后一列的所有列\n",
"X = dataset.iloc[ : , :-1].values\n",
"#取最后一列\n",
"Y = dataset.iloc[ : , 3].values\n",
"print(\"Step 2: Importing dataset\")\n",
"print(\"X\")\n",
"print(X)\n",
"print(\"Y\")\n",
"print(Y)\n",
"print(X[ : , 1:3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第三步：处理丢失数据\n",
"我们得到的数据很少是完整的。数据可能因为各种原因丢失，为了不降低机器学习模型的性能，需要处理数据。我们可以用整列的平均值或中间值替换丢失的数据。我们用sklearn.preprocessing库中的Imputer类完成这项任务。"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"---------------------\nStep 3: Handling the missing data\nstep2\nX\n[['France' 44.0 72000.0]\n ['Spain' 27.0 48000.0]\n ['Germany' 30.0 54000.0]\n ['Spain' 38.0 61000.0]\n ['Germany' 40.0 63777.77777777778]\n ['France' 35.0 58000.0]\n ['Spain' 38.77777777777778 52000.0]\n ['France' 48.0 79000.0]\n ['Germany' 50.0 83000.0]\n ['France' 37.0 67000.0]]\n"
]
}
],
"source": [
"# If you use the newest version of sklearn, use the lines of code commented out\n",
"from sklearn.impute import SimpleImputer\n",
"imputer = SimpleImputer(missing_values=np.nan, strategy=\"mean\")\n",
"#from sklearn.preprocessing import Imputer\n",
"# axis=0表示按列进行\n",
"#imputer = Imputer(missing_values = \"NaN\", strategy = \"mean\", axis = 0)\n",
"#print(imputer)\n",
"#\n",
"# print(X[ : , 1:3])\n",
"imputer = imputer.fit(X[ : , 1:3]) #put the data we want to process in to this imputer\n",
"X[ : , 1:3] = imputer.transform(X[ : , 1:3]) #replace the np.nan with mean\n",
"#print(X[ : , 1:3])\n",
"print(\"---------------------\")\n",
"print(\"Step 3: Handling the missing data\")\n",
"print(\"step2\")\n",
"print(\"X\")\n",
"print(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第四步：解析分类数据\n",
"分类数据指的是含有标签值而不是数字值的变量。取值范围通常是固定的。例如\"Yes\"和\"No\"不能用于模型的数学计算，所以需要解析成数字。为实现这一功能，我们从sklearn.preprocessing库导入LabelEncoder类。"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"---------------------\nStep 4: Encoding categorical data\nX\n[[1.0 0.0 0.0 44.0 72000.0]\n [0.0 0.0 1.0 27.0 48000.0]\n [0.0 1.0 0.0 30.0 54000.0]\n [0.0 0.0 1.0 38.0 61000.0]\n [0.0 1.0 0.0 40.0 63777.77777777778]\n [1.0 0.0 0.0 35.0 58000.0]\n [0.0 0.0 1.0 38.77777777777778 52000.0]\n [1.0 0.0 0.0 48.0 79000.0]\n [0.0 1.0 0.0 50.0 83000.0]\n [1.0 0.0 0.0 37.0 67000.0]]\nY\n[0 1 0 0 1 1 0 1 0 1]\n"
]
}
],
"source": [
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
"from sklearn.compose import ColumnTransformer \n",
"#labelencoder_X = LabelEncoder()\n",
"#X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])\n",
"#Creating a dummy variable\n",
"#print(X)\n",
"ct = ColumnTransformer([(\"\", OneHotEncoder(), [0])], remainder = 'passthrough')\n",
"X = ct.fit_transform(X)\n",
"#onehotencoder = OneHotEncoder(categorical_features = [0])\n",
"#X = onehotencoder.fit_transform(X).toarray()\n",
"labelencoder_Y = LabelEncoder()\n",
"Y = labelencoder_Y.fit_transform(Y)\n",
"print(\"---------------------\")\n",
"print(\"Step 4: Encoding categorical data\")\n",
"print(\"X\")\n",
"print(X)\n",
"print(\"Y\")\n",
"print(Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第五步：拆分数据集为测试集合和训练集合\n",
"把数据集拆分成两个：一个是用来训练模型的训练集合，另一个是用来验证模型的测试集合。两者比例一般是80:20。我们导入sklearn.model_selection库中的train_test_split()方法。"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"---------------------\nStep 5: Splitting the datasets into training sets and Test sets\nX_train\n[[0.0 1.0 0.0 40.0 63777.77777777778]\n [1.0 0.0 0.0 37.0 67000.0]\n [0.0 0.0 1.0 27.0 48000.0]\n [0.0 0.0 1.0 38.77777777777778 52000.0]\n [1.0 0.0 0.0 48.0 79000.0]\n [0.0 0.0 1.0 38.0 61000.0]\n [1.0 0.0 0.0 44.0 72000.0]\n [1.0 0.0 0.0 35.0 58000.0]]\nX_test\n[[0.0 1.0 0.0 30.0 54000.0]\n [0.0 1.0 0.0 50.0 83000.0]]\nY_train\n[1 1 1 0 1 0 0 1]\nY_test\n[0 0]\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)\n",
"print(\"---------------------\")\n",
"print(\"Step 5: Splitting the datasets into training sets and Test sets\")\n",
"print(\"X_train\")\n",
"print(X_train)\n",
"print(\"X_test\")\n",
"print(X_test)\n",
"print(\"Y_train\")\n",
"print(Y_train)\n",
"print(\"Y_test\")\n",
"print(Y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第六步：特征量化\n",
"大部分模型算法使用两点间的欧氏距离表示，但此特征在幅度、单位和范围姿态问题上变化很大。在距离计算中，高幅度的特征比低幅度特征权重更大。可用特征标准化或Z值归一化解决。导入sklearn.preprocessing库的StandardScalar类。"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"---------------------\nStep 6: Feature Scaling\nX_train\n[[-1. 2.64575131 -0.77459667 0.26306757 0.12381479]\n [ 1. -0.37796447 -0.77459667 -0.25350148 0.46175632]\n [-1. -0.37796447 1.29099445 -1.97539832 -1.53093341]\n [-1. -0.37796447 1.29099445 0.05261351 -1.11141978]\n [ 1. -0.37796447 -0.77459667 1.64058505 1.7202972 ]\n [-1. -0.37796447 1.29099445 -0.0813118 -0.16751412]\n [ 1. -0.37796447 -0.77459667 0.95182631 0.98614835]\n [ 1. -0.37796447 -0.77459667 -0.59788085 -0.48214934]]\nX_test\n[[-1. 2.64575131 -0.77459667 -1.45882927 -0.90166297]\n [-1. 2.64575131 -0.77459667 1.98496442 2.13981082]]\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc_X = StandardScaler()\n",
"X_train = sc_X.fit_transform(X_train)\n",
"X_test = sc_X.transform(X_test) #we should not use fit_transfer cause the u and z is determined from x_train\n",
"print(\"---------------------\")\n",
"print(\"Step 6: Feature Scaling\")\n",
"print(\"X_train\")\n",
"print(X_train)\n",
"print(\"X_test\")\n",
"print(X_test)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"完整的项目请前往Github项目100-Days-Of-ML-Code查看。有任何的建议或者意见欢迎在issue中提出~"
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3.8.3 64-bit (conda)",
"metadata": {
"interpreter": {
"hash": "1b78ff499ec469310b6a6795c4effbbfc85eb20a6ba0cf828a15721670711b2c"
}
}
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3-final"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 1_Data_Preprocessing.md
================================================
# 数据预处理
如图所示，通过6步完成数据预处理。
此例用到的[数据](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/datasets/Data.csv)，[代码](https://github.com/MLEveryday/100-Days-Of-ML-Code/blob/master/Code/Day%201_Data_Preprocessing.py)。
## 第1步：导入库
```Python
import numpy as np
import pandas as pd
```
## 第2步：导入数据集
```python
//随后一列是label
dataset = pd.read_csv('Data.csv')//读取csv文件
X = dataset.iloc[ : , :-1].values//.iloc[行，列]
Y = dataset.iloc[ : , 3].values // : 全部行 or 列；[a]第a行 or 列
// [a,b,c]第 a,b,c 行 or 列
```
## 第3步：处理丢失数据
```python
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
imputer = imputer.fit(X[ : , 1:3])
X[ : , 1:3] = imputer.transform(X[ : , 1:3])
```
## 第4步：解析分类数据
```python
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
```
### 创建虚拟变量
```python
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
```
## 第5步：拆分数据集为训练集合和测试集合
```python
#from sklearn.model_selection import train_test_split
from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)
```
## 第6步：特征量化
```python
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
```
================================================
FILE: Code/Day 1_Data_Preprocessing.py
================================================
#Day 1: Data Prepocessing
#Step 1: Importing the libraries
import numpy as np
import pandas as pd
#Step 2: Importing dataset
dataset = pd.read_csv('../datasets/Data.csv')
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 3].values
print("Step 2: Importing dataset")
print("X")
print(X)
print("Y")
print(Y)
#Step 3: Handling the missing data
# If you use the newest version of sklearn, use the lines of code commented out
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
#from sklearn.preprocessing import Imputer
# axis=0表示按列进行
#imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
imputer = imputer.fit(X[ : , 1:3])
X[ : , 1:3] = imputer.transform(X[ : , 1:3])
print("---------------------")
print("Step 3: Handling the missing data")
print("step2")
print("X")
print(X)
#Step 4: Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
#labelencoder_X = LabelEncoder()
#X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
#Creating a dummy variable
#print(X)
ct = ColumnTransformer([("", OneHotEncoder(), [0])], remainder = 'passthrough')
X = ct.fit_transform(X)
#onehotencoder = OneHotEncoder(categorical_features = [0])
#X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
print("---------------------")
print("Step 4: Encoding categorical data")
print("X")
print(X)
print("Y")
print(Y)
#Step 5: Splitting the datasets into training sets and Test sets
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)
print("---------------------")
print("Step 5: Splitting the datasets into training sets and Test sets")
print("X_train")
print(X_train)
print("X_test")
print(X_test)
print("Y_train")
print(Y_train)
print("Y_test")
print(Y_test)
#Step 6: Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
print("---------------------")
print("Step 6: Feature Scaling")
print("X_train")
print(X_train)
print("X_test")
print(X_test)
================================================
FILE: Code/Day 25_Decision_Tree.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 机器学习100天——第25天：决策树（Decision Tree）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第1步：导入需要用到的python库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第2步：导入数据集"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
"X = dataset.iloc[:, [2, 3]].values\n",
"y = dataset.iloc[:, 4].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第3步：将数据集拆分为训练集和测试集"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第4步：特征缩放"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ymao/usr/miniconda/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第5步：对测试集进行决策树分类拟合"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
" splitter='best')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
"classifier.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第6步：预测测试集的结果"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_pred = classifier.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第7步：制作混淆矩阵"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第8步：将训练集结果进行可视化"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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GYcTIlfOvZELzhJK2Cc0TuHJ+tPLXAFctuYpFr17Epic2seCFC/jO7d+J3GcxFlQ2DMOI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\nnsJsAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set, y_set = X_train, y_train\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('Decision Tree Classification (Training set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第9步：将测试集结果进行可视化"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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t5fCI40aZsysogURtF6m3OL2M/tnMzgOeBLqBf3T3O1JPWTOIqq6pdYqGtHrj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\nifLpAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set, y_set = X_test, y_test\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('Decision Tree Classification (Test set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"完整的项目请前往Github项目100-Days-Of-ML-Code查看。有任何的建议或者意见欢迎在issue中提出~"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 25_Decision_Tree.md
================================================
# 决策树分类
### 导入需要用到的python库
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
### 导入数据集
```python
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
```
### 将数据集拆分为训练集和测试集
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
```
### 特征缩放
```python
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
```
### 对测试集进行决策树分类拟合
```python
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
```
### 预测测试集的结果
```python
y_pred = classifier.predict(X_test)
```
### 制作混淆矩阵
```python
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
```
### 将训练集结果进行可视化
```python
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Decision Tree Classification (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```
### 将测试集结果进行可视化
```python
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Decision Tree Classification (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```
================================================
FILE: Code/Day 25_Decision_Tree.py
================================================
