├── .gitignore ├── LICENSE ├── README.md └── code └── ml.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 kevingo 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Python-Machine-Learning-Mini-Course-zh-TW 2 | 3 | [Python Machine Learning Mini-Course](https://machinelearningmastery.com/python-machine-learning-mini-course/) 是 Jason Brownlee 在 Machine Learning Mastery 所發表的一篇教學文，內容淺顯易懂，對於想要透過 Python 來入手機器學習的新手來說，是很好的文章，在這裡分享給大家。 4 | 5 | ## 14 天從開發者到機器學習工作者 6 | 7 | Python 已經成為應用機器學習領域中發展最快速的平台語言之一。 8 | 9 | 在這門簡短的課程中，你會了解到如何在 14 天內，使用 Python 來建構機器學習模型，並且有自信地完成一個機器學習的專案。 10 | 11 | 這是一篇很重要的文章，你可以把他加入書籤中。 12 | 13 | 讓我們開始吧！ 14 | 15 | * **[更新] Oct/2016**: 更新範例至 sklearn v0.18 16 | * **[更新] Feb/2018**: 更新 Python 和 library 版本 17 | * **[更新] March/2018**: 更改某些資料集的下載連結，某些連結已經失效 18 | 19 | ## 這門課程的對象是誰？ 20 | 21 | 在我們開始學習之前，讓我們確保你站在正確的位置。 22 | 23 | 底下我描述了一些通則，讓你知道這門課所設計的學習對象為何。 24 | 25 | 如果你不完全符合以下的條件，別緊張，你可能只要在某一個或幾個領域重新學習就可以跟上。 26 | 27 | - 知道如何撰寫一些程式碼的開發者。這代表說你在學習一門新的語言，像是 Python 時，一旦你知道了基本的語法，這不是太大的問題。這不代表你需要是一個開發狂熱份子，只要你可以輕鬆的了解基本的 C 或類似於 C 語言的語法即可。 28 | - 知道一點點機器學習相關知識的開發者。意味者你知道基本的 cross-validation、一些演算法和偏差和方差之間的取捨 (Bias–variance tradeoff) 等概念。 29 | 30 | 這門基本的課程並不是 Python 或機器學習的教科書。 31 | 32 | 這門課程會讓你從一個知道一點點機器學習的開發者，成長為一個能使用 Python 相關生態系來得到機器學習所訓練出來的模型結果。 33 | 34 | ## 課程導覽 35 | 36 | 這門課程共分為 14 章。 37 | 38 | 你可以一天完成一堂課 (這是推薦的節奏)，或是在一天之內完成所有課程 (相當精實！)。這完全取決你的時間以及熱情。 39 | 40 | Below are 14 lessons that will get you started and productive with machine learning in Python: 41 | 42 | 以下是 14 門課程的標題： 43 | 44 | - 第一課：下載並安裝 Python 和 SciPy 45 | - 第二課：開始練習 Python、NumPy、Matplotlib 和 Pandas 46 | - 第三課：從 CSV 中讀取資料 47 | - 第四課：透過敘述統計的方法來瞭解資料 48 | - 第五課：透過視覺化來了解資料 49 | - 第六課：針對資料進行前處理，準備進入建模階段 50 | - 第七課：透過重複抽樣(Resample method)的方法來進行演算法評估 51 | - 第八課：演算法評估指標 52 | - 第九課：針對演算法進行抽樣做比較 53 | - 第十課：模型的比較與選擇 54 | - 第十一課：透過演算法調優來改善準確率 55 | - 第十二課：透過集成式預測方法 (Ensemble Predictions) 來改善準確率 56 | - 第十三課：完成並保存你的模型 57 | - 第十四課：從頭到尾來完成一個 「Hello World」 的機器學習專案 58 | 59 | 每一門課程可能會花費 60 秒到 30 分鐘，按照自己的步調，花一點時間完成每一堂課。有任何問題都歡迎隨時發問。 60 | 61 | 我期待你可以從課程中自行學習，內容中會有提示，但我期待你可以自己去找相關的資源來完成所有內容，你也可以在原作者的部落格中找到一些提示。 62 | 63 | ## 第一課：下載並安裝 Python 和 SciPy 64 | 65 | 你必須要擁有 Python 相關開發環境後，才能夠開始學習機器學習。 66 | 67 | 今天的課程很簡單，你必須要下載 Python 3.6 在自己的電腦上。 68 | 69 | 訪問 Python 官方網站，並根據自己的作業系統下載對應的 Python 版本。你可能要根據自己的作業系統來使用對應的套件管理工具來進行安裝，像是 OSX 的 macports 或 RedHat 的 yum。 70 | 71 | 你也需要安裝 SciPy 以及 scikit-learn，我建議使用和安裝 Python 相同的方式來進行安裝。 72 | 73 | 你也可以使用 Anaconda，它幫你把所有需要的函式庫都打包在一起，對於初學者來說相當方便。 74 | 75 | 開始學習 Python 了，你可以在 command line 輸入 `python` 指令來進入 python 的互動 shell。 76 | 77 | 透過以下簡單的程式碼來確認你的函式庫的版本： 78 | 79 | ```python 80 | # Python version 81 | import sys 82 | print('Python: {}'.format(sys.version)) 83 | # scipy 84 | import scipy 85 | print('scipy: {}'.format(scipy.__version__)) 86 | # numpy 87 | import numpy 88 | print('numpy: {}'.format(numpy.