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting Decision Tree Classification to the Training set
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Decision Tree Classification (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Decision Tree Classification (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
================================================
FILE: Code/Day 2_Simple_Linear_Regression.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 机器学习100天——第二天：简单线性回归\n",
"## 第一步：数据预处理"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这里导入我们需要的库，值得注意的是，这里比第一天多了一个matplotlib.pyplot,matplotlib是python上的一个2D绘图库,\n",
"matplotlib下的模块pyplot是一个有命令样式的函数集合，\n",
"matplotlib.pyplot是为我们对结果进行图像化作准备的。"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"导入相关数据"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Hours Scores\n0 2.5 21\n1 5.1 47\n2 3.2 27\n3 8.5 75\n4 3.5 30\n5 1.5 20\n6 9.2 88\n7 5.5 60\n8 8.3 81\n9 2.7 25\n10 7.7 85\n11 5.9 62\n12 4.5 41\n13 3.3 42\n14 1.1 17\n15 8.9 95\n16 2.5 30\n17 1.9 24\n18 6.1 67\n19 7.4 69\n20 2.7 30\n21 4.8 54\n22 3.8 35\n23 6.9 76\n24 7.8 86\n25 2.1 93\n26 2.2 93\n27 2.5 93\n Hours Scores\n15 8.9 95\n27 2.5 93\n26 2.2 93\n25 2.1 93\n6 9.2 88\n24 7.8 86\n10 7.7 85\n8 8.3 81\n23 6.9 76\n3 8.5 75\n19 7.4 69\n18 6.1 67\n11 5.9 62\n7 5.5 60\n21 4.8 54\n1 5.1 47\n13 3.3 42\n12 4.5 41\n22 3.8 35\n20 2.7 30\n4 3.5 30\n16 2.5 30\n2 3.2 27\n9 2.7 25\n17 1.9 24\n0 2.5 21\n5 1.5 20\n14 1.1 17\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Hours Scores\n",
"0 2.5 21\n",
"1 5.1 47\n",
"2 3.2 27\n",
"3 8.5 75\n",
"4 3.5 30\n",
"5 1.5 20\n",
"6 9.2 88\n",
"7 5.5 60\n",
"8 8.3 81\n",
"9 2.7 25\n",
"10 7.7 85\n",
"11 5.9 62\n",
"12 4.5 41\n",
"13 3.3 42\n",
"14 1.1 17\n",
"15 8.9 95\n",
"16 2.5 30\n",
"17 1.9 24\n",
"18 6.1 67\n",
"19 7.4 69\n",
"20 2.7 30\n",
"21 4.8 54\n",
"22 3.8 35\n",
"23 6.9 76\n",
"24 7.8 86\n",
"25 2.1 93\n",
"26 2.2 93\n",
"27 2.5 93"
],
"text/html": "\n\n
\n \n \n | \n Hours | \n Scores | \n
\n \n \n \n | 0 | \n 2.5 | \n 21 | \n
\n \n | 1 | \n 5.1 | \n 47 | \n
\n \n | 2 | \n 3.2 | \n 27 | \n
\n \n | 3 | \n 8.5 | \n 75 | \n
\n \n | 4 | \n 3.5 | \n 30 | \n
\n \n | 5 | \n 1.5 | \n 20 | \n
\n \n | 6 | \n 9.2 | \n 88 | \n
\n \n | 7 | \n 5.5 | \n 60 | \n
\n \n | 8 | \n 8.3 | \n 81 | \n
\n \n | 9 | \n 2.7 | \n 25 | \n
\n \n | 10 | \n 7.7 | \n 85 | \n
\n \n | 11 | \n 5.9 | \n 62 | \n
\n \n | 12 | \n 4.5 | \n 41 | \n
\n \n | 13 | \n 3.3 | \n 42 | \n
\n \n | 14 | \n 1.1 | \n 17 | \n
\n \n | 15 | \n 8.9 | \n 95 | \n
\n \n | 16 | \n 2.5 | \n 30 | \n
\n \n | 17 | \n 1.9 | \n 24 | \n
\n \n | 18 | \n 6.1 | \n 67 | \n
\n \n | 19 | \n 7.4 | \n 69 | \n
\n \n | 20 | \n 2.7 | \n 30 | \n
\n \n | 21 | \n 4.8 | \n 54 | \n
\n \n | 22 | \n 3.8 | \n 35 | \n
\n \n | 23 | \n 6.9 | \n 76 | \n
\n \n | 24 | \n 7.8 | \n 86 | \n
\n \n | 25 | \n 2.1 | \n 93 | \n
\n \n | 26 | \n 2.2 | \n 93 | \n
\n \n | 27 | \n 2.5 | \n 93 | \n
\n \n
\n
",
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"#散点图\n",
"plt.scatter(X_train , Y_train, color = 'red')\n",
"#线图\n",
"plt.plot(X_train , regressor.predict(X_train), 'bo-')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 测试集结果可视化"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "",
"image/svg+xml": "\r\n\r\n\r\n\r\n",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"#散点图\n",
"plt.scatter(X_test , Y_test, color = 'red')\n",
"#线图\n",
"plt.plot(X_test ,Y_pred, 'bo-')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[3.2]\n [3.8]\n [1.1]\n [1.9]\n [1.5]\n [5.9]\n [7.8]] [[27]\n [35]\n [17]\n [24]\n [20]\n [62]\n [86]]\n"
]
}
],
"source": [
"print(X_test,Y_test)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3-final"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 2_Simple_Linear_Regression.md
================================================
# 简单线性回归模型
# 第一步：数据预处理
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dataset = pd.read_csv('studentscores.csv')
X = dataset.iloc[ : , : 1 ].values
Y = dataset.iloc[ : , 1 ].values
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0)
```
# 第二步：训练集使用简单线性回归模型来训练
```python
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor = regressor.fit(X_train, Y_train)
```
# 第三步：预测结果
```python
Y_pred = regressor.predict(X_test)
```
# 第四步：可视化
## 训练集结果可视化
```python
plt.scatter(X_train , Y_train, color = 'red')
plt.plot(X_train , regressor.predict(X_train), color ='blue')
plt.show()
```
## 测试集结果可视化
```python
plt.scatter(X_test , Y_test, color = 'red')
plt.plot(X_test , regressor.predict(X_test), color ='blue')
plt.show()
```
================================================
FILE: Code/Day 2_Simple_Linear_Regression.py
================================================
# Data Preprocessing
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dataset = pd.read_csv('../datasets/studentscores.csv')
X = dataset.iloc[ : , : 1 ].values
Y = dataset.iloc[ : , 1 ].values
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0)
# Fitting Simple Linear Regression Model to the training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor = regressor.fit(X_train, Y_train)
# Predecting the Result
Y_pred = regressor.predict(X_test)
# Visualising the Training results
plt.scatter(X_train , Y_train, color = 'red')
plt.plot(X_train , regressor.predict(X_train), color ='blue')
# Visualizing the test results
plt.scatter(X_test , Y_test, color = 'red')
plt.plot(X_test , regressor.predict(X_test), color ='blue')
plt.show()
================================================
FILE: Code/Day 34_Random_Forests.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"导入库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"导入数据集"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"dataset = pd.read_csv('Social_Network_Ads.csv')\n",
"X = dataset.iloc[:, [2, 3]].values\n",
"y = dataset.iloc[:, 4].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"将数据集拆分成训练集和测试集"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"特征缩放"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\softwareinstallation\\anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"调试训练集的随机森林"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
" oob_score=False, random_state=0, verbose=0, warm_start=False)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
"classifier.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"预测测试集结果"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"y_pred = classifier.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"生成混淆矩阵，也称作误差矩阵"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"将训练集结果可视化"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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YQTAMw0iQnb3uMtdB7XnCDIJhGEaCTJ/gLnMd1J4nzCAYhmEkyNULrmZc67iKtnGt47h6Qbzy1wDXLLmGq152FVsf2srCZy3khzf+MPY1y7GgsmEYRoKUAsfXrb2Onb07mT5hOlcvuDqRgPIXl38x9jXqYQbBMIxU6O7tZsuTWyj2F2lraaNjSgdTJ0zNWqyGcOncS3OfUeTCDILR1IwEpTMS+lBNsa/Igz0PMqAD3ut+7zXQ9H0byZhBMBpG0oqvu7e76ZXOSOiDi8N9h1G0om1AB9jy5Jam7NcAA6gqIpK1KHVRVQYYGPb7zSAYDSENxbflyS3HrleintLJ40g8ah+ahWpjUKLYX2ywJMmw89BO2ve30zaxLbdGQVUp7i+y89Dw01vNIBgNIQ3FF6RcXO15HYlH6UNeWXFyN0s7trC9rcisYhv9AoI4jUJbS1sGEsbn5u03cyVXMn38dAo5Tc4cYICdh3Zy8/abh30NMwhGQ0hD8bW1tDnf71I6eR2JR+lDHllxcjdLOh/kYIv3bLeNK4LCGGmln/6KZ16QAh1TOrISNRa9/b1cv/X6rMVInXyaOmPEEaTg4ii+jikdFKTyKxykdKIapO7ebtY+spauh7tY+8haunu7hy1nPaL0IY8s7dhyzBgcQ6BP++hs7zz2+ba1tNHZ3tnUbrDRgM0QjIbQMaWjwmUD8RVfSbmEiQtEGYk30r0UpQ95ZHub26AqytQJU2v6kXUcJ+v75x0zCEZDSEvxuZSOiygGqdHupbB9yCOzim2em6gKoTbwmnUcJ+v7NwNmEIyGkaXii2KQkoh3jJaR6LItHRUxBAAUxo0ZV3Nu1nGcrO/fDJhBMEYNYQ1S3EBv041Eu7thyxYoFqGtDTo6YGo4ORfv9s4rzzLaPrZIW2vts8o6oyrr+zcDZhAMo4q48Y68j0RXb+069vdV64EHH4QBX95i0XsNkYxCyTAAjDm/y3le1hlVWd+/GcjMIIjITOC7wDRgAFiuql/OSh7DKBE33pHmSDQpV9TAslY47zxYuxYGquQaGPBmDCENQljSSCxopvs3A1nOEPqAD6jqH0RkIvB7EblTVf+SoUyGAcSLd6Q1Ek3SFVVY2gd00dcFN50FSy+E7ZNg1j5YtgoWr0/HeHW2d2YWW2n2jK5GkJlBUNWdwE7/7/0isgE4BTCD0ESM1OBpnH6lNRJNyhW1cM6iY39/5fldLL0IDo71Xm+bDEsuhcfHwzULu0Jdb2D1opq2oOJ2ne2dLJi5ILSsSdPMGV2NIBcxBBGZDZwN3OM4tgRYAtDWbr6+PNF0wdOQxO1XWiPRNFxRn3pJKwdb+yraDo712hfOOW/I95fHI8oZacXtRguZGwQRmQDcArxfVZ+qPq6qy4HlABPnTHRXzDIyIYkRa5SReKNmI0n0K42RaBquqH1VxmCo9rCMtOJ2o4VMDYKIjMEzBitU9UdZymJEJ+6INcpIvJGzkaj92rRnEzt6dxx7PWPCDOaeODdRmSAdV1QUI7Nm+xr3RarSVq+cDCvmj6zidqOFLLOMBLgB2KCq6e4LZ6RC3BFrlJF4I1M5o/Sr2hgAx17HMQoX/qabt9+yhZN7iuxub+P6yztYdW7yrqj28e018pfay1m9tYuWAZhwpPK8D/yGmrTVb9wGj5zUyt2nJl/cbqTGrPJCljOEFwFvANaLyDq/7WOqenvQG3qLvYE+S6PxKFAoFIb9o48yEm/koqIoI3GXMi21D9cgXPibbj74nQcZd8S7/7SeIh/8jjcbWnVusq6onkM9zvYd+3ewc39l345+zk9VLed3tWmrxx+F7/6gj0s+e4ZtiNRkZJlltAYcBU/q8Nz9E7hv9TkpSWREpbCwK1YaYZSReCMXFWWdnvj6/9rAuKqR+LgjA7zx+xuPzRLKqR41F/uKgT+shXMWVQyqFAJ/hTXZQ64Yc9FtkE/d6y5uF4e8L/gbCWQeVDaamzg/+igj8UYvKsoyPXHWPnf7qXtrffKuUTN4ij7uvl6FOmmnx4xFW5vTKDw6Ofldxaz0RPqYQTAyI8pIPOtRexAzJsxwuo1mTJgx7GtunwT/M6t2sdj5j9QqWdeoGfFmTkH5/uXrENZsX0PfQG1GUWtLK+fNcqedVrhtOzoqYwjAgTHwmZfWFreLi5WeSB8zCEamRBmJ53FRUSlOkGSW0esuh3XT4FDZYrF3XAp/c2B6zblxR80uY1CvvUT57GHg+/Mqsoze9bIit57dxvxQEoTHSk+kjxkEw4jJ3BPnJppm+oc5tSPhQ2Nh5fgeqsf8cUfNw3l/+Qxj9dYur+ZRWd2jm8/qYkKou0cjr7PEkYQZBMPIGVFG/XFHzc026s7jLHEkYQbBGNXkMa89yqg97qjZRt1GOWYQjFFLd283G/dsPLaitthfZOOejUC2ee1RR+1xR815HXXn0ViPdMwgGKOWzU9srimvoCibn9icqeIZCaP2fYf2OheRlscf6mGL0LLBDIKROM0yshtuhk0jyOuoPQxH717kbK+3rqEaW4SWDWYQjEQZjSO7ZjGAzYQtQsuGQtYCGCOLeiO7vNEiLZHaXZQMYElRlQxgd293IjKOVoLSXm0RWrqYQTASJe39hNc+spauh7tY+8ja2Ep3brt77UBQu4tmMoDNRMeUDgpSqZ7ynA47UjCXkZEozbCfcIkkgrcjwbWRR5fXSAisNyNDGgQReQ/eBjZPNkAeI89UbYRy1QlwE10Vp7iqZ0Yd2bkUVF6DjM1eXyfPMZ9mDqw3K2FmCNOAe0XkD8C3gJ+rqm1lOdro7q7ZCGXFTwqs2NBZUbZgzPldjGkbz4AODGtkF6Sgagq4+cQZiSehDPO60jfsqD+vhtbIhiENgqp+XESuBV4CvAX4moh8H7hBVf+atoBGTtiypaKiJeC93rKlwiAAtLW2MX9auNJm1TuDnfHufgZaaxVUEFECwNUktXdy6Vp5cW1EMXQjweVlJEeoGIKqqojsAnYBfcAU4IcicqeqfihNAY2cELARSmB7CFw7gz0VUb97O7EOj6SUYd5cG1EMXbO7vIxkCRNDeC/wJmAPcD3wD6p6VEQKwGbADMJoIGAjFNpqFUfQKtVqvv1f1OwMNmufV+45LHEWkY1UZdjI4nh1ccScdsyJf1kjPcLMENqB16jqtvJGVR0QkVekI5aROxwboVAoeO1lBK1SdbKvq6Zp2SpYcikcHFt2GylQkIJT+U/qa+WmD6x1bkY/FFGVYR6zcVzUM3SuPsTZBjUQR8xp+Uq47sTu0J+P0XjqrkPwZwGXVxuDEqq6IRWpjPwxdSp0dg7OCNravNdTY/y4HbOLxeth+R0tnHa4DdRTYp3tnZx+wuk1eektA/DFlX1M6ylSwHM5Xf3tDZy6cnW4Lk2YSmd757EZQeleLmXYTAvQgnL428e3O/sAsGDmAhbNXsSCmQuSMXKOmNPxR+Htt9j6jDxTd4bgzwL+JCKzVHV7o4QyckrVRihhCaphc9UJsHylpyhKHBgDt8/pZ3tbPwI120CWj2Q/f3uRt/6p8prHH4XP/lJ52YvDjebD+v+bKRsnKNDd0D4ExJZO7rFgdZ4J4zKaDjwgIr8DDpQaVfWVqUlljAzWrYPeXljornK5Y47nQijPMrr+8g52nDuVhY7LVSvv9/6uy3nbu2cROZ103a519B7pDexKX3+fc9f6Yl8+FZzL0G3Y457Qp5JRFBBz2t3e3PGZkU4Yg/Dp1KUwRiSF9+0d8pxV504dtk95d3sb0xwjzo9eXJuqOtRIeN+hvUw6HHyv44/CY0+rbT9lfySRM6WhQXRHzOnAGLj+cis9kWfCrEMI55A1DAdh698Ph+sv76hIWwU4PLbAoxOHt4jtyXsWBR5b0dNVE+w+7gh8/k644VmRxM6Mhi6iK7kWy7KMlvxNkR0WUM41YdJOXwh8FZgHjAVagAOq6hgvGUbjKM0sql1Oba1bnMr/uCPw7fd1MWsfbJ8ESy+Em84Kd6/Fm9pgZZGlF3rvnbXPy4h60Xa4IWY/GpW91PBFdFUxp5vO6nK6AvOQvZUHGfJAGJfR14ArgR8A5wBvBE5PUyhj9BD3h+hyOXX0UjMSbhnwAtiz93mvZ+/DWXojkI4OFj/wIIvXV84+Fr8mXr8aXUsob4vo8lBLKQ8y5IWwK5UfEpEWVe0Hvi0iv0lZLmMUkNYP0TUS/vztRRavrzoxoPSG+6K1LhAKBW466xBnVCn/9vHt7DqwK1S/mil7KQ3y0P88yJAXwhiEgyIyFlgnIv8E7ASOT1csYzSQ5g8xbEbSQLFIa4StHQfxXFJKbUbTjt4dtfcJ6NdoryWUh/7nQYa8EMYgvAEvbvAe4GpgJnB5EjcXkW8BrwB2q+qZSVzTaB4a+UMMykja3d7GwjkLHO8Ix9pH1oaWNyjDJ+nMn3plQ9IM8oeh2pXWWmh1rkAP6n8avv6RWsJkOAy5Y5qqblPVQ6r6lKp+WlWvUdWHErr/d4BLErqW0WQ0cpvE6y/v4PDYyq/74bGF2GmQUYyXq19p7Qw2sHpR5b8vRygQlRKl2VT5SumgWlTt49tr2tJaLW67sw0SOEMQkfX4+524UNXYyXaq+msRmR33OkZz0sg0yKCMpLh1dYJGl9UE9SuP5bPTpF4p83J6DvXUtKXlYpw6YSr7Du+rcPVNO37aiP0M6lHPZZSLwnUisgRYAjDLUfvGaF4arQzjLIILIsioTTt+Gj2HekL1K2+ZP3nAZWSjuhijZHrtOrCrom3XgV1MGjdp1H0ugQYhqKBdo1HV5cBygHMmTrSd2kYYUZRhHnPFR9sIv1G43GtRfP1RMtgsy2gQW5hmNAV5zhW3EX54ClKoUb6CoGXe6SD3WhQXYxQlb1lGgwwZVMZbmHYV3mY444G34xkIw2gY9X7gRnMgUFNufN6J8zjjxDNClSCPUq48ipJvZHJD3sl0YZqI3AQsAk4UkUeBT6pq3EoAxgjERnEjg6DZVJQ9rMOcG8W91NAaTzkn04VpqnpVEtcxRj5J5IrnMQZhpEMUJW9xoEHCLkwrkMLCNMMIS9xRXJoxiFwamqr9jCkEe4dzKX9Moip5iwN5hCl/vQ1ARPqB/wYeU9XdaQtmGOXEHcWllUmSx2D3Veup2c+41F69yX0e5Teyo97CtG8AX1XVB0RkErAW6AdOEJEPqupNjRLSMCDeKC6tGEQeUxaXraJmP+NS+1uq9jnMo/xJYIZueNSbIZyvqu/y/34LsElVXy0i04A7ADMIRtMQNQYR1o2Sx2D3rH3h2/MofxKMVEOXNvXSTo+U/X0x8BMAVd3lPt0w8kuUejVRaubkMWVx+6Tw7XmUPwlGqqFLm3oGYa+IvEJEzgZeBPwMQERa8dYjGEbTMHXCVKYdP62iLaheTZQ1D3ksjLb0QpxB5KUX1p6bR/mTYKQaurSp5zJ6J/AVYBrw/rKZwYXAbWkLZmRIdYZKR0fwJjJRzs2QKPVqoowuk0hZTDrLx9sWdIBlq6jZLrR6C8uRmnJpawuGR71aRptwlKZW1Z8DP09TKCNDurtrM1Qe9IJxNYo+6Nx9+6Cnh74u2N2+tm5V0Qt/011TgRSSr0oaxaccNd4QJ9idRvBz4ZxF7JhTG0B27Wdcuk9Q0bdmNRQj1dCljag2T724cyZO1PvOOSdrMUY2a9ceS1OsoK0NFiwId24VB8bAkktrN7S/ar23z/HxRwfbii2gCuMGHO9/llReIMJ3V8GrneA4UN3sPNe/Vak5qY1mgjbYaWtpY8HM4W/cE1eZd/d2s2HPhpr2GRNmVFRxLfYV0dWLKs6Z8oIu9o1zXzfrDXpGK11v7vq9qg6pPEOVrjBGEUEK3tUewhiAp/BX3NbGiiccBuVo5TXa+t3v/5efw46qcop33dgK550XSobZz1vDtuNrN2M57WArD99bdY01a3jGO/vYMsUzUG39cMNKYfEUb4xdWNhVsStZWCXnUtJpBD+TmHVs6tnkbC/fM6Ak44qTu1m8e/C6vWNh0vjJzJ82f1jyG9lhBiFL8uh/b22FPscuVq2ttfK2tEC/Q4O7iGFQAKYdgLsernJ6hLMFACz7pbLkZXBw7GDbcUe8dqqzb847j788UNU2ZfDPgdKIeN06Cu/bG+r+QUo66hZlr4OeAAAgAElEQVSSYUgi5bJfQ36uAks7tlQYBKN5qbcw7Zp6b1TVLyYvzigiiq++kQS5Yfr7a+UVlw8mANfmRm1t4Y1CzM2RFv+xH/q84Or2SV6wddkqWLy+3yuvmDJBSlqQmpLQcYOfjU653N5mqZwjhXozhIn+/53A8/DKVgBcCvw6TaFGBVu21K4mHRjw2rM0CEEjftVaY6HqzRxaWgZnDePHw17HqLm9do9cOjoqjQx4Rqb6PoWCd24c2tpYvL7I4vW17XGpt6l9iaAYRv9A7fMe0AE2Pr6BjY/X+vBD47hXlFlH0MzFxaxiW6TZkpFf6mUZfRpARH4BPEdV9/uvPwX8oCHSjWSi+OobSZRRO3jupXI//po17vN274ZJk2pdZJ2dtW2QvCvNZXziGpr58xlYHe7U2S9cy7Zxtc/1tGIbD/92+MFjF7KwK/as4/QTTmfjno0VG9dUb2QDgMKyLR14RZAtaNzshIkhzKJy1fIRYHYq0owmghRv1vtGBylOR20cJ674Q6nd5SLr7KzNXoLkZ0ml62UUs1m2pYMlnQ9ysGXwOR7XX/CVabIIMDBQ657a+PiGUDGENdvX0N/fV636UVHmnTivIjBe7Cv68YOdiclvZEcYg/CfwO9E5Md4M9/LgO+mKtVoIOqIddMm2DGY4cGMGTB3bvj7hQ1gBynODTHcFyWydpFNnZqZO64UdF3asYXtbUVmFdtYtqUjlWDswOpFzvLXhQ8d8txbIiycHbQqwWPSYXjynkXHXr949mpWn6bH3FgCHOkrOjN569HMaxtGA2HKXy8TkTuA8/2mt6jqH9MVaxQQZcRabQxg8HUYoxA1gO1SnCU5q6me0UTJPCrJMkpYvHtqY7JxXJ93ocDAZwRaWigs7WPN9jWcNyt8mtZdDy+Eh2OKZRVIc0/YtNPjgKdU9dsicpKIzFHVrWkKNioIO2KtNgbl7WEMQtQAtms20d7ulqM6WDx3rns2US+dtbTALS+pt+B+BhDP5RQ0S0s6/Tjo8/YXF0463EVvInseRhTLKpDmniENgoh8EjgHL9vo28AY4Ea8gndGMxAlgN3dDRs3Dmb6FIve6yB27640SlOneqUrqt1bkya5M4r6+gYNRdqpt2EVr2uEXf5MqmWFoa/b3V1pKItF7/W+fbBrV7z04+p+FYusOMuVYpvtbMwqkOafMDOEy4CzgT8AqOoOEZlY/y1GrogSwN682Z1eGkT1qL+721Nw5eza5RmE6oyivr5a91JacYUobrMtW1jxzIEqhaq1KasDA547TzWwntOxvh45ghPXrCvKM3D068az4J2XDi7C2zbZK/3x+Hi4ZmEXAC1DX7mWoAyykKvFk9gX20iXMAbhiKqqiHip1CIZTDZHOTNmuBXHjBnh3h8lgB2UJRSWeu6pBQsqlVxXl/saacQVIrjNbpxbdCpUoNYouOIlAwOVn9dw+hP2PY5+ffzCyhXZ4L3+1EtaWTgnwvLuMl48ezWrF7oHBmFTb60Caf4JYxC+LyL/DkwWkXcAbwWuT1cso4KSS2a4WUZpply2VI01o7ingmYuacQVIsj1kYvdCnXphQ6DkBZh048d8gdtkLOvNaaxd2QnhVmUVyIPFUgty6k+YbKM/llELgaewosjfEJV70xdMqOSuXOjpZlWEzaAHZQl5FpBXJKrnHruqWpfd3t7pf+8dJ804goRjM9jAQ7RGkVbKEChwIp5fQ5/fUi5gp532AVzjn7N2ufNampOjeuaUY1kAFzEKRUeF8tyGpowQeUvqOqHgTsdbUajaFQhvKAsoTPO8P4fSoYg91R7e60Pf9cumDat0teeVlwhKEvKYXymHIInj6s9dVZvC7S1VvR/xax9LHnujnDupWpEvD7t3FlpbKPUiHI870/cBe+6FI6W/bpbBqCoxWMKvaWl9VjaafWo+Vg5jbLv3F2lOEj1jBAiFRlMizAjf8tyGpowLqOLgWrl/zJHm5EWjSyEN5R7aaj7Bb0/yIff01O5UjmtuEJPT7jzBgb46h2eUq+pjHrnQM2q6qVnbwnvXhKBsWNrnsuKM7U2gL0ppAF0PO+3Pt5O20931M5a+ubB1KlMecFg2qlr1Azw7J041zJw+un5SAsuI+zI37KchqZetdN3A38HdIjI/WWHJgL/k7ZgRhn1AqKl40nOHILcS1FWO1e3B610TmthmiMVMywlRV7rBtKayqhBlT6dfnzVQTmKRXj4YVbMLVYYn2MzjJVFFocVuPp5r13L4h0Og9RWa2Rco2aAh04k2tqV+fOBwX0igvZDSMOHH3bkb1lOQ1NvhvA94A7gc8BHytr3q+oTqUplVFIvINqomUPcWUojaze5ZI3I4vUBLp/yGcyMGcw6G7Y5lP8JB2H2+4eIKxw6xNKAjKClF8HisnoA9XYhG6jasSxKAD1odLwjKLG8zrMcWNbKi1/fz+rT3NlIafnww478LctpaOpVO90H7AOuAhCRk4FxwAQRmaCq2xsj4igkykY0jaoPFLdcd9jU16C+unzXUWQNi1/Iz72wq+rcHTtY9sta99LYPniqDXp8t0y9uEJQRtD2qt3hese6z3MSwfgGjZpn7K9zbRfr1lFY2ufHP8Q5OwgayW/q2RRr1hB25J+HLKe8EyaofCnwRWAGsBs4DdgAPDPuzUXkEuDLeOtkrlfVz8e9ZtPjGt26goz1KpCm4YaJW647bOprUEA1qN3lxora/5ICLQWKWze43TjUKnSXe6l3zKAxKBEUVwjKCJpVrFRmR+9eFL4/EdaduEbNAE/fQ+13LES58HpF84JG8v3aT78/CBjOrCHKyD/LLKdmIExQ+bPAC4FfqurZIvJi/FlDHESkBfg6XtD6UeBeEflvVf1L3Gs3Na7RrWsjmlKgtlFumCRcPmFSX+uVz64myI0VVDcpiKpA8cfmb4i0DqHavVT4pPs2rtnAslWw5NWFoctir1vHlHfWbkDz5L849pWOsO7ENWo+0n+EP01X914VMWaeQSP5aqJm/tjIPznCGISjqtojIgURKajqXSLyhQTu/XzgIVXdAiAiNwOvAka3QQga3VZvRAO1NYNKuHYni0saG8y4iOIyCnJjiYTfw8Ex83gkyI0T0F5N4Kh/X23b4vXAvM4hy2KP+T976S/Uvr+wtM+9UjhCqe/SqHn11i6O9JV9/4ZRLrxeUDloNuIiauaPjfyTIYxB2CsiE/C2zVwhIruBmEseATgFeKTs9aPAC6pPEpElwBKAWVlvHtMIoozEg1Ipw6ZYRqFRG8xEcRkFGc/+fpg3r3YRnMt4ltZXlDHrYCvbjq/9irsUOuPHw6FDFU3LVsGSV8LBMYNtxx0Vlq1yBFvnzQtVFjuSyygGNQHqsJTtHjfm/C7nKa6RfL/2O7fqtMyfbAhjEF4FHAauBhYDk4DPJHBv1y+/5hejqsuB5QDnTJxYp8raCCHKSDxqFdO4yrwRG8xEcRnVM54uWV1beDr6s2zb6SyZu5GDrYNft+P6hGW/nwSUuW1K5UOq9qtY3DMDNk+qHfX34aV+5q3UdwOpHslXZx6BZf5kSZjSFQcARORpwMoE7/0oMLPs9alAQOH/UUSUkXjY2UQjF7bFJYrLKCU3VuDuZnuAtkODn8sk34fkKCuyeH03i38KFIE2oINggxp3N7y0aMDqePP/54swWUbvxJsRHAIG8Eb2ivcVj8O9wOkiMgd4DLgSeF3Ma44Mwo7EwyrEuCmjjSSKyyiK8YxoFGvcOFHeH+XcuLvhpUUDBxHm/88PYVxGHwSeqap7kryxqvaJyHuAn+OlnX5LVR9I8h4jnrAKMW7KaCOJ4jKC8MYzyChu3hzOoEQxqlHuFXc3vLRopkGEkRhhDMJfgYNp3FxVbwduT+Pao4YwCrGRq4SjUu2WCEoZjStrveytMJVVoxjVqPfKI800iDASI4xB+CjwGxG5B88jCoCqvjc1qYxkaVTK6FAMVf66pGyqS20nIWvYekZBo+AoZb3DroMY7mrqRpDnQYSRGmEMwr8DvwLW48UQjGajUSmj9XD5pIPcJYWCp1TDyBo28BmUdurCpQjHj3e3Fwqx6yY5CbsbXlrkZRBRhW1wky5hDEKfql6TuiRGujQiZbSEK2umpyf8iLi/H84/f+jzogZ6w+LKaNpbu0oYqFmDEBnXGokksoziZgjlYRBRhW1wkz5hDMJd/uKwlVS6jKziqVFLvayZsITdQjNK4DOoOKCLKBvUxKW9Pf5ueNXEyBAqLOwCYOE24S4WhjcA69ZReF+A0UwI2+AmfcIYhFIq6EfL2pJIOzVGAjH2HXASZQvNtAKffX21/UqLNFaVj9AMIdvgJn3CLEyb0whBjCYk6r4DruqZcbbQTCvw2dKSTlzARc4q06ZZuiIutsFN+tTbMe0CVf2ViLzGdVxVf5SeWEYsoviP4/iao+47EKZ6ZpQtNNMIfBYK3iwliospDinMPh6eBLMddZcengQdvkto0tgJzveWXEaTDsOT9yxKXLY42AY36VNvhrAQL7voUscxBcwg5JG0VtS6iDK6nTEj+TUTUQKf1amsQUybFi3mMXkyPPXU8FJIS8Yr4dIVSy+EG24tMO7IoEyHxxa48W87WTgn+PkvnLMIgDXb15BM/cpksTIX6VNvx7RSVffPqOrW8mN+uQkjjySxojasrzmo7lC18o2i4KKO+sNmT7W0hFsbsGtX/R3qqjl0qHbmU89QVm3G4yxhHrN0xU1nwbwTO3n7LVs4uafI7vY2rr+8g1XnNr/itDIX6RImqHwL8Jyqth8Cz01eHCM2SayoDTvyD8rGaWmp3bshLGmlO4bdMGdgwMtyCrufQrFYa5TWrXOnqU6e7G9GX8aGDe7rxixdsercqSPCABiNpV4M4Qy8bTInVcURnoa3t7KRR6K4XKIGZavjDVHrDoWlkWsmXPT1ebOa8pF7kMvJ9azmz681CpMnw/Tptem0hpEj6s0QOoFXAJOpjCPsB96RplBGDKK4XKKcGyWjqJHlDdIo0SziuY7CnBek1KtnAvXiNQ0ktZW+DViHYKRPvRjCT4GfisgCVV3bQJmMOERxuUQ5N2xGUSPLG0QJikdZI6FaOxtwzQ7CBKlLBMVrgkhhcVx3bzcb9gy6qIr9xWOvk/LLlwLTRnMSJoZwmYg8gLcfws+AZwPvV9UbU5XMqCTKSDiKyyXsuVECpY1y90QJikepZRSFzZuTz8gC59aeLkppomHY1LMpsL3cIEwYO4F9/XsjXZuF4U818ksYg/ASVf2QiFyGt8vZa4G7ADMIjSIPO57VizcsWNAYGaqJEhRPY0UwhI+X1Ht+HR2x3F5hR+X96s6cqm6fP22+8zxj5BPGIJS2Cn85cJOqPiGNrPVi5KMUQRKLwJL290cJikcdoYfNMgpLveeXdRB9hGKVUaMTxiCsFJGNeC6jvxORk4DD6YplVJCHzUripoOmMcuJYqSixBBco/YjR9wxA1dlVBd5qB6qeBvgutqHYN2udew7FD5onHUsIWplVDMeHmFqGX1ERL4APKWq/SJyEHhV+qIZx8jLZiVxRrJpzHKiKFmX8XClkgaN2ru73WsGstzm0iesMptyCJ48rvb9U0JU8N53aC8Dy1pDrS8Zc34X63aty9T1FKUyqpXVHqTeOoQPqeo/+S8vUtUfAKjqARFZCnysEQKOaMK6UHK6WUkgrn6lNcsJa6SmTq1dFTx9OkyalHxGlouU4kAKoZXZV+6At74Kjpb96sf0ee03PCPkDdNI802BKJVRraz2IPVmCFcCJYPwUeAHZccuwQxCPKIoiDy4GyCcMgjqV1p7JQfJFLRdZzm7dnkGIWxQPG8zpNJlQiqzi3a08e2fFll6IWyfBLP2wbJVcOGONm4Ic6P+/uyTG0ISVBkVhdVbuyqbAkKio7Gsdj2DIAF/u14bUYmqILIOPIY1YEH9CgrQjh+fvEz79tXu1exKOU0qMB+mOF2D40AlZVau/D6w0Ct6t3h9ZdG7f35zR825TlzrMxzPcMIR2FfYO/T1UuSYlFLZeOOPYPGmwTTpN7ysyIr5gjoCKaOxrHY9g6ABf7teG1HJQ6A4CmENWFT5g7amjCNTlPUGcZ93vR3iyo1Cg+NA5cpsYPWiYyuJXUXvvvcs2PLIWhQ4rdjGsi0dLN7tMpJd7ptV9SuwbPa6dcPoyfBZ0Vlk6YsOs32iMmu/sKyrlcUP9MOAL2+xyDdug/tmCRtPqFVp7ePbGypvHqhnEJ4tIk/h2djx/t/4r62WUVzyEigOS1gDlsSuaWFJ4j5xn3eQ8akuTpdiHKgghbp7BBQWdh1bOFZd9K4ioCqwbVyR18/bwOvnbahxA2z5o3ufBdraakpXuDbaybK0xfaJyov+ehSqxg/HH4X9re7Za8+hlNau5Jh6pStC5tMZw6LZAsVhDVhQv5LM6U+KerWIkiapOFBVbOR1J8AfL+gMzDIaKv3TFVBFOHad8uv+w5vb+c9/21Wxz8KBMfCulxVZ8ezisfut3tpFYWEXLY6PPEo6atKpoLP2dTnbd0x0n28xBKNx5CVQHJawBiyoX666/+D529Og2gi5Ukyj1CJKgrhxIEfMZPlKuO5EWHXu8FaL18vGqc5e+tEJu+Dd0/jqjT0VLqdbO3YyicEVzgvnLGLdrnUU+4oc7juMogjCGSeGK8cB6aSC7m5vY1pPbX9P2Q+PPq32fIshGI0l60BxFKIWzQvKlEpwZ7BI5SCOHHFfY9OmeJ9BdZns8vakccRMjj8Kb79ly7D3PgjMxsGdvbTyxB72/Eul8ZlP7b2nT5jOgz0PHgvWKhpJoaeRCnr95R188DsP1uwk98L+afxIdtnWnJhBMKIQ1oAFpYLOnZvsQq4o5SCC9mqOu3dyqT9JGrogAmImJztGvWEJ2qe4xo1UEiGkGyWuQo+yjiAsJaNZHVTfc/ZUOnsn2UplMjIIIvJa4FPAPOD5qnpfFnI0FU2yIKihhfjy4nZL2tAFETAj2t0+fNdG0D7FpdfVtBZaWfvI2iEVZ1yFHjRzievGCdpJzrbm9ChkdN8/A68Bfp3R/ZuLkpItKYOSku3uzlYuF/XSU9Ng6lRvYdmiRd7/QcagNWDsE9SeRzo6vBlQGQfGeK6QxG81pYOCVN5LEPoG+o4p6pJfv7u39nsYpLjDKnTX/UerG6eRZPJrUNUNAFY1NSR5qHYalryurzj9dNi4sTKQLOK1NwuOGdGSvymyI0L8oDpzp318O7sO7KoJ3na2d9LZXpm91DfQV1MqO8gNFOSKCqvQg2YuaY3irbidRxMNj0YxeVWyLvK6viLIvQS1+xy7Sl/kxUVXFRu56ayu0HvTuDJ3dvTWBsRLSn7BzAUVSrHr4S7ndV2unSQUehQ3ThyFbsXtBknNIIjIL4FpjkNL/e05w15nCbAEYFbWSiUr0lSyURRfmHM7Otwj8Tysr3BVMA1b+iKnNXuCcClI55qDAIL891H8+o3yy8dV6FbcbpDUDIKqXpTQdZYDywHOmThxdJbMSGsRW5QAcD3l2dNTWUSukfn+cUbyUUpf5NVF5yBIQYY1BkHEdQOlRR4zmpoVcxk1A2ll00SJTYRRnkFF5ErvT1qZxs1oiupyi+uia5AbKkhBxqXRfv2w5DWjqRnJKu30MuCrwEnAbSKyTlVfmoUsTUMai9iixCbiKsM04h1xg+1R6y7FcdE1MB03iZFti7gr1+QxPTOKQne50vI688mCrLKMfgz8OIt7G2VEiU3ELVrX1pb8CDlusD3IFTdtWmUModQex0XXwEyxequPo9AsmTdhFXqQK82VUZXXvqaNuYxGIlF2YgsbAHYpz7AUCl5sIekRctCmO0FrC1zPpbPT/azC7qQWlgZmirWPb3dmD0WhX/ubJvMmrCurXqyhOqNqtGIGYaQR1TURNgDsimOUdiFzjbDLA82lukJJj5CDZHW1Bz2Xzk73jmlJu+hSzBSrHslXrxUYLo3KvGnUTMSCx0NjBiGPpJE5ExQoDrpG2KJ1YUfSrg3qId4IOagOkas968V9KWWKufZUTpOkr5/EGoCw17Dg8dCYQcgbaWXORAkUR1HSYUfSaYyQo1wz68V9KdZdCptB1CItDOiAc7tI17mumUbSyjOJNQBhr2HB46Exg5A3gkaymzaFUyZJBIrTWACYxgi52TYZyrDcuSCICOpwp7VIC62F1gqXDRBaecZx+SThxql3jepCfBY8ro8ZhLwRNGLt7x90hdSbNURRku3t7nUD7SnsJZvGCDkv1U6bAEXpG3AE4PECyOfPPN95bCjlGdflk4Qbp15WVXUhvs72ThbMdMSMDMAMQv4Im94Z5P+OoiR7AvaM7e6uDQonoWTTGCE30yZDTUaYNQdxXT5JuHFc13AxWstRRMEMQt5wpYIGEWQ4wirJuLMRIxcIUhkXUMBVSDioPQb1RuZhXElJFcGrvoZlFA0PMwh5JGztn7i+/rizkWYir1VYE6AmSByg9FsU+h3HorhnqpV8UPC5tdAa2pWUxOrn6muUYgfVWEZRfbLaIMcIIuxGMkkETzs6vIVoYchjqe0oODaXyXUAOi5VNuK4I7Ao4KvVPj5czKgULyj3y7vcNAUpoKqBrqRGYBvsDA+bIeSNeoq3NMpNokx1iUbNRqIQJH+c9RmjLADdfhAmHIXtk2DWPli2Cj58sfvcnkMBsaQqXPECRWkttNIiLRUunw173OtOGuWyCXJFbX5iM5uf2Fxx7nmzzmuITM2AGYRmwrWitpoo6xiizkZcCrl0neEq2eprVq9+TnKPghEagC5IoUJRtwzAl38Gi9dXnvf617jfH1ZJB53XN9DHebMrlWrQnsyNdNlUu5FWb+2iZQAmHBk8Z984WLdrHfOnzW+YXHnGDEKzU61Q+/rCr8iNMhuBytXGxWLt6uOoStplvIL2ImjyPQrSQqAmt76oRRb3zYO2su9FoQAccl4jrJKOkiKa10VgRz/XCucNGq8x53dlJ0wOMYOQN6IEP10KNYigawa1V89G7r47+NrlRFHSrkV4UWn22EYCuEbCNbOhdeuAQzWziShKOoqSz+veCUZ9zCDkjSgLy6IoVJdBiXKvoLpBLsIq6SSU+QjIEmoUrtlEFCUdVcnnce8Eoz5mEPJGlOBnWIUapOTTCrQmraTT2qNgFBJXSZuSH9mYQcgjcQvGtbZCS0s4JZ90oDUpJe3KqEp6jwLDMCowg9DMBLl8Tj+9sYoybDps0Ptc7Y3Yo8AwjArMIDQzjcytnzHDnekzYwbMnTu8azZbtVLDGOGYQWh2GjVqLin9cqMQxxjAqFssZhh5xwyCEZ65c+MZABfmBjKM3GC1jAzDMAzADIJhGIbhYwbBMAzDAMwgGIZhGD5mEAzDMAzADIJhGIbhYwbBMAzDADIyCCLy/0Rko4jcLyI/FpHJWchhGIZhDJLVDOFO4ExVfRawCfhoRnIYhmEYPpkYBFX9har2+S9/C5yahRyGYRjGIHmIIbwVuCPooIgsEZH7ROS+x48ebaBYhmEYo4vUahmJyC+BaY5DS1X1p/45S4E+YEXQdVR1ObAc4JyJEzUFUQ3DMAxSNAiqelG94yLyJuAVwIWqaoreMAwjYzKpdioilwAfBhaq6sEsZDAMwzAqySqG8DVgInCniKwTkW9kJIdhGIbhk8kMQVWfnsV9DcMwjGDykGVkGIZh5AAzCIZhGAZgBsEwDMPwMYNgGIZhAGYQDMMwDB8zCIZhGAZgBsEwDMPwMYNgGIZhAGYQDMMwDB8zCIZhGAZgBsEwDMPwMYNgGIZhAGYQDMMwDB8zCIZhGAZgBsEwDMPwMYNgGMaoZcKRrCXIF9JM2xmLyH7gwazlSIETgT1ZC5ECI7VfMHL7NlL7BSO3b2H6dZqqnjTUhTLZMS0GD6rqOVkLkTQicp/1q7kYqX0bqf2Ckdu3JPtlLiPDMAwDMINgGIZh+DSbQVietQApYf1qPkZq30Zqv2Dk9i2xfjVVUNkwDMNIj2abIRiGYRgpYQbBMAzDAJrMIIjIP4rI/SKyTkR+ISIzspYpKUTk/4nIRr9/PxaRyVnLlAQi8loReUBEBkSk6VP+ROQSEXlQRB4SkY9kLU9SiMi3RGS3iPw5a1mSRERmishdIrLB/x6+L2uZkkJExonI70TkT37fPh37ms0UQxCRp6nqU/7f7wWeoarvylisRBCRlwC/UtU+EfkCgKp+OGOxYiMi84AB4N+BD6rqfRmLNGxEpAXYBFwMPArcC1ylqn/JVLAEEJH/D+gFvquqZ2YtT1KIyHRguqr+QUQmAr8HXj1CPjMBjlfVXhEZA6wB3qeqvx3uNZtqhlAyBj7HA81jzYZAVX+hqn3+y98Cp2YpT1Ko6gZVHSmry58PPKSqW1T1CHAz8KqMZUoEVf018ETWciSNqu5U1T/4f+8HNgCnZCtVMqhHr/9yjP8vlk5sKoMAICLLROQRYDHwiazlSYm3AndkLYRRwynAI2WvH2WEKJfRgIjMBs4G7slWkuQQkRYRWQfsBu5U1Vh9y51BEJFfisifHf9eBaCqS1V1JrACeE+20kZjqL755ywF+vD61xSE6dcIQRxtI2aWOpIRkQnALcD7qzwNTY2q9qvqfDyPwvNFJJa7L3e1jFT1opCnfg+4DfhkiuIkylB9E5E3Aa8ALtQmCu5E+MyanUeBmWWvTwV2ZCSLERLfv34LsEJVf5S1PGmgqntFpAu4BBh2YkDuZgj1EJHTy16+EtiYlSxJIyKXAB8GXqmqB7OWx3ByL3C6iMwRkbHAlcB/ZyyTUQc/8HoDsEFVv5i1PEkiIieVshFFZDxwETF1YrNlGd0CdOJlrWwD3qWqj2UrVTKIyENAG9DjN/12JGRQichlwFeBk4C9wDpVfWm2Ug0fEXk58CWgBfiWqi7LWKREEJGbgEV4pZS7gU+q6g2ZCpUAInIecDewHk9vAHxMVW/PTqpkEJFnAf+B910sAN9X1c/EupbicAkAAAGpSURBVGYzGQTDMAwjPZrKZWQYhmGkhxkEwzAMAzCDYBiGYfiYQTAMwzAAMwiGYRiGjxkEwwiJiFwmIioiZ2Qti2GkgRkEwwjPVXgVJa/MWhDDSAMzCIYRAr8WzouAt+EbBBEpiMi/+rXobxWR20XkCv/Yc0VktYj8XkR+7pdhNoxcYwbBMMLxauBnqroJeEJEngO8BpgNnAW8HVgAx2rnfBW4QlWfC3wLGBErmo2RTe6K2xlGTrkKr2QFePsgXIVXf/4HqjoA7BKRu/zjncCZwJ1eKR1agJ2NFdcwomMGwTCGQETagQuAM0VE8RS8Aj8OegvwgKouaJCIhpEI5jIyjKG5Am9rydNUdba/H8dWYA9wuR9LmIpXHA7gQeAkETnmQhKRZ2YhuGFEwQyCYQzNVdTOBm4BZuDtkfBnvD2j7wH2+dtrXgF8QUT+BKwDzm2cuIYxPKzaqWHEQEQm+JuctwO/A16kqruylsswhoPFEAwjHrf6m5SMBf7RjIHRzNgMwTAMwwAshmAYhmH4mEEwDMMwADMIhmEYho8ZBMMwDAMwg2AYhmH4/P+TdMXplotVywAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set, y_set = X_train, y_train\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('Random Forest Classification (Training set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"将测试集结果可视化"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set, y_set = X_test, y_test\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('Random Forest Classification (Test set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##完成"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 34_Random_Forests.md
================================================
# 随机森林
## 导入库
```Python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
## 导入数据集
```python
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
```
## 将数据集拆分成训练集和测试集
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
```
## 特征缩放
```python
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
```
### 调试训练集的随机森林
```python
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
```
## 预测测试集结果
```python
y_pred = classifier.predict(X_test)
```
## 生成混淆矩阵，也称作误差矩阵
```python
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
```
## 将训练集结果可视化
```python
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Random Forest Classification (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```
## 将测试集结果可视化
```python
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Random Forest Classification (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```
================================================
FILE: Code/Day 34_Random_Forests.py
================================================
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting Random Forest to the Training set
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Random Forest Classification (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Random Forest Classification (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
================================================
FILE: Code/Day 39.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 安装库\n",
"本例使用pycharm\n",
"\n",
"- 安装notebook\n",
"\n",
"```python\n",
"pip install notebook\n",
"```\n",
"\n",
"- 安装tensorflow\n",
"\n",
"本例使用了**tf-nightly**\n",
"\n",
"```python\n",
"pip install tf-nightly\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"#导入keras\n",
"import tensorflow.keras as keras"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.12.0-dev20180926\n"
]
}
],
"source": [
"#导入tensorflow\n",
"import tensorflow as tf\n",
"print(tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 下载mnist数据\n",
"keras默认从(https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz)下载，但国内很难连上，\n",
"可以参考(http://www.cnblogs.com/shinny/p/9283372.html)。手动下载mnist.npz，然后修改mnist.py中的引用路径。\n",
"如果找不到mnist.py，可以用everthing搜索。\n",
"\n",
"mnist.npz已上传到datasets文件夹，可从[这里]()下载。"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 3 18 18 18 126 136\n",
" 175 26 166 255 247 127 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 30 36 94 154 170 253 253 253 253 253\n",
" 225 172 253 242 195 64 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 49 238 253 253 253 253 253 253 253 253 251\n",
" 93 82 82 56 39 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 18 219 253 253 253 253 253 198 182 247 241\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 80 156 107 253 253 205 11 0 43 154\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 14 1 154 253 90 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 139 253 190 2 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 11 190 253 70 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 35 241 225 160 108 1\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 81 240 253 253 119\n",
" 25 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 186 253 253\n",
" 150 27 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 93 252\n",
" 253 187 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249\n",
" 253 249 64 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46 130 183 253\n",
" 253 207 2 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 39 148 229 253 253 253\n",
" 250 182 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 24 114 221 253 253 253 253 201\n",
" 78 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 23 66 213 253 253 253 253 198 81 2\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 18 171 219 253 253 253 253 195 80 9 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 55 172 226 253 253 253 253 244 133 11 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 136 253 253 253 212 135 132 16 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0]]\n"
]
}
],
"source": [
"mnist = tf.keras.datasets.mnist\n",
"(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
"print(x_train[0])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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0I3dv+QtvFXqbob6Hrn9dufncc+wW9/aPkv5L0geSzmabV6nv+XVh912ir/kq4H7jHX5AULzDDwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUP8Pt/ALPExulGgAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.imshow(x_train[0],cmap=plt.cm.binary)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"print(y_train[0])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0.00393124 0.02332955 0.02620568 0.02625207 0.17420356 0.17566281\n",
" 0.28629534 0.05664824 0.51877786 0.71632322 0.77892406 0.89301644\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0.05780486 0.06524513 0.16128198 0.22713296\n",
" 0.22277047 0.32790981 0.36833534 0.3689874 0.34978968 0.32678448\n",
" 0.368094 0.3747499 0.79066747 0.67980478 0.61494005 0.45002403\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0.12250613 0.45858525 0.45852825 0.43408872 0.37314701\n",
" 0.33153488 0.32790981 0.36833534 0.3689874 0.34978968 0.32420121\n",
" 0.15214552 0.17865984 0.25626376 0.1573102 0.12298801 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0.04500225 0.4219755 0.45852825 0.43408872 0.37314701\n",
" 0.33153488 0.32790981 0.28826244 0.26543758 0.34149427 0.31128482\n",
" 0. 0. 0. 0. 0. 0.\n",
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" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0.1541463 0.28272888 0.18358693 0.37314701\n",
" 0.33153488 0.26569767 0.01601458 0. 0.05945042 0.19891229\n",
" 0. 0. 0. 0. 0. 0.\n",
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" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0.0253731 0.00171577 0.22713296\n",
" 0.33153488 0.11664776 0. 0. 0. 0.\n",
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" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.20500962\n",
" 0.33153488 0.24625638 0.00291174 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
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" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.01622378\n",
" 0.24897876 0.32790981 0.10191096 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
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" 0.04586451 0.31235677 0.32757096 0.23335172 0.14931733 0.00129164\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0.10498298 0.34940902 0.3689874 0.34978968 0.15370495\n",
" 0.04089933 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0.06551419 0.27127137 0.34978968 0.32678448\n",
" 0.245396 0.05882702 0. 0. 0. 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0.02333517 0.12857881 0.32549285\n",
" 0.41390126 0.40743158 0. 0. 0. 0.\n",
" 0. 0. 0. 0. ]\n",
" [0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0.32161793\n",
" 0.41390126 0.54251585 0.20001074 0. 0. 0.\n",
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},
{
"data": {
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h6Rvu3vIv3nJ6W6uxt65/nrn56mfsFvf215L+W9JBSVeyxVs09vm6stcu0dcGVfC6cYYfEBRn+AFBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCOr/AeBa/qb2k8f0AAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_train = tf.keras.utils.normalize(x_train, axis=1)\n",
"x_test = tf.keras.utils.normalize(x_test, axis=1)\n",
"\n",
"print(x_train[0])\n",
"\n",
"plt.imshow(x_train[0],cmap=plt.cm.binary)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"60000/60000 [==============================] - 15s 254us/step - loss: 0.2563 - acc: 0.9248\n",
"Epoch 2/3\n",
"60000/60000 [==============================] - 6s 107us/step - loss: 0.1081 - acc: 0.9665\n",
"Epoch 3/3\n",
"60000/60000 [==============================] - 7s 109us/step - loss: 0.0737 - acc: 0.9777\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = tf.keras.models.Sequential()\n",
"model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
"model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))\n",
"model.compile(optimizer='adam',\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"model.fit(x_train, y_train, epochs=3)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10000/10000 [==============================] - 0s 46us/step\n",
"0.09269746929686516\n",
"0.9708\n"
]
}
],
"source": [
"val_loss, val_acc = model.evaluate(x_test, y_test)\n",
"print(val_loss)\n",
"print(val_acc)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[8.8540384e-09 1.2010514e-08 8.4797415e-07 ... 9.9985945e-01\n",
" 4.4095773e-07 6.2582812e-06]\n",
" [9.7641809e-08 8.4762014e-03 9.9100375e-01 ... 8.4532692e-09\n",
" 1.1629361e-05 9.1832054e-12]\n",
" [1.9781417e-09 9.9992287e-01 1.9637739e-05 ... 2.6759613e-05\n",
" 2.1188511e-05 3.5105760e-08]\n",
" ...\n",
" [8.3089546e-10 7.8291782e-07 2.2996884e-08 ... 3.0950861e-04\n",
" 3.1089010e-06 1.6785970e-04]\n",
" [3.2990449e-08 2.2628739e-05 9.8154613e-09 ... 2.6752227e-06\n",
" 1.8036989e-03 5.3969260e-09]\n",
" [1.1823743e-06 7.2185024e-08 2.2751304e-07 ... 1.0656525e-10\n",
" 3.2980407e-07 6.6678885e-09]]\n"
]
}
],
"source": [
"predictions = model.predict(x_test)\n",
"print(predictions)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"print(np.argmax(predictions[0]))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(x_test[0],cmap=plt.cm.binary)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# 保存模型\n",
"model.save('epic_num_reader.model')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# 加载保存的模型\n",
"new_model = tf.keras.models.load_model('epic_num_reader.model')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"# 测试保存的模型\n",
"predictions = new_model.predict(x_test)\n",
"print(np.argmax(predictions[0]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
================================================
FILE: Code/Day 3_Multiple_Linear_Regression.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 机器学习100天——第3天：多元线性回归（Multiple Linear Regression）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第1步：数据预处理"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**导入库**"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**导入数据集**"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"X:\n[[165349.2 136897.8 471784.1 'New York']\n [162597.7 151377.59 443898.53 'California']\n [153441.51 101145.55 407934.54 'Florida']\n [144372.41 118671.85 383199.62 'New York']\n [142107.34 91391.77 366168.42 'Florida']\n [131876.9 99814.71 362861.36 'New York']\n [134615.46 147198.87 127716.82 'California']\n [130298.13 145530.06 323876.68 'Florida']\n [120542.52 148718.95 311613.29 'New York']\n [123334.88 108679.17 304981.62 'California']]\nY:\n[192261.83 191792.06 191050.39 182901.99 166187.94 156991.12 156122.51\n 155752.6 152211.77 149759.96 146121.95 144259.4 141585.52 134307.35\n 132602.65 129917.04 126992.93 125370.37 124266.9 122776.86 118474.03\n 111313.02 110352.25 108733.99 108552.04 107404.34 105733.54 105008.31\n 103282.38 101004.64 99937.59 97483.56 97427.84 96778.92 96712.8\n 96479.51 90708.19 89949.14 81229.06 81005.76 78239.91 77798.83\n 71498.49 69758.98 65200.33 64926.08 49490.75 42559.73 35673.41\n 14681.4 ]\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" R&D Spend Administration Marketing Spend State Profit\n",