__version__)) 89 | # matplotlib 90 | import matplotlib 91 | print('matplotlib: {}'.format(matplotlib.__version__)) 92 | # pandas 93 | import pandas 94 | print('pandas: {}'.format(pandas.__version__)) 95 | # scikit-learn 96 | import sklearn 97 | print('sklearn: {}'.format(sklearn.__version__)) 98 | ``` 99 | 100 | 如果出現任何錯誤，現在就花時間修正他們。 101 | 102 | 需要幫助的時候，看看底下的這篇文章： 103 | 104 | [如何使用 Anaconda 來建立機器學習和深度學習的開發環境](https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/) 105 | 106 | ## 第二課：開始練習 Python、NumPy、Matplotlib 和 Pandas 107 | 108 | 你需要有能力可以撰寫基本的 Python 程式碼。 109 | 110 | 身為開發者，你可以快速的學習新的程式語言。Python 區分大小寫，# 作為註解，並使用空白來表示程式碼區塊 (空白是重要的)。 111 | 112 | 今天的任務是在 Python 的互動環境中練習 Python 的基本語法，並練習 SciPy 的資料結構。 113 | 114 | 練習使用指派運算，使用 list 和流程控制。 115 | 練習 NumPy 陣列。 116 | 練習使用 Matplotlib 建立簡單的圖表。 117 | 練習使用 Pandas 的 Series 和 DataFrames。 118 | 舉例來說，底下是使用 Pandas 建立 DataFrame 的例子： 119 | 120 | ```python 121 | # dataframe 122 | import numpy 123 | import pandas 124 | myarray = numpy.array([[1, 2, 3], [4, 5, 6]]) 125 | rownames = ['a', 'b'] 126 | colnames = ['one', 'two', 'three'] 127 | mydataframe = pandas.DataFrame(myarray, index=rownames, columns=colnames) 128 | print(mydataframe) 129 | ``` 130 | 131 | ## 第三課：從 CSV 中讀取資料 132 | 133 | 機器學習演算法需要資料。你的資料可以從自己的資料集來，可是當你一開始練習的時候，應該練習使用標準機器學習資料集。 134 | 135 | 今天你的任務就是熟悉在 Python 中讀取資料，並且使用標準的機器學習資料集。 136 | 137 | 在網路你可以找到許多優秀的機器學習用的資料集。在這堂課中，你可以從[UCI 機器學習資料庫](http://machinelearningmastery.com/practice-machine-learning-with-small-in-memory-datasets-from-the-uci-machine-learning-repository/) 中下載相關的資料集。 138 | 139 | 練習在 Python 中使用 [CSV.reader()](https://docs.python.org/2/library/csv.html) 來讀取 CSV 資料。 140 | 141 | 練習使用 Numpy 的 [numpy.loadtxt()](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.loadtxt.html) 函示來讀取資料。 142 | 143 | 練習使用 Pandas 的 [pandas.read_csv()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) 函示來讀取資料。 144 | 145 | 為了讓你可以更快入門，下面的程式碼是一個簡單的範例，它直接從 UCI 機器學習資料庫中，透過 Pandas 來讀取 Pima 印地安人糖尿病資料集： 146 | 147 | ```python 148 | # Load CSV using Pandas from URL 149 | import pandas 150 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" 151 | names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] 152 | data = pandas.read_csv(url, names=names) 153 | print(data.shape) 154 | ``` 155 | 156 | ## 第四課：透過敘述統計的方法來瞭解資料 157 | 158 | 一旦你將資料讀取到 Python 後，你就能夠更好地瞭解你的資料。 159 | 160 | 當你更了解你的資料，你所建立的模型就會更好且更準確。了解資料的第一步就是使用敘述統計的方式來進行。 161 | 162 | 今天的功課就是透過敘述統計的方法來了解資料。我建議你可以使用 Pandas DataFrame 提供的相關函式。 163 | 164 | 透過 head() 函式來看看數筆資料。透過 shape 屬性來觀看資料的維度。透過 dtypes 屬性來看看資料的型態。透過 describe() 函式來看看資料的分佈狀況。使用 corr() 函式來計算資料之間的相關係數。 165 | 166 | 底下的範例是讀取 Pima 印地安人糖尿病資料集後，透過 describe() 函式來觀看資料的分佈。 167 | 168 | ```python 169 | import pandas 170 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" 171 | names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] 172 | data = pandas.read_csv(url, names=names) 173 | description = data.describe() 174 | print(description) 175 | ``` 176 | 177 | ## 第五課：透過視覺化來了解資料 178 | 179 | 延續昨天的課程，你必須要花時間了解你的資料。 