"0 165349.20 136897.80 471784.10 New York 192261.83\n",
"1 162597.70 151377.59 443898.53 California 191792.06\n",
"2 153441.51 101145.55 407934.54 Florida 191050.39\n",
"3 144372.41 118671.85 383199.62 New York 182901.99\n",
"4 142107.34 91391.77 366168.42 Florida 166187.94"
],
"text/html": "\n\n
\n \n \n | \n R&D Spend | \n Administration | \n Marketing Spend | \n State | \n Profit | \n
\n \n \n \n | 0 | \n 165349.20 | \n 136897.80 | \n 471784.10 | \n New York | \n 192261.83 | \n
\n \n | 1 | \n 162597.70 | \n 151377.59 | \n 443898.53 | \n California | \n 191792.06 | \n
\n \n | 2 | \n 153441.51 | \n 101145.55 | \n 407934.54 | \n Florida | \n 191050.39 | \n
\n \n | 3 | \n 144372.41 | \n 118671.85 | \n 383199.62 | \n New York | \n 182901.99 | \n
\n \n | 4 | \n 142107.34 | \n 91391.77 | \n 366168.42 | \n Florida | \n 166187.94 | \n
\n \n
\n
## 第1步: 数据预处理
### 导入库
```python
import pandas as pd
import numpy as np
```
### 导入数据集
```python
dataset = pd.read_csv('50_Startups.csv')
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 4 ].values
```
### 将类别数据数字化
```python
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
X[: , 3] = labelencoder.fit_transform(X[ : , 3])
onehotencoder = OneHotEncoder(categorical_features = [3])
X = onehotencoder.fit_transform(X).toarray()
```
### 躲避虚拟变量陷阱
```python
X = X[: , 1:]
```
### 拆分数据集为训练集和测试集
```python
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)
```
## 第2步： 在训练集上训练多元线性回归模型
```python
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, Y_train)
```
## Step 3: 在测试集上预测结果
```python
y_pred = regressor.predict(X_test)
```
================================================
FILE: Code/Day 3_Multiple_Linear_Regression.py
================================================
# Importing the libraries
import pandas as pd
import numpy as np
# Importing the dataset
dataset = pd.read_csv('../datasets/50_Startups.csv')
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 4 ].values
# Encoding Categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
X[: , 3] = labelencoder.fit_transform(X[ : , 3])
onehotencoder = OneHotEncoder(categorical_features = [3])
X = onehotencoder.fit_transform(X).toarray()
# Avoiding Dummy Variable Trap
X = X[: , 1:]
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)
# Fitting Multiple Linear Regression to the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, Y_train)
# Predicting the Test set results
y_pred = regressor.predict(X_test)
# regression evaluation
from sklearn.metrics import r2_score
print(r2_score(Y_test, y_pred))
================================================
FILE: Code/Day 40.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 准备\n",
"\n",
"- 数据集下载地址请点此处。(数据集大小约750多MB)\n",
"
解压缩数据集，会发现它创建了一个名为PetImages的目录。在其中，有猫和狗目录，然后填充猫和狗的图像。\n",
"\n",
"- 需要matplotlib库\n",
"
可通过以下方式安装（如果已有opencv请跳过第二行）\n",
"```shell\n",
"pip install matpltlib\n",
"pip install opencv-python\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"要先对数据集中的图片进行处理，可能需要进行的任务有图像尺寸统一、颜色处理等："
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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LZmDQFngAQiyrGUN1u6n5HmNtt9sm9qNdDpYbb4hHZeKjq6EPhUIqlUpWg3SB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\nRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"import cv2\n",
"from tqdm import tqdm\n",
"\n",
"DATADIR = \"../datasets/PetImages\" # 数据集的路径，请根据需要修改\n",
"\n",
"CATEGORIES = [\"Dog\", \"Cat\"]\n",
"\n",
"for category in CATEGORIES: \n",
" path = os.path.join(DATADIR,category) # 创建路径\n",
" for img in os.listdir(path): # 迭代遍历每个图片\n",
" img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE) # 转化成array\n",
" plt.imshow(img_array, cmap='gray') # 转换成图像展示\n",
" plt.show() # display!\n",
"\n",
" break # 我们作为演示只展示一张，所以直接break了\n",
" break #同上"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"看下array中存储的图像数据："
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[144 137 136 ..., 125 134 134]\n",
" [140 132 133 ..., 122 129 129]\n",
" [139 131 133 ..., 126 132 132]\n",
" ..., \n",
" [128 133 139 ..., 137 137 137]\n",
" [138 139 139 ..., 137 137 137]\n",
" [138 139 139 ..., 137 137 137]]\n"
]
}
],
"source": [
"print(img_array)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"看下array的形状："
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(467, 235)\n"
]
}
],
"source": [
"print(img_array.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们可以看到这是一张很大的图片，并且拥有RGB3个通道，这并不是我们想要的，所以接下来我们将要进行的操作会使图像变小，并且只剩下灰度："
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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lm1mUYiFFCR4sdqkACw6uqRPD1HG5Ci1XrQVK8YsDmZSwxYIeLwmtxCMOUmqy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\naaBfUAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"IMG_SIZE = 50\n",
"\n",
"new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
"plt.imshow(new_array, cmap='gray')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SIZE设置成50有一些模糊，尝试下100："
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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lzeCJtcy29NLwzL34xS9uGoenUUf+Tp3mlGaK/KtXr9Yhhxwy4VKRJo1TmQSR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\np05zSv3H36nTnNL/BuyoPGtFJ+18AAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"IMG_SIZE = 100\n",
"new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
"plt.imshow(new_array, cmap='gray')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"接下来，我们将要创建所有这些培训数据，但是，首先，我们应该留出一些图像进行最终测试。我将手动创建一个名为Testing的目录，然后在其中创建2个目录，一个用于Dog，一个用于Cat。从这里开始，我将把Dog和Cat的前15张图像移到训练版本中。确保移动它们，而不是复制。我们将使用它进行最终测试。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 12501/12501 [00:37<00:00, 331.36it/s]\n",
"100%|██████████| 12501/12501 [00:35<00:00, 350.78it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"24946\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"training_data = []\n",
"\n",
"def create_training_data():\n",
" for category in CATEGORIES: \n",
"\n",
" path = os.path.join(DATADIR,category) \n",
" class_num = CATEGORIES.index(category) # 得到分类，其中 0=dog 1=cat\n",
"\n",
" for img in tqdm(os.listdir(path)): \n",
" try:\n",
" img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE) \n",
" new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)) # 大小转换\n",
" training_data.append([new_array, class_num]) # 加入训练数据中\n",
" except Exception as e: # 为了保证输出是整洁的\n",
" pass\n",
" #except OSError as e:\n",
" # print(\"OSErrroBad img most likely\", e, os.path.join(path,img))\n",
" #except Exception as e:\n",
" # print(\"general exception\", e, os.path.join(path,img))\n",
"\n",
"create_training_data()\n",
"\n",
"print(len(training_data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们有大约25,000张图片。\n",
"
我们要做的一件事是确保我们的数据是平衡的。在这个数据集的情况下，我可以看到数据集开始时是平衡的。平衡，我的意思是每个班级都有相同数量的例子（相同数量的狗和猫）。如果不平衡，您要么将类权重传递给模型，以便它可以适当地测量误差，或者通过将较大的集修剪为与较小集相同的大小来平衡样本。\n",
"
现在数据集中要么全是dog要么全是cat，因此接下来要引入随机："
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random\n",
"\n",
"random.shuffle(training_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们的training_data是一个列表，这意味着它是可变的，所以它现在很好地改组了。我们可以通过迭代几个初始样本并打印出类来确认这一点："
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"0\n",
"1\n",
"1\n",
"0\n",
"1\n",
"0\n",
"1\n",
"0\n"
]
}
],
"source": [
"for sample in training_data[:10]:\n",
" print(sample[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"现在可以看到已经是0、1交替了，我们可以开始我们的模型了："
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[[ 95]\n",
" [ 79]\n",
" [107]\n",
" ..., \n",
" [ 31]\n",
" [ 59]\n",
" [ 75]]\n",
"\n",
" [[ 56]\n",
" [116]\n",
" [104]\n",
" ..., \n",
" [ 84]\n",
" [ 33]\n",
" [ 43]]\n",
"\n",
" [[ 23]\n",
" [ 54]\n",
" [ 45]\n",
" ..., \n",
" [ 97]\n",
" [ 46]\n",
" [ 90]]\n",
"\n",
" ..., \n",
" [[179]\n",
" [101]\n",
" [120]\n",
" ..., \n",
" [166]\n",
" [150]\n",
" [146]]\n",
"\n",
" [[128]\n",
" [103]\n",
" [127]\n",
" ..., \n",
" [153]\n",
" [112]\n",
" [142]]\n",
"\n",
" [[145]\n",
" [117]\n",
" [158]\n",
" ..., \n",
" [125]\n",
" [ 79]\n",
" [145]]]]\n"
]
}
],
"source": [
"X = []\n",
"y = []\n",
"\n",
"for features,label in training_data:\n",
" X.append(features)\n",
" y.append(label)\n",
"\n",
"print(X[0].reshape(-1, IMG_SIZE, IMG_SIZE, 1))\n",
"\n",
"X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"让我们保存这些数据，这样我们就不需要每次想要使用神经网络模型时继续计算它："
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"pickle_out = open(\"../datasets/X.pickle\",\"wb\")\n",
"pickle.dump(X, pickle_out)\n",
"pickle_out.close()\n",
"\n",
"pickle_out = open(\"../datasets/y.pickle\",\"wb\")\n",
"pickle.dump(y, pickle_out)\n",
"pickle_out.close()\n",
"# We can always load it in to our current script, or a totally new one by doing:\n",
"\n",
"pickle_in = open(\"../datasets/X.pickle\",\"rb\")\n",
"X = pickle.load(pickle_in)\n",
"\n",
"pickle_in = open(\"../datasets/y.pickle\",\"rb\")\n",
"y = pickle.load(pickle_in)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"现在我们已经拿出了数据集，我们已经准备好覆盖卷积神经网络，并用我们的数据进行分类。\n",
"
以上就是这次的关于数据集操作的全部任务。"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 41.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 基础知识\n",
"基本的CNN结构如下： Convolution(卷积) -> Pooling(池化) -> Convolution -> Pooling -> Fully Connected Layer(全连接层) -> Output\n",
"\n",
"Convolution（卷积）是获取原始数据并从中创建特征映射的行为。Pooling(池化)是下采样，通常以“max-pooling”的形式，我们选择一个区域，然后在该区域中取最大值，这将成为整个区域的新值。Fully Connected Layers(全连接层)是典型的神经网络，其中所有节点都“完全连接”。卷积层不像传统的神经网络那样完全连接。\n",
"\n",
"卷积：我们将采用某个窗口，并在该窗口中查找要素,该窗口的功能现在只是新功能图中的一个像素大小的功能，但实际上我们将有多层功能图。接下来，我们将该窗口滑过并继续该过程,继续此过程，直到覆盖整个图像。\n",
"\n",
"池化：最常见的池化形式是“最大池化”，其中我们简单地获取窗口中的最大值，并且该值成为该区域的新值。\n",
"\n",
"全连接层：每个卷积和池化步骤都是隐藏层。在此之后，我们有一个完全连接的层，然后是输出层。完全连接的层是典型的神经网络（多层感知器）类型的层，与输出层相同。\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 注意\n",
"本次代码中所需的X.pickle和y.pickle为上一篇的输出，路径请根据自己的情况更改！\n",
"\n",
"此篇中文为译者根据原文整理得到，可能有不当之处，可以点击此处查看原文。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 17462 samples, validate on 7484 samples\n",
"Epoch 1/3\n",
"17462/17462 [==============================] - 47s 3ms/step - loss: 0.6728 - acc: 0.6019 - val_loss: 0.6317 - val_acc: 0.6463\n",
"Epoch 2/3\n",
"17462/17462 [==============================] - 41s 2ms/step - loss: 0.6164 - acc: 0.6673 - val_loss: 0.6117 - val_acc: 0.6776\n",
"Epoch 3/3\n",
"17462/17462 [==============================] - 41s 2ms/step - loss: 0.5690 - acc: 0.7129 - val_loss: 0.5860 - val_acc: 0.6963\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.datasets import cifar10\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
"\n",
"import pickle\n",
"\n",
"pickle_in = open(\"../datasets/X.pickle\",\"rb\")\n",
"X = pickle.load(pickle_in)\n",
"\n",
"pickle_in = open(\"../datasets/y.pickle\",\"rb\")\n",
"y = pickle.load(pickle_in)\n",
"\n",
"X = X/255.0\n",
"\n",
"model = Sequential()\n",
"\n",
"model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))\n",
"model.add(Activation('relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Conv2D(256, (3, 3)))\n",
"model.add(Activation('relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors\n",
"\n",
"model.add(Dense(64))\n",
"\n",
"model.add(Dense(1))\n",
"model.add(Activation('sigmoid'))\n",
"\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(X, y, batch_size=32, epochs=3, validation_split=0.3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"在仅仅三个epoches之后，我们的验证准确率为71％。如果我们继续进行更多的epoches，我们可能会做得更好，但我们应该讨论我们如何知道我们如何做。为了解决这个问题，我们可以使用TensorFlow附带的TensorBoard，它可以帮助您在训练模型时可视化模型。\n",
"\n",
"我们将在下一个教程中讨论TensorBoard以及对我们模型的各种调整！"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 42.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这是Python，TensorFlow和Keras教程系列的深度学习基础知识的第4部分。\n",
"\n",
"在这一部分，我们将讨论的是TensorBoard。TensorBoard是一个方便的应用程序，允许您在浏览器中查看模型或模型的各个方面。我们将TensorBoard与Keras一起使用的方式是通过Keras回调。实际上有很多Keras回调，你可以自己制作。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from tensorflow.keras.callbacks import TensorBoard"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#创建TensorBoard回调对象\n",
"NAME = \"Cats-vs-dogs-CNN\"\n",
"\n",
"tensorboard = TensorBoard(log_dir=\"logs/{}\".format(NAME))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"最终，你会希望获得更多的自定义NAME，但现在这样做。因此，这将保存模型的训练数据logs/NAME，然后由TensorBoard读取。\n",
"\n",
"最后，我们可以通过将它添加到.fit方法中来将此回调添加到我们的模型中，例如：\n",
"```python\n",
"model.fit(X, y,\n",
" batch_size=32,\n",
" epochs=3,\n",
" validation_split=0.3,\n",
" callbacks=[tensorboard])\n",
"```\n",
"请注意，这callbacks是一个列表。您也可以将其他回调传递到此列表中。我们的模型还没有定义，所以现在让我们把它们放在一起："
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 17462 samples, validate on 7484 samples\n",
"Epoch 1/3\n",
"17462/17462 [==============================] - 44s 3ms/step - loss: 0.6992 - acc: 0.5480 - val_loss: 0.6900 - val_acc: 0.5274\n",
"Epoch 2/3\n",
"17462/17462 [==============================] - 41s 2ms/step - loss: 0.6754 - acc: 0.5782 - val_loss: 0.6685 - val_acc: 0.5885\n",
"Epoch 3/3\n",
"17462/17462 [==============================] - 41s 2ms/step - loss: 0.6377 - acc: 0.6483 - val_loss: 0.6217 - val_acc: 0.6625\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.datasets import cifar10\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
"from tensorflow.keras.callbacks import TensorBoard\n",
"# more info on callbakcs: https://keras.io/callbacks/ model saver is cool too.\n",
"import pickle\n",
"import time\n",
"\n",
"NAME = \"Cats-vs-dogs-CNN\"\n",
"\n",
"pickle_in = open(\"../datasets/X.pickle\",\"rb\")\n",
"X = pickle.load(pickle_in)\n",
"\n",
"pickle_in = open(\"../datasets/y.pickle\",\"rb\")\n",
"y = pickle.load(pickle_in)\n",
"\n",
"X = X/255.0\n",
"\n",
"model = Sequential()\n",
"\n",
"model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))\n",
"model.add(Activation('relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Conv2D(256, (3, 3)))\n",
"model.add(Activation('relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors\n",
"model.add(Dense(64))\n",
"\n",
"model.add(Dense(1))\n",
"model.add(Activation('sigmoid'))\n",
"\n",
"tensorboard = TensorBoard(log_dir=\"logs/{}\".format(NAME))\n",
"\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'],\n",
" )\n",
"\n",
"model.fit(X, y,\n",
" batch_size=32,\n",
" epochs=3,\n",
" validation_split=0.3,\n",
" callbacks=[tensorboard])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"运行此之后，您应该有一个名为的新目录logs。我们现在可以使用tensorboard从这个目录中可视化初始结果。打开控制台，切换到工作目录，然后键入：tensorboard --logdir=logs/。您应该看到一个通知：TensorBoard 1.10.0 at http://H-PC:6006 (Press CTRL+C to quit)“h-pc”是您机器的名称。打开浏览器并前往此地址。你应该看到类似的东西：\n",
"![](\"https://pythonprogramming.net/static/images/machine-learning/tensorboard-basic.png\")
\n",
"现在我们可以看到我们的模型随着时间的推移。让我们改变模型中的一些东西。首先，我们从未在密集层中添加激活。另外，让我们尝试整体较小的模型："
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 17462 samples, validate on 7484 samples\n",
"Epoch 1/10\n",
"17462/17462 [==============================] - 11s 604us/step - loss: 0.6033 - acc: 0.6652 - val_loss: 0.5298 - val_acc: 0.7320\n",
"Epoch 2/10\n",
"17462/17462 [==============================] - 11s 646us/step - loss: 0.4859 - acc: 0.7659 - val_loss: 0.4723 - val_acc: 0.7763\n",
"Epoch 3/10\n",
"17462/17462 [==============================] - 11s 641us/step - loss: 0.4270 - acc: 0.8045 - val_loss: 0.4603 - val_acc: 0.7803\n",
"Epoch 4/10\n",
"17462/17462 [==============================] - 12s 699us/step - loss: 0.3675 - acc: 0.8347 - val_loss: 0.4476 - val_acc: 0.7929\n",
"Epoch 5/10\n",
"17462/17462 [==============================] - 12s 707us/step - loss: 0.3012 - acc: 0.8694 - val_loss: 0.4854 - val_acc: 0.7797\n",
"Epoch 6/10\n",
"17462/17462 [==============================] - 12s 705us/step - loss: 0.2165 - acc: 0.9118 - val_loss: 0.5450 - val_acc: 0.7865\n",
"Epoch 7/10\n",
"17462/17462 [==============================] - 12s 712us/step - loss: 0.1332 - acc: 0.9510 - val_loss: 0.6512 - val_acc: 0.7821\n",
"Epoch 8/10\n",
"17462/17462 [==============================] - 12s 705us/step - loss: 0.0764 - acc: 0.9743 - val_loss: 0.7487 - val_acc: 0.7809\n",
"Epoch 9/10\n",
"17462/17462 [==============================] - 12s 713us/step - loss: 0.0389 - acc: 0.9887 - val_loss: 0.9041 - val_acc: 0.7743\n",
"Epoch 10/10\n",
"17462/17462 [==============================] - 12s 708us/step - loss: 0.0287 - acc: 0.9921 - val_loss: 1.0411 - val_acc: 0.7702\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
"from tensorflow.keras.callbacks import TensorBoard\n",
"# more info on callbakcs: https://keras.io/callbacks/ model saver is cool too.\n",
"import pickle\n",
"import time\n",
"\n",
"NAME = \"Cats-vs-dogs-64x2-CNN\"\n",
"\n",
"pickle_in = open(\"../datasets/X.pickle\",\"rb\")\n",
"X = pickle.load(pickle_in)\n",
"\n",
"pickle_in = open(\"../datasets/y.pickle\",\"rb\")\n",
"y = pickle.load(pickle_in)\n",
"\n",
"X = X/255.0\n",
"\n",
"model = Sequential()\n",
"\n",
"model.add(Conv2D(64, (3, 3), input_shape=X.shape[1:]))\n",
"model.add(Activation('relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Conv2D(64, (3, 3)))\n",
"model.add(Activation('relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors\n",
"model.add(Dense(64))\n",
"model.add(Activation('relu'))\n",
"\n",
"model.add(Dense(1))\n",
"model.add(Activation('sigmoid'))\n",
"\n",
"tensorboard = TensorBoard(log_dir=\"logs/{}\".format(NAME))\n",
"\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'],\n",
" )\n",
"\n",
"model.fit(X, y,\n",
" batch_size=32,\n",
" epochs=10,\n",
" validation_split=0.3,\n",
" callbacks=[tensorboard])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"除此之外，我还改名为NAME = \"Cats-vs-dogs-64x2-CNN\"。不要忘记这样做，否则你会偶然附加到你以前的型号的日志，它看起来不太好。我们现在检查TensorBoard：\n",
"![](\"https://pythonprogramming.net/static/images/machine-learning/second-model-tensorboard.png\")
\n",
"看起来更好！但是，您可能会立即注意到验证丢失的形状。损失是衡量错误的标准，看起来很明显，在我们的第四个时代之后，事情开始变得糟糕。\n",
"\n",
"有趣的是，我们的验证准确性仍然持续，但我想它最终会开始下降。更可能的是，第一件遭受的事情确实是你的验证损失。这应该提醒你，你几乎肯定会开始过度适应。这种情况发生的原因是该模型不断尝试减少样本损失。\n",
"\n",
"在某些时候，模型不是学习关于实际数据的一般事物，而是开始只记忆输入数据。如果你继续这样做，是的，样本中的“准确性”会上升，但你的样本，以及你试图为模型提供的任何新数据可能会表现得很差。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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================================================
FILE: Code/Day 6_Logistic_Regression.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 机器学习100天——第6天：逻辑回归（Linear Regression）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第1步：数据预处理\n",
"### 导入库"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 导入数据集\n",
"这里获取数据集"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
"X = dataset.iloc[:, [2, 3]].values\n",
"Y = dataset.iloc[:,4].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 将数据集分成训练集和测试集"
]
},
{
"cell_type": "code",
"execution_count":2,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 特征缩放"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\python\\python36\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第二步：逻辑回归模型\n",
"该项工作的库将会是一个线性模型库，之所以被称为线性是因为逻辑回归是一个线性分类器，这意味着我们在二维空间中，我们两类用户（购买和不购买）将被一条直线分割。然后导入逻辑回归类。下一步我们将创建该类的对象，它将作为我们训练集的分类器。\n",
"### 将逻辑回归应用于训练集"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"classifier = LogisticRegression()\n",
"classifier.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第3步：预测\n",
"### 预测测试集结果"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"y_pred = classifier.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第4步：评估预测\n",
"我们预测了测试集。 现在我们将评估逻辑回归模型是否正确的学习和理解。因此这个混淆矩阵将包含我们模型的正确和错误的预测。\n",
"### 生成混淆矩阵"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 可视化"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set,y_set=X_train,y_train\n",
"X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),\n",
" np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(),X1.max())\n",
"plt.ylim(X2.min(),X2.max())\n",
"for i,j in enumerate(np. unique(y_set)):\n",
" plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],\n",
" c = ListedColormap(('red', 'green'))(i), label=j)\n",
"\n",
"plt. title(' LOGISTIC(Training set)')\n",
"plt. xlabel(' Age')\n",
"plt. ylabel(' Estimated Salary')\n",
"plt. legend()\n",
"plt. show()\n",
"\n",
"X_set,y_set=X_test,y_test\n",
"X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),\n",
" np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))\n",
"\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(),X1.max())\n",
"plt.ylim(X2.min(),X2.max())\n",
"for i,j in enumerate(np. unique(y_set)):\n",
" plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],\n",
" c = ListedColormap(('red', 'green'))(i), label=j)\n",
"\n",
"plt. title(' LOGISTIC(Test set)')\n",
"plt. xlabel(' Age')\n",
"plt. ylabel(' Estimated Salary')\n",
"plt. legend()\n",
"plt. show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 6_Logistic_Regression.md
================================================
### 数据集 | 社交网络
该数据集包含了社交网络中用户的信息。这些信息涉及用户ID,性别,年龄以及预估薪资。一家汽车公司刚刚推出了他们新型的豪华SUV，我们尝试预测哪些用户会购买这种全新SUV。并且在最后一列用来表示用户是否购买。我们将建立一种模型来预测用户是否购买这种SUV，该模型基于两个变量，分别是年龄和预计薪资。因此我们的特征矩阵将是这两列。我们尝试寻找用户年龄与预估薪资之间的某种相关性，以及他是否购买SUV的决定。
### 步骤1 | 数据预处理
#### 导入库
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
#### 导入数据集
[这里](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/datasets/Social_Network_Ads.csv)获取数据集
```python
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
Y = dataset.iloc[:,4].values
```
#### 将数据集分成训练集和测试集
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)
```
#### 特征缩放
```python
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
```
### 步骤2 | 逻辑回归模型
该项工作的库将会是一个线性模型库，之所以被称为线性是因为逻辑回归是一个线性分类器，这意味着我们在二维空间中，我们两类用户（购买和不购买）将被一条直线分割。然后导入逻辑回归类。下一步我们将创建该类的对象，它将作为我们训练集的分类器。
#### 将逻辑回归应用于训练集
```python
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)
```
### 步骤3 | 预测
#### 预测测试集结果
```python
y_pred = classifier.predict(X_test)
```
### 步骤4 | 评估预测
我们预测了测试集。 现在我们将评估逻辑回归模型是否正确的学习和理解。因此这个混淆矩阵将包含我们模型的正确和错误的预测。
#### 生成混淆矩阵
```python
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
```
#### 可视化
```python
from matplotlib.colors import ListedColormap
X_set,y_set=X_train,y_train
X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np. unique(y_set)):
plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
c = ListedColormap(('red', 'green'))(i), label=j)
plt. title(' LOGISTIC(Training set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()
X_set,y_set=X_test,y_test
X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np. unique(y_set)):
plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
c = ListedColormap(('red', 'green'))(i), label=j)
plt. title(' LOGISTIC(Test set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()
```
================================================
FILE: Code/Day 6_Logistic_Regression.py
================================================
# Importing the Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting Logistic Regression to the Training set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm) # print confusion_matrix
print(classification_report(y_test, y_pred)) # print classification report
#Visualization
from matplotlib.colors import ListedColormap
X_set,y_set=X_train,y_train
X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np. unique(y_set)):
plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
c = ListedColormap(('red', 'green'))(i), label=j)
plt. title(' LOGISTIC(Training set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()
X_set,y_set=X_test,y_test
X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np. unique(y_set)):
plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
c = ListedColormap(('red', 'green'))(i), label=j)
plt. title(' LOGISTIC(Test set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()
================================================
FILE: Code/KafkaProducer.py
================================================
#!/usr/bin/python
from kafka import KafkaProducer
kafkaHosts=["kafka01.paas.longfor.sit:9092"
,"kafka02.paas.longfor.sit:9092"
,"kafka03.paas.longfor.sit:9092"]
producer = KafkaProducer(bootstrap_servers=kafkaHosts);
for _ in range(20):
producer.send("testapplog_plm-prototype",b"Hello....")