180 | 181 | 第二種了解資料的方式就是透過視覺化的技巧(例如：繪圖)。 182 | 183 | 今天，你的功課就是去學習如何在 Python 中透過繪圖的方法來了解資料中個屬性的特性，以及彼此交互的關係。同樣，我也建立可以使用 Pandas 中相關輔助的函式來幫助你更了解資料。 184 | 185 | 使用 hist() 函式來建立每個屬性的直方圖。 186 | 使用 plot(kind='box') 函式來建立每個屬性的 box-and-whisker 圖。 187 | 使用 pandas.scatter_matrix() 函式來建立所有屬性兩兩之間的散佈圖。 188 | 189 | 舉例來說，底下的程式碼會讀取糖尿病資料集，並建立資料集之間的散佈圖矩陣。 190 | 191 | ```python 192 | # Scatter Plot Matrix 193 | import matplotlib.pyplot as plt 194 | import pandas 195 | 196 | # The plotting method is moving to pandas.plotting from pandas.tools.plotting 197 | # If error occured, try to load plotting from old version. 198 | try: 199 | from pandas.plotting import scatter_matrix 200 | except Exception as e: 201 | from pandas.tools.plotting import scatter_matrix 202 | 203 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" 204 | names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] 205 | data = pandas.read_csv(url, names=names) 206 | scatter_matrix(data) 207 | plt.show() 208 | ``` 209 | 210 | ![image](https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2016/09/Sample-Scatter-Plot-Matrix.png) 211 | 212 | 213 | ## 第六課：針對資料進行前處理，準備進入建模階段 214 | 215 | 你的原始資料很有可能不是建立模型的最好狀態。 216 | 217 | 很多時候，你需要針對資料進行前處理，讓你的資料可以很好的餵給模型演算法。今天的課程中，你將會使用 scikit-learn 的資料前處理功能來處理資料。 218 | 219 | scikit-learn 的函式庫提供了兩種標準的資料轉換方式。這兩種都很有用，分別是：進行 fit() 和 多次的 transform()，或是 fit() 和 transform() 結合的轉換。 220 | 221 | 有許多的方法可以在建模前來進資料準備，讓我們看看底下的例子： 222 | 223 | - 對數值資料進行標準化 (例如：將資料轉換為平均數為 0，標準差為 1 的分配) 224 | - 利用 range 的參數讓數值資料標準化 (例如：將數值資料轉換為 0-1 的區間) 225 | - 探索更進階的資料工程技巧，例如：二值化 (Binarizing) 226 | 227 | 來看個例子，底下的程式碼會讀取 Pima 印地安人糖尿病資料集，計算進行資料正規化所需要的參數，然後建立正規化後的資料： 228 | 229 | ```python 230 | # Standardize data (0 mean, 1 stdev) 231 | from sklearn.preprocessing import StandardScaler 232 | import pandas 233 | import numpy 234 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" 235 | names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] 236 | dataframe = pandas.read_csv(url, names=names) 237 | array = dataframe.values 238 | # separate array into input and output components 239 | X = array[:,0:8] 240 | Y = array[:,8] 241 | scaler = StandardScaler().fit(X) 242 | rescaledX = scaler.transform(X) 243 | # summarize transformed data 244 | numpy.set_printoptions(precision=3) 245 | print(rescaledX[0:5,:]) 246 | ``` 247 | 248 | ## 第七課：透過重複抽樣(Resample method)的方法來進行演算法評估 249 | 250 | 用來給機器學習演算法學習的資料集稱之為訓練資料集。然而，訓練資料集並不能保證機器學習演算法學習到的模型能夠完美的用來預測新的資料。這就是一個大的問題，因為我們之所以訓練模型，就是希望能夠準確的預測新的資料。 251 | 252 | 要解決這樣的問題，你可以透過一種統計的方法，叫做重複抽樣，將你的訓練資料集分成數個子集合，某些用來訓練模型，其他的則是用來評估模型的準確度，以便了解訓練出來的模型在面對沒有看過的資料時的效果如何。 