producer.flush();
================================================
FILE: Code/TestKafka.py
================================================
#!/usr/bin/python
from kafka import KafkaConsumer;
kafkaHosts=["kafka01.paas.longfor.sit:9092"
,"kafka02.paas.longfor.sit:9092"
,"kafka03.paas.longfor.sit:9092"]
'''
earliest
当各分区下有已提交的offset时，从提交的offset开始消费；无提交的offset时，从头开始消费
latest
当各分区下有已提交的offset时，从提交的offset开始消费；无提交的offset时，消费新产生的该分区下的数据
none
topic各分区都存在已提交的offset时，从offset后开始消费；只要有一个分区不存在已提交的offset，则抛出异常
'''
consumer = KafkaConsumer(
bootstrap_servers=kafkaHosts,group_id='mdf_group',auto_offset_reset='latest');
consumer.subscribe("testapplog_plm-prototype");
for msg in consumer:
print(msg.value)
================================================
FILE: Code/my/Data_age_salary.csv
================================================
Age,Salary
44,72000
27,48000
30,54000
38,61000
40,78000
35,58000
35,52000
48,79000
50,83000
37,67000
================================================
FILE: Code/my/LinerTest.py
================================================
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dataset = pd.read_csv('Data_age_salary.csv');
dataset.iloc[:1]
================================================
FILE: FAQ.MD
================================================
# 常见问题解答
欢迎到Issues提问
### 1. “拆分数据”和“特征缩放”的顺序
可以先“拆分数据”，再“特征缩放”，但需要使用训练集的fit参数，去transform测试集，以保证参数相同。
详见[issue#41](https://github.com/MachineLearning100/100-Days-Of-ML-Code/issues/41)。
### 2. 3Blue1Brown视频
原作中提到的YouTube视频，在B站有[官方中文版](https://space.bilibili.com/88461692/#/)，README的链接也是到B站。
感谢网友在[issue#45](https://github.com/MachineLearning100/100-Days-Of-ML-Code/issues/45)的反馈。
### 3. Deep Learning basics with Python, TensorFlow and Keras
[文字版](https://pythonprogramming.net/introduction-deep-learning-python-tensorflow-keras/)的中文版见第39天到第42天。
详见[issue#52](https://github.com/MachineLearning100/100-Days-Of-ML-Code/issues/52)。
### 4.《Python数据科学手册》
**[高清中文版pdf](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Other%20Docs/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E6%89%8B%E5%86%8C.zip)**，[Jupyter notebooks](https://github.com/jakevdp/PythonDataScienceHandbook)。
仅作为个人学习，不能用于商业用途。
### 5. 微信群
见[issue#59](https://github.com/MachineLearning100/100-Days-Of-ML-Code/issues/59)
### 6. 常用工具推荐
见[issue#60](https://github.com/MLEveryday/100-Days-Of-ML-Code/issues/60)
### 7. sklearn版本
sklearn工具包0.19和0.20版本，cross_validation在0.20版本是将被移除的并转移到model_selection包下。要排除这些问题，注意平时运行时的出现warning即可。
见[issue63](https://github.com/MLEveryday/100-Days-Of-ML-Code/issues/63)。
### 8. Python版本
建议使用Python3。使用Python2.7运行示例代码时可能有问题，例如：线性回归如下代码中“1/4”，在Python2.7中1/4=0，可以改成“1.0/4”或者“0.25”。
```python
X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0)
```
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2018 MachineLearning100
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: Other Docs/README.md
================================================
演示图片
================================================
FILE: Other Docs/速查手册/README.md
================================================
# 14张速查表，带你玩转 Python 数据科学
译自 DataCamp 的速查表，有兴趣的朋友可以在这里查看[英文原版](https://www.datacamp.com/community/data-science-cheatsheets)。
欢迎扫描二维码关注我的 **呆鸟的Python数据分析** 公众号，虽然现在内容还比较少，但我会不断增加的。
一、[Python 基础系列](https://www.jianshu.com/p/4574d95755db)
* [Python数据科学速查表 - Python 基础](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Python%20基础.pdf)
* [Python数据科学速查表 - 导入数据](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20导入数据.pdf)
* [Python数据科学速查表 - Jupyter Notebook](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20Jupyter%20Notebook.pdf)
二、[数据处理系列（Numpy、Pandas 及 SciPy）](https://www.jianshu.com/p/8d51642dfa26)
* [Python数据科学速查表 - Numpy 基础](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Numpy%20基础.pdf)
* [Python数据科学速查表 - Pandas 基础](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Pandas%20基础.pdf)
* [Python数据科学速查表 - Pandas 进阶](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Pandas%20进阶.pdf)
* [Python数据科学速查表 - SciPy](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20SciPy.pdf)
三、[可视化系列（Matplotlib、Bokeh、Seaborn）](https://www.jianshu.com/p/7e186d43d7f1)
* [Python数据科学速查表 - Matplotlib](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Matplotlib%20绘图.pdf)
* [Python数据科学速查表 - Bokeh](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Bokeh.pdf)
* [Python数据科学速查表 - Seaborn](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20Seaborn.pdf)
四、[机器学习系列（Keras、Scikit-learn）](https://www.jianshu.com/p/cba49ff5fc97)
* [Python数据科学速查表 - Keras](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Keras.pdf)
* [Python数据科学速查表 - Scikit-learn](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20Scikit-Learn.pdf)
五、[PySpark系列（SQL与RDD）](https://www.jianshu.com/p/7dea578c56d8)
* [Python数据科学速查表 - Spark SQL 基础](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20Spark%20SQL%20%E5%9F%BA%E7%A1%80.pdf)
* [Python数据科学速查表 - Spark RDD 基础](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20Spark%20RDD%20%E5%9F%BA%E7%A1%80.pdf)
如果喜欢本文，敬请关注我的简书专题 **[呆鸟的Python数据分析](https://www.jianshu.com/c/38980843c0f2)**
感谢**天善智能**的**Python爱好者社区**公众号一直以来对我的支持，这里也大力推荐，是我学习入门 Python 数据分析入门的引路者，欢迎关注！
================================================
FILE: README.md
================================================
# 机器学习100天
英文原版请移步[Avik-Jain](https://github.com/Avik-Jain/100-Days-Of-ML-Code)。数据在[这里](https://github.com/MachineLearning100/100-Days-Of-ML-Code/tree/master/datasets)。
翻译前请先阅读[规范](Translation%20specification.MD)。常见问题解答见[FAQ](FAQ.MD)。
# 目录
- 有监督学习
- [数据预处理](#数据预处理--第1天)
- [简单线性回归](#简单线性回归--第2天)
- [多元线性回归](#多元线性回归--第3天)
- [逻辑回归](#逻辑回归--第4天)
- [k近邻法(k-NN)](#k近邻法k-nn--第7天)
- [支持向量机(SVM)](#支持向量机svm--第12天)
- [决策树](#决策树--第23天)
- [随机森林](#随机森林--第33天)
- 无监督学习
- [K-均值聚类](#k-均值聚类--第43天)
- [层次聚类](#层次聚类--第54天)
## 数据预处理 | 第1天
[数据预处理实现](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%201_Data_Preprocessing.md)
## 简单线性回归 | 第2天
[简单线性回归实现](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%202_Simple_Linear_Regression.md)
## 多元线性回归 | 第3天
[多元线性回归实现](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%203_Multiple_Linear_Regression.md)
## 逻辑回归 | 第4天
## 逻辑回归 | 第5天
今天我深入研究了逻辑回归到底是什么，以及它背后的数学是什么。学习了如何计算代价函数，以及如何使用梯度下降法来将代价函数降低到最小。
由于时间关系，我将隔天发布信息图。如果有人在机器学习领域有一定经验，并愿意帮我编写代码文档，也了解github的Markdown语法，请在领英联系我。
## 逻辑回归 | 第6天
[逻辑回归实现](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%206_Logistic_Regression.md)
## K近邻法(k-NN) | 第7天
## 逻辑回归背后的数学 | 第8天
为了使我对逻辑回归的见解更加清晰，我在网上搜索了一些资源或文章，然后我就发现了Saishruthi Swaminathan的这篇文章
它给出了逻辑回归的详细描述。请务必看一看。
## 支持向量机(SVM) | 第9天
直观了解SVM是什么以及如何使用它来解决分类问题。
## 支持向量机和K近邻法 | 第10天
了解更多关于SVM如何工作和实现knn算法的知识。
## K近邻法(k-NN) | 第11天
[K近邻法(k-NN)实现](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2011_K-NN.md)
## 支持向量机(SVM) | 第12天
## 支持向量机(SVM) | 第13天
[SVM实现](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2013_SVM.md)
## 支持向量机(SVM)的实现 | 第14天
今天我在线性相关数据上实现了SVM。使用Scikit-Learn库。在scikit-learn中我们有SVC分类器，我们用它来完成这个任务。将在下一次实现时使用kernel-trick。Python代码见[此处](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2013_SVM.py),Jupyter notebook见[此处](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2013_SVM.ipynb)。
## 朴素贝叶斯分类器(Naive Bayes Classifier)和黑盒机器学习(Black Box Machine Learning) | 第15天
学习不同类型的朴素贝叶斯分类器同时开始Bloomberg的课程。课程列表中的第一个是黑盒机器学习。它给出了预测函数，特征提取，学习算法，性能评估，交叉验证，样本偏差，非平稳性，过度拟合和超参数调整的整体观点。
## 通过内核技巧实现支持向量机 | 第16天
使用Scikit-Learn库实现了SVM算法以及内核函数，该函数将我们的数据点映射到更高维度以找到最佳超平面。
## 在Coursera开始深度学习的专业课程 | 第17天
在1天内完成第1周和第2周内容以及学习课程中的逻辑回归神经网络。
## 继续Coursera上的深度学习专业课程 | 第18天
完成课程1。用Python自己实现一个神经网络。
## 学习问题和Yaser Abu-Mostafa教授 | 第19天
开始Yaser Abu-Mostafa教授的Caltech机器学习课程-CS156中的课程1。这基本上是对即将到来的课程的一种介绍。他也介绍了感知算法。
## 深度学习专业课程2 | 第20天
完成改进深度神经网络第1周内容：参数调整，正则化和优化。
## 网页搜罗 | 第21天
观看了一些关于如何使用Beautiful Soup进行网络爬虫的教程，以便收集用于构建模型的数据。
## 学习还可行吗? | 第22天
完成Yaser Abu-Mostafa教授的Caltech机器学习课程-CS156中的课程2。学习Hoeffding不等式。
## 决策树 | 第23天
## 统计学习理论的介绍 | 第24天
Bloomberg ML课程的第3课介绍了一些核心概念，如输入空间，动作空间，结果空间，预测函数，损失函数和假设空间。
## 决策树 | 第25天
[决策树实现](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2025_Decision_Tree.md)
## 跳到复习线性代数 | 第26天
发现YouTube一个神奇的频道[3Blue1Brown](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw)，它有一个播放列表《线性代数的本质》。看完了4个视频，包括了向量，线性组合，跨度，基向量，线性变换和矩阵乘法。
B站播放列表在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=9450)。
## 跳到复习线性代数 | 第27天
继续观看了4个视频，内容包括三维变换、行列式、逆矩阵、列空间、零空间和非方矩阵。
B站播放列表在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=9450)。
## 跳到复习线性代数 | 第28天
继续观看了3个视频，内容包括点积和叉积。
B站播放列表在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=9450)。
## 跳到复习线性代数 | 第29天
观看了剩余的视频12到14，内容包括特征向量和特征值，以及抽象向量空间。
B站播放列表在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=9450)。
## 微积分的本质 | 第30天
完成上一播放列表后，YouTube推荐了新内容《微积分的本质》，今天看完了其中的3个视频，包括导数、链式法则、乘积法则和指数导数。
B站播放列表在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=13407)。
## 微积分的本质 | 第31天
观看了2个视频，内容包括隐分化与极限。
B站播放列表在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=13407)。
## 微积分的本质 | 第32天
观看了剩余的4个视频，内容包括积分与高阶导数。
B站播放列表在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=13407)。
## 随机森林 | 第33天
## 随机森林 | 第34天
[随机森林实现](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2034_Random_Forests.md)
## 什么是神经网络？ | 深度学习，第1章 | 第 35天
Youtube频道3Blue1Brown中有精彩的视频介绍神经网络。这个视频提供了很好的解释，并使用手写数字数据集演示基本概念。
B站视频在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=26587)。
## 梯度下降法，神经网络如何学习 | 深度学习，第2章 | 第36天
Youtube频道3Blue1Brown关于神经网络的第2部分，这个视频用有趣的方式解释了梯度下降法。推荐必须观看169.
B站视频在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=26587)。
## 反向传播法究竟做什么？ | 深度学习，第3章 | 第37天
Youtube频道3Blue1Brown关于神经网络的第3部分，这个视频主要介绍了偏导数和反向传播法。
B站视频在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=26587)。
## 反向传播法演算 | 深度学习，第4章 | 第38天
Youtube频道3Blue1Brown关于神经网络的第3部分，这个视频主要介绍了偏导数和反向传播法。
B站视频在[这里](https://space.bilibili.com/88461692/#/channel/detail?cid=26587)。
## 第1部分 | 深度学习基础Python，TensorFlow和Keras | 第39天
视频地址在[这里](https://www.youtube.com/watch?v=wQ8BIBpya2k&t=19s&index=2&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN)。
中文文字版[notebook](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2039.ipynb)。
## 第2部分 | 深度学习基础Python，TensorFlow和Keras | 第40天
视频地址在[这里](https://www.youtube.com/watch?v=wQ8BIBpya2k&t=19s&index=2&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN)。
中文文字版[notebook](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2040.ipynb)。
## 第3部分 | 深度学习基础Python，TensorFlow和Keras | 第41天
视频地址在[这里](https://www.youtube.com/watch?v=wQ8BIBpya2k&t=19s&index=2&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN)。
中文文字版[notebook](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2041.ipynb)。
## 第4部分 | 深度学习基础Python，TensorFlow和Keras | 第42天
视频地址在[这里](https://www.youtube.com/watch?v=wQ8BIBpya2k&t=19s&index=2&list=PLQVvvaa0QuDfhTox0AjmQ6tvTgMBZBEXN)。
中文文字版[notebook](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%2042.ipynb)。
## K-均值聚类 | 第43天
转到无监督学习，并研究了聚类。可在[作者网站](http://www.avikjain.me/)查询。发现一个奇妙的[动画](http://shabal.in/visuals/kmeans/6.html)有助于理解K-均值聚类。
## K-均值聚类 | 第44天
实现（待添加代码）
## 深入研究 | NUMPY | 第45天
得到JK VanderPlas写的书《Python数据科学手册（Python Data Science HandBook）》，Jupyter notebooks在[这里](https://github.com/jakevdp/PythonDataScienceHandbook)。
**[高清中文版pdf](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Other%20Docs/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E6%89%8B%E5%86%8C.zip)**
第2章：NumPy介绍，包括数据类型、数组和数组计算。
代码如下：
[2 NumPy入门](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.00-Introduction-to-NumPy.ipynb)
[2.1 理解Python中的数据类型](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.01-Understanding-Data-Types.ipynb)
[2.2 NumPy数组基础](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb)
[2.3 NumPy数组的计算：通用函数](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb)
## 深入研究 | NUMPY | 第46天
第2章： 聚合, 比较运算符和广播。
代码如下：
[2.4 聚合：最小值、最大值和其他值](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.04-Computation-on-arrays-aggregates.ipynb)
[2.5 数组的计算：广播](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb)
[2.6 比较、掩码和布尔运算](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb)
## 深入研究 | NUMPY | 第47天
第2章： 花哨的索引，数组排序，结构化数据。
代码如下：
[2.7 花哨的索引](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.07-Fancy-Indexing.ipynb)
[2.8 数组的排序](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.08-Sorting.ipynb)
[2.9 结构化数据：NumPy的结构化数组](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.09-
Structured-Data-NumPy.ipynb)
## 深入研究 | PANDAS | 第48天
第3章：Pandas数据处理
包含Pandas对象，数据取值与选择，数值运算方法，处理缺失值，层级索引，合并数据集。
代码如下：
[3 Pandas数据处理](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.00-Introduction-to-Pandas.ipynb)
[3.1 Pandas对象简介](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.01-Introducing-Pandas-Objects.ipynb)
[3.2 数据取值与选择](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.02-Data-Indexing-and-Selection.ipynb)
[3.3 Pandas数值运算方法](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.03-Operations-in-Pandas.ipynb)
[3.4 处理缺失值](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.04-Missing-Values.ipynb)
[3.5 层级索引](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.05-Hierarchical-Indexing.ipynb)
[3.6 合并数据集：ConCat和Append方法](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.06-Concat-And-Append.ipynb)
## 深入研究 | PANDAS | 第49天
第3章：完成剩余内容-合并与连接，累计与分组，数据透视表。
代码如下：
[3.7 合并数据集：合并与连接](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.07-Merge-and-Join.ipynb)
[3.8 累计与分组](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.08-Aggregation-and-Grouping.ipynb)
[3.9 数据透视表](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.09-Pivot-Tables.ipynb)
## 深入研究 | PANDAS | 第50天
第3章：向量化字符串操作，处理时间序列。
代码如下：
[3.10 向量化字符串操作](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.10-Working-With-Strings.ipynb)
[3.11 处理时间序列](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.11-Working-with-Time-Series.ipynb)
[3.12 高性能Pandas：eval()与query()](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.12-Performance-Eval-and-Query.ipynb)
## 深入研究 | MATPLOTLIB | 第51天
第4章：Matplotlib数据可视化
学习简易线形图, 简易散点图，密度图与等高线图.
代码如下：
[4 Matplotlib数据可视化](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb)
[4.1 简易线形图](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.01-Simple-Line-Plots.ipynb)
[4.2 简易散点图](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.02-Simple-Scatter-Plots.ipynb)
[4.3 可视化异常处理](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.03-Errorbars.ipynb)
[4.4 密度图与等高线图](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.04-Density-and-Contour-Plots.ipynb)
## 深入研究 | MATPLOTLIB | 第52天
第4章：Matplotlib数据可视化
学习直方图，配置图例，配置颜色条，多子图。
代码如下：
[4.5 直方图](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.05-Histograms-and-Binnings.ipynb)
[4.6 配置图例](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.06-Customizing-Legends.ipynb)
[4.7 配置颜色条](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.07-Customizing-Colorbars.ipynb)
[4.8 多子图](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.08-Multiple-Subplots.ipynb)
[4.9 文字与注释](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.09-Text-and-Annotation.ipynb)
## 深入研究 | MATPLOTLIB | 第53天
第4章：Matplotlib数据可视化
学习三维绘图。
[4.12 画三维图](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.12-Three-Dimensional-Plotting.ipynb)
## 层次聚类 | 第54天
[动画演示](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Other%20Docs/%E5%B1%82%E6%AC%A1%E8%81%9A%E7%B1%BB.gif)
================================================
FILE: Translation specification.MD
================================================
## 翻译规范`v20180809`
### 交付物
>- 图片（.jpg），质量选择最高，源文件自己保留好，文件命名`Day xxx.jpg`
>- 代码，含md、py、notebook等，要求调试通过，数据引用datasets文件夹`../datasets/xxxx.csv`，文件命名`Day xxx_xxxx_xxxx.md`
### 忽略区域
>- 页眉（`#100DaysOfMLCode`+`Day XXX`+`@Avik Jain`）
>- 页脚（`github`+`Follow`）
>- 图表坐标轴
>- 代码，如库、类、方法名称等
### 强制区域
>- 图表说明文字
### 格式
>- 字体颜色、间距等格式和原文保持一致。
### 数字
>- 需要用到数字编号的，一律使用阿拉伯数字`1234567890`，如：`步骤1`，`步骤2`等
>- 文字内容中，从中文习惯出发，使用`二维`、`三维`、`两个`等
### 符号
>- 编号等使用英文符号，如`1.`，`2.`等
>- 除特殊要求外，其他都用中文符号，如`，`，`。`，`：`，`？`等
### 保留英文场景
>- 标题或正文中，如果英文认知度更高，更准确，则翻译成`中(英)`。注意使用了**英文括号**。
### 词典
按字母升序排列。
|English|中文|
|---|---|
|dependent variable|因变量|
|independent variable|自变量|
================================================
FILE: datasets/50_Startups.csv
================================================
R&D Spend,Administration,Marketing Spend,State,Profit
165349.2,136897.8,471784.1,New York,192261.83
162597.7,151377.59,443898.53,California,191792.06
153441.51,101145.55,407934.54,Florida,191050.39
144372.41,118671.85,383199.62,New York,182901.99
142107.34,91391.77,366168.42,Florida,166187.94
131876.9,99814.71,362861.36,New York,156991.12
134615.46,147198.87,127716.82,California,156122.51
130298.13,145530.06,323876.68,Florida,155752.6
120542.52,148718.95,311613.29,New York,152211.77
123334.88,108679.17,304981.62,California,149759.96
101913.08,110594.11,229160.95,Florida,146121.95
100671.96,91790.61,249744.55,California,144259.4
93863.75,127320.38,249839.44,Florida,141585.52
91992.39,135495.07,252664.93,California,134307.35
119943.24,156547.42,256512.92,Florida,132602.65
114523.61,122616.84,261776.23,New York,129917.04
78013.11,121597.55,264346.06,California,126992.93
94657.16,145077.58,282574.31,New York,125370.37
91749.16,114175.79,294919.57,Florida,124266.9
86419.7,153514.11,0,New York,122776.86
76253.86,113867.3,298664.47,California,118474.03
78389.47,153773.43,299737.29,New York,111313.02
73994.56,122782.75,303319.26,Florida,110352.25
67532.53,105751.03,304768.73,Florida,108733.99
77044.01,99281.34,140574.81,New York,108552.04
64664.71,139553.16,137962.62,California,107404.34
75328.87,144135.98,134050.07,Florida,105733.54
72107.6,127864.55,353183.81,New York,105008.31
66051.52,182645.56,118148.2,Florida,103282.38
65605.48,153032.06,107138.38,New York,101004.64
61994.48,115641.28,91131.24,Florida,99937.59
61136.38,152701.92,88218.23,New York,97483.56
63408.86,129219.61,46085.25,California,97427.84
55493.95,103057.49,214634.81,Florida,96778.92
46426.07,157693.92,210797.67,California,96712.8
46014.02,85047.44,205517.64,New York,96479.51
28663.76,127056.21,201126.82,Florida,90708.19
44069.95,51283.14,197029.42,California,89949.14
20229.59,65947.93,185265.1,New York,81229.06
38558.51,82982.09,174999.3,California,81005.76
28754.33,118546.05,172795.67,California,78239.91
27892.92,84710.77,164470.71,Florida,77798.83
23640.93,96189.63,148001.11,California,71498.49
15505.73,127382.3,35534.17,New York,69758.98
22177.74,154806.14,28334.72,California,65200.33
1000.23,124153.04,1903.93,New York,64926.08
1315.46,115816.21,297114.46,Florida,49490.75
0,135426.92,0,California,42559.73
542.05,51743.15,0,New York,35673.41
0,116983.8,45173.06,California,14681.4
================================================
FILE: datasets/Data.csv
================================================
Country,Age,Salary,Purchased
France,44,72000,No
Spain,27,48000,Yes
Germany,30,54000,No
Spain,38,61000,No
Germany,40,,Yes
France,35,58000,Yes
Spain,,52000,No
France,48,79000,Yes
Germany,50,83000,No
France,37,67000,Yes
================================================
FILE: datasets/Social_Network_Ads.csv
================================================
User ID,Gender,Age,EstimatedSalary,Purchased
15624510,Male,19,19000,0
15810944,Male,35,20000,0
15668575,Female,26,43000,0
15603246,Female,27,57000,0
15804002,Male,19,76000,0
15728773,Male,27,58000,0
15598044,Female,27,84000,0
15694829,Female,32,150000,1
15600575,Male,25,33000,0
15727311,Female,35,65000,0
15570769,Female,26,80000,0
15606274,Female,26,52000,0
15746139,Male,20,86000,0
15704987,Male,32,18000,0
15628972,Male,18,82000,0
15697686,Male,29,80000,0
15733883,Male,47,25000,1
15617482,Male,45,26000,1
15704583,Male,46,28000,1
15621083,Female,48,29000,1
15649487,Male,45,22000,1
15736760,Female,47,49000,1
15714658,Male,48,41000,1
15599081,Female,45,22000,1
15705113,Male,46,23000,1
15631159,Male,47,20000,1
15792818,Male,49,28000,1
15633531,Female,47,30000,1
15744529,Male,29,43000,0
15669656,Male,31,18000,0
15581198,Male,31,74000,0
15729054,Female,27,137000,1
15573452,Female,21,16000,0
15776733,Female,28,44000,0
15724858,Male,27,90000,0
15713144,Male,35,27000,0
15690188,Female,33,28000,0
15689425,Male,30,49000,0
15671766,Female,26,72000,0
15782806,Female,27,31000,0
15764419,Female,27,17000,0
15591915,Female,33,51000,0
15772798,Male,35,108000,0
15792008,Male,30,15000,0
15715541,Female,28,84000,0
15639277,Male,23,20000,0
15798850,Male,25,79000,0
15776348,Female,27,54000,0
15727696,Male,30,135000,1
15793813,Female,31,89000,0
15694395,Female,24,32000,0
15764195,Female,18,44000,0
15744919,Female,29,83000,0
15671655,Female,35,23000,0
15654901,Female,27,58000,0
15649136,Female,24,55000,0
15775562,Female,23,48000,0
15807481,Male,28,79000,0
15642885,Male,22,18000,0
15789109,Female,32,117000,0
15814004,Male,27,20000,0
15673619,Male,25,87000,0
15595135,Female,23,66000,0
15583681,Male,32,120000,1
15605000,Female,59,83000,0
15718071,Male,24,58000,0
15679760,Male,24,19000,0
15654574,Female,23,82000,0
15577178,Female,22,63000,0
15595324,Female,31,68000,0
15756932,Male,25,80000,0
15726358,Female,24,27000,0
15595228,Female,20,23000,0
15782530,Female,33,113000,0
15592877,Male,32,18000,0
15651983,Male,34,112000,1
15746737,Male,18,52000,0
15774179,Female,22,27000,0
15667265,Female,28,87000,0
15655123,Female,26,17000,0
15595917,Male,30,80000,0
15668385,Male,39,42000,0
15709476,Male,20,49000,0
15711218,Male,35,88000,0
15798659,Female,30,62000,0
15663939,Female,31,118000,1
15694946,Male,24,55000,0
15631912,Female,28,85000,0
15768816,Male,26,81000,0
15682268,Male,35,50000,0
15684801,Male,22,81000,0
15636428,Female,30,116000,0
15809823,Male,26,15000,0
15699284,Female,29,28000,0
15786993,Female,29,83000,0
15709441,Female,35,44000,0
15710257,Female,35,25000,0
15582492,Male,28,123000,1
15575694,Male,35,73000,0
15756820,Female,28,37000,0
15766289,Male,27,88000,0
15593014,Male,28,59000,0
15584545,Female,32,86000,0
15675949,Female,33,149000,1
15672091,Female,19,21000,0
15801658,Male,21,72000,0
15706185,Female,26,35000,0
15789863,Male,27,89000,0
15720943,Male,26,86000,0
15697997,Female,38,80000,0
15665416,Female,39,71000,0
15660200,Female,37,71000,0
15619653,Male,38,61000,0
15773447,Male,37,55000,0
15739160,Male,42,80000,0
15689237,Male,40,57000,0
15679297,Male,35,75000,0
15591433,Male,36,52000,0
15642725,Male,40,59000,0
15701962,Male,41,59000,0
15811613,Female,36,75000,0
15741049,Male,37,72000,0
15724423,Female,40,75000,0
15574305,Male,35,53000,0
15678168,Female,41,51000,0
15697020,Female,39,61000,0
15610801,Male,42,65000,0
15745232,Male,26,32000,0
15722758,Male,30,17000,0
15792102,Female,26,84000,0
15675185,Male,31,58000,0
15801247,Male,33,31000,0
15725660,Male,30,87000,0
15638963,Female,21,68000,0
15800061,Female,28,55000,0
15578006,Male,23,63000,0
15668504,Female,20,82000,0
15687491,Male,30,107000,1
15610403,Female,28,59000,0
15741094,Male,19,25000,0
15807909,Male,19,85000,0
15666141,Female,18,68000,0
15617134,Male,35,59000,0
15783029,Male,30,89000,0
15622833,Female,34,25000,0
15746422,Female,24,89000,0
15750839,Female,27,96000,1
15749130,Female,41,30000,0
15779862,Male,29,61000,0
15767871,Male,20,74000,0
15679651,Female,26,15000,0
15576219,Male,41,45000,0
15699247,Male,31,76000,0
15619087,Female,36,50000,0
15605327,Male,40,47000,0
15610140,Female,31,15000,0
15791174,Male,46,59000,0
15602373,Male,29,75000,0
15762605,Male,26,30000,0
15598840,Female,32,135000,1
15744279,Male,32,100000,1
15670619,Male,25,90000,0
15599533,Female,37,33000,0
15757837,Male,35,38000,0
15697574,Female,33,69000,0
15578738,Female,18,86000,0
15762228,Female,22,55000,0
15614827,Female,35,71000,0
15789815,Male,29,148000,1
15579781,Female,29,47000,0
15587013,Male,21,88000,0
15570932,Male,34,115000,0
15794661,Female,26,118000,0
15581654,Female,34,43000,0
15644296,Female,34,72000,0
15614420,Female,23,28000,0
15609653,Female,35,47000,0
15594577,Male,25,22000,0
15584114,Male,24,23000,0
15673367,Female,31,34000,0
15685576,Male,26,16000,0
15774727,Female,31,71000,0
15694288,Female,32,117000,1
15603319,Male,33,43000,0
15759066,Female,33,60000,0
15814816,Male,31,66000,0
15724402,Female,20,82000,0
15571059,Female,33,41000,0
15674206,Male,35,72000,0
15715160,Male,28,32000,0
15730448,Male,24,84000,0
15662067,Female,19,26000,0
15779581,Male,29,43000,0
15662901,Male,19,70000,0
15689751,Male,28,89000,0
15667742,Male,34,43000,0
15738448,Female,30,79000,0
15680243,Female,20,36000,0
15745083,Male,26,80000,0
15708228,Male,35,22000,0
15628523,Male,35,39000,0
15708196,Male,49,74000,0
15735549,Female,39,134000,1
15809347,Female,41,71000,0
15660866,Female,58,101000,1
15766609,Female,47,47000,0
15654230,Female,55,130000,1
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15800890,Female,40,142000,1
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15724536,Female,48,96000,1
15735878,Male,52,150000,1
15707596,Female,59,42000,0
15657163,Male,35,58000,0
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15779529,Female,60,108000,1
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15732987,Male,59,143000,1
15789432,Female,41,80000,0
15663161,Male,35,91000,1
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15575002,Female,35,60000,0
15622171,Male,37,53000,0
15795224,Female,36,126000,1
15685346,Male,56,133000,1
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15721007,Female,42,80000,1
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15813113,Male,40,107000,1
15614187,Male,49,86000,1
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15646227,Male,46,79000,1
15660541,Male,40,57000,0
15753874,Female,37,80000,0
15617877,Female,46,82000,0
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15701537,Male,42,149000,1
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15780572,Female,50,88000,1
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15586996,Female,41,72000,0
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15775590,Female,57,122000,1
15730688,Male,41,52000,0
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15810075,Female,44,39000,0
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15584320,Female,37,146000,1
15724161,Female,50,44000,0
15750056,Female,52,90000,1
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15569641,Female,58,95000,1
15815236,Female,45,131000,1
15811177,Female,35,77000,0
15680587,Male,36,144000,1
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15767681,Female,35,72000,0
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15801336,Female,42,108000,1
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15746203,Female,47,144000,1
15583137,Male,40,61000,0
15680752,Female,43,133000,0
15688172,Female,59,76000,1
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15589449,Male,39,106000,1
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15727467,Male,57,74000,1
15734312,Male,38,71000,0
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15613014,Female,52,38000,1
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15685536,Male,35,61000,0
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15638646,Male,48,141000,0
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================================================
FILE: datasets/mnist.npz
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[File too large to display: 11.0 MB]
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FILE: datasets/readme.md
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## 代码中使用的数据
50 50_Startups.csv
10 Data.csv
400 Social_Network_Ads.csv
40966 mnist.npz
28 studentscores.csv
41455 total
================================================
FILE: datasets/studentscores.csv
================================================
Hours,Scores
2.5,21
5.1,47
3.2,27
8.5,75
3.5,30
1.5,20
9.2,88
5.5,60
8.3,81
2.7,25
7.7,85
5.9,62
4.5,41
3.3,42
1.1,17
8.9,95
2.5,30
1.9,24
6.1,67
7.4,69
2.7,30
4.8,54
3.8,35
6.9,76
7.8,86
2.1,93
2.2,93
2.5,93