253 | 254 | 而今天課程的目的就是要來練習這種重複抽樣的方法，在 scikit-learn 當中，你可以透過以下步驟來實現： 255 | 256 | - 將資料集分成訓練資料集和測試資料集 257 | - 透過 k-fold 交叉驗證的方法來預估某個模型的準確率 258 | - 透過 leave one out 交叉驗證的方式來預估某個演算法的準確率 259 | 260 | 底下的程式碼使用 scikit-learn 的 10-fold 交叉驗證的方式來驗證使用 Logistic Regression 演算法在 Pima 印地安人糖尿病資料集上的準確率。 261 | 262 | ```python 263 | # Evaluate using Cross Validation 264 | from pandas import read_csv 265 | from sklearn.model_selection import KFold 266 | from sklearn.model_selection import cross_val_score 267 | from sklearn.linear_model import LogisticRegression 268 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" 269 | names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] 270 | dataframe = read_csv(url, names=names) 271 | array = dataframe.values 272 | X = array[:,0:8] 273 | Y = array[:,8] 274 | kfold = KFold(n_splits=10, random_state=7) 275 | model = LogisticRegression() 276 | results = cross_val_score(model, X, Y, cv=kfold) 277 | print("Accuracy: %.3f%% (%.3f%%)") % (results.mean()*100.0, results.std()*100.0) 278 | ``` 279 | 280 | 你得到多少的準確率？在底下留言讓我知道。 281 | 282 | 你知道目前已經學習到一半了嗎？做得好！ 283 | 284 | ## 第八課：演算法評估指標 285 | 286 | 你可以用許多不同的指標來衡量機器學習演算法在資料集上的效果。 287 | 288 | 你可以透過 scikit-learn 中的 cross_validation.cross_val_score() 函式來針對你的測試資料集進行評估，這可以用在回歸和分類的問題上。今天，你的目標是練習使用 scikit-learn 中提供的不同演算法的評估指標。 289 | 290 | - 練習在分類問題上使用 Accuracy 和 LogLoss 指標 291 | - 練習使用混淆矩陣和分類報告 292 | - 練習在回歸問題上使用 RMSE 和 RSquared 指標 293 | 294 | 底下的程式碼會在 Pima 印地安人糖尿病資料集上使用 LogLoss 指標來進行評估。 295 | 296 | ```python 297 | # Cross Validation Classification LogLoss 298 | from pandas import read_csv 299 | from sklearn.model_selection import KFold 300 | from sklearn.model_selection import cross_val_score 301 | from sklearn.linear_model import LogisticRegression 302 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" 303 | names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] 304 | dataframe = read_csv(url, names=names) 305 | array = dataframe.values 306 | X = array[:,0:8] 307 | Y = array[:,8] 308 | kfold = KFold(n_splits=10, random_state=7) 309 | model = LogisticRegression() 310 | scoring = 'neg_log_loss' 311 | results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring) 312 | print("Logloss: %.3f (%.3f)") % (results.mean(), results.std()) 313 | ``` 314 | 315 | 你跑出怎樣的 Log Loss 值呢？留言讓我知道。 316 | 317 | ## 第九課：針對演算法進行抽樣做比較 318 | 319 | 你不可能事先知道哪一種演算法在你的資料集上表現最好。 320 | 321 | 你必須透過試誤的過程來發現。我稱這叫做「演算法的抽樣」。scikit-learn 針對各種演算法提供了標準的函式庫介面，讓你可以用來比較這些演算法的精準度(accuracy)。 322 | 323 | 在這門課中，你必須要練習抽樣來比較不同的演算法。 324 | 325 | - 針對線性的機器學習演算法來進行抽樣比較 (例如：線性回歸、邏輯回歸，以及線性判別分析) 326 | - 針對非線性的演算法進行抽樣比較 (例如：KNN、SVM 和 CART) 327 | - 針對複雜的整體學習演算法進行抽樣比較 (例如：隨機森林和隨機梯度提升演算法) 328 | 329 | 底下的程式碼是在使用 KNN 演算法在波士頓房價的資料集上進行隨機抽樣來進行比較。 330 | 331 | ```python 332 | # KNN Regression 333 | from pandas import read_csv 334 | from sklearn.model_selection import KFold 335 | from sklearn.model_selection import cross_val_score 336 | from sklearn.neighbors import KNeighborsRegressor 337 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data" 338 | names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] 339 | dataframe = read_csv(url, delim_whitespace=True, names=names) 340 | array = dataframe.values 341 | X = array[:,0:13] 342 | Y = array[:,13] 343 | kfold = KFold(n_splits=10, random_state=7) 344 | model = KNeighborsRegressor() 345 | scoring = 'neg_mean_squared_error' 346 | results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring) 347 | print(results.mean()) 348 | ``` 349 | 350 | 你得到的誤差是多少？留言告訴我。 351 | 352 | ## 第十課：模型的比較與選擇 353 | 354 | 現在你知道如何針對機器學習演算法進行抽樣比較，而你現在需要知道如何針對不同的演算法進行挑選，從中選擇最好的模型。 355 | 356 | 在今天的課程中，你需要練習 scikit-learn 中不同的機器學習演算法的準確度。 357 | 358 | - 針對同一個資料集比較線性演算法 359 | - 針對同一個資料集比較非線性演算法 360 | - 針對單一演算法比較不同的參數設定 361 | 362 | 針對不同演算法的比較使用圖表進行呈現。 363 | 364 | 下面的範例中，我們比較了邏輯回歸和線性判別分析等兩種機器學習演算法在 Pima 印地安人糖尿病資料集上的表現。 365 | 366 | ```python 367 | # Compare Algorithms 368 | from pandas import read_csv 369 | from sklearn.model_selection import KFold 370 | from sklearn.model_selection import cross_val_score 371 | from sklearn.linear_model import LogisticRegression 372 | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis 373 | # load dataset 374 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" 375 | names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] 376 | dataframe = read_csv(url, names=names) 377 | array = dataframe.values 378 | X = array[:,0:8] 379 | Y = array[:,8] 380 | # prepare models 381 | models = [] 382 | models.append(('LR', LogisticRegression())) 383 | models.append(('LDA', LinearDiscriminantAnalysis())) 384 | # evaluate each model in turn 385 | results = [] 386 | names = [] 387 | scoring = 'accuracy' 388 | for name, model in models: 389 | kfold = KFold(n_splits=10, random_state=7) 390 | cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring) 391 | results.append(cv_results) 392 | names.append(name) 393 | msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std()) 394 | print(msg) 395 | ``` 396 | 397 | 哪一種演算法你得到了比較好的結果？你可以做得更好嗎？留言讓我知道。 398 | 399 | ## 第十一課：透過演算法調優來改善準確率 400 | 401 | 一旦你發現一個或兩個演算法對於你的資料集的表現很好時，你就可以開始改善這些模型的效能。 402 | 403 | 其中一種改善效能的方式是針對演算法的參數進行調整。 404 | 405 | scikit-learn 的函式庫提供兩種方式來針對參數進行搜索。你今天的任務是要學習他們。 406 | 407 | - 透過指定的網格搜尋方法來調整演算法 408 | - 透過隨機搜尋來調整演算法 409 | 410 | 下方的範例是在嶺回歸演算法(Ridge Regression algorithm)上，透過網格搜尋來尋找 Pima 印地安人糖尿病資料集上最佳的參數。 411 | 412 | ```python 413 | # Grid Search for Algorithm Tuning 414 | from pandas import read_csv 415 | import numpy 416 | from sklearn.linear_model import Ridge 417 | from sklearn.model_selection import GridSearchCV 418 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" 419 | names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] 420 | dataframe = read_csv(url, names=names) 421 | array = dataframe.values 422 | X = array[:,0:8] 423 | Y = array[:,8] 424 | alphas = numpy.array([1,0.1,0.01,0.001,0.0001,0]) 425 | param_grid = dict(alpha=alphas) 426 | model = Ridge() 427 | grid = GridSearchCV(estimator=model, param_grid=param_grid) 428 | grid.fit(X, Y) 429 | print(grid.best_score_) 430 | print(grid.best_estimator_.alpha) 431 | ``` 432 | 433 | 哪一個參數的效果最好？你可以做得更好嗎？留言讓我知道。 434 | 435 | ## 第十二課：透過集成式預測方法 (Ensemble Predictions) 來改善準確率 436 | 437 | 另外一種改善模型準確率的方式是透過結合多種模型來改善準確率。 438 | 439 | 有一些模型內建了這樣的功能。比如說：隨機森林 (random forest) 提供了 bagging，而隨機梯度提升法 (stochastic gradient boosting) 也使用了這樣的想法。另一種集成式的方法叫做投票 (voting)，這種方式可以整合不同模型的預測結果。 440 | 441 | 在今天的課程中，你會練習使用集成式的方法來提升準確率。 442 | 443 | - 練習隨機森林的 bagging 集成方法，或是其他的樹狀演算法 444 | - 練習梯度提升集成方法，或是 AdaBoost 演算法 445 | - 練習投票式的集成方法來整合多個模型的結果 446 | 447 | 底下的程式碼會教你如何使用隨機森林演算法 (一種以 bagged 為集成方法的決策樹) 在 Pima 印地安人糖尿病資料集上。 448 | 449 | ```python 450 | # Random Forest Classification 451 | from pandas import read_csv 452 | from sklearn.model_selection import KFold 453 | from sklearn.model_selection import cross_val_score 454 | from sklearn.ensemble import RandomForestClassifier 455 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" 456 | names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] 457 | dataframe = read_csv(url, names=names) 458 | array = dataframe.values 459 | X = array[:,0:8] 460 | Y = array[:,8] 461 | num_trees = 100 462 | max_features = 3 463 | kfold = KFold(n_splits=10, random_state=7) 464 | model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features) 465 | results = cross_val_score(model, X, Y, cv=kfold) 466 | print(results.mean()) 467 | ``` 468 | 469 | 你能夠設計一個更好的集成方法嗎？留言告訴我。 470 | 471 | ## 第十三課：完成並保存你的模型 472 | 473 | 一旦你發現一個模型可以針對你的問題表現得很好時，該是時候完成你的模型了。 474 | 475 | 在今天的課程中，你要練習如何完成你的模型。 476 | 477 | 練習使用你訓練出來的模型在新的資料上進行預測 (這個資料在訓練或測試階段都沒有用過)。 478 | 479 | 練習儲存將訓練好的模型儲存成檔案格式，並且再讀取它們。 480 | 481 | 底下的範例會建立一個邏輯回歸模型 (Logistic Regression Model)，將此模型儲存成檔案，並且之後讀取此模型並用來預測沒看過的資料。 482 | 483 | ```python 484 | # Save Model Using Pickle 485 | from pandas import read_csv 486 | from sklearn.model_selection import train_test_split 487 | from sklearn.linear_model import LogisticRegression 488 | import pickle 489 | url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" 490 | names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] 491 | dataframe = read_csv(url, names=names) 492 | array = dataframe.values 493 | X = array[:,0:8] 494 | Y = array[:,8] 495 | test_size = 0.33 496 | seed = 7 497 | X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=seed) 498 | # Fit the model on 33% 499 | model = LogisticRegression() 500 | model.fit(X_train, Y_train) 501 | # save the model to disk 502 | filename = 'finalized_model.sav' 503 | pickle.dump(model, open(filename, 'wb')) 504 | 505 | # some time later... 506 | 507 | # load the model from disk 508 | loaded_model = pickle.load(open(filename, 'rb')) 509 | result = loaded_model.score(X_test, Y_test) 510 | print(result) 511 | ``` 512 | 513 | ## 第十四課：從頭到尾來完成一個 「Hello World」 的機器學習專案 514 | 515 | 你現在知道如何從頭到尾完成一個機器學習問題了。 516 | 517 | 在今天的課程中，你需要練習把之前學到的每一個部分都放在一起，從頭到尾的來練習一個標準的機器學習專案。 518 | 519 | 練習 [iris 資料集](https://archive.ics.uci.edu/ml/datasets/Iris) 來完成一個從頭到尾的機器學習專案 (這個資料集可以說是機器學習專案的 Hello World)。 520 | 521 | 這會包含以下步驟： 522 | 523 | 1. 透過敘述性統計和資料視覺化來了解你的資料 524 | 2. 針對資料進行前處理，讓資料可以最好的描述你想解決的問題 525 | 3. 使用自己的測試方式來對不同的演算法進行抽樣比較 526 | 4. 透過演算法參數的調整來改善結果 527 | 5. 透過集成式方法來改善結果 528 | 6. 保存最終模型以供未來使用 529 | 530 | 慢慢來，並記錄你的結果。 531 | 532 | 你用了什麼模型？得到什麼結果？留言讓我知道。 533 | 534 | ## 結束！ 535 | ## (看看你走了多遠) 536 | 537 | 你成功了，做得好！ 538 | 539 | 看看你過去學習到的成果。 540 | 541 | - 你對機器學習開始產生興趣，並且可以使用 Python 來練習和應用機器學習的相關技術 542 | - 你下載、安裝，並且開始使用 Python，這可能是你第一次熟悉這個語言 543 | - 在課程中，你逐步的學習預測性機器學習建模的標準任務，並且在 Python 上面實作他們 544 | - 基於常見的機器學習方法，你透過 Python 從頭到尾完成了一個專案 545 | - 透過標準的模板和學習經驗，你可以自己處理新的資料集與不同的機器學習問題 546 | 547 | 不要小看這一切，你在很短的時間內走了很長的路。 548 | 549 | 而這只是機器學習旅程的開端，持續練習並且發展你的技能。 550 | 551 | ## 總結 552 | 553 | 你怎麼使用這個課程？ 554 | 你喜歡這個課程嗎？ 555 | 556 | 有沒有任何問題？你在什麼地方被困住呢？ 557 | 讓我知道，在底下留言。 558 | 559 | ## 參考連結 560 | - [Python Machine Learning Mini-Course](https://machinelearningmastery.com/python-machine-learning-mini-course/) 561 | -------------------------------------------------------------------------------- /code/ml.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [ 10 | { 11 | "name": "stdout", 12 | "output_type": "stream", 13 | "text": [ 14 | "Python: 2.7.12 |Anaconda custom (x86_64)| (default, Jul 2 2016, 17:43:17) \n", 15 | "[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2336.11.00)]\n", 16 | "scipy: 1.0.1\n", 17 | "numpy: 1.11.3\n", 18 | "matplotlib: 1.5.3\n", 19 | "pandas: 0.18.1\n", 20 | "sklearn: 0.19.1\n" 21 | ] 22 | } 23 | ], 24 | "source": [ 25 | "## 第一課：下載並安裝 Python 和 SciPy\n", 26 | "\n", 27 | "# Python version\n", 28 | "import sys\n", 29 | "print('Python: {}'.format(sys.version))\n", 30 | "# scipy\n", 31 | "import scipy\n", 32 | "print('scipy: {}'.format(scipy.__version__))\n", 33 | "# numpy\n", 34 | "import numpy\n", 35 | "print('numpy: {}'.format(numpy.__version__))\n", 36 | "# matplotlib\n", 37 | "import matplotlib\n", 38 | "print('matplotlib: {}'.format(matplotlib.__version__))\n", 39 | "# pandas\n", 40 | "import pandas\n", 41 | "print('pandas: {}'.format(pandas.__version__))\n", 42 | "# scikit-learn\n", 43 | "import sklearn\n", 44 | "print('sklearn: {}'.format(sklearn.__version__))" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 1, 50 | "metadata": { 51 | "collapsed": false 52 | }, 53 | "outputs": [ 54 | { 55 | "name": "stdout", 56 | "output_type": "stream", 57 | "text": [ 58 | " one two three\n", 59 | "a 1 2 3\n", 60 | "b 4 5 6\n" 61 | ] 62 | } 63 | ], 64 | "source": [ 65 | "## 第二課：開始練習 Python、NumPy、Matplotlib 和 Pandas\n", 66 | "\n", 67 | "# dataframe\n", 68 | "import numpy\n", 69 | "import pandas\n", 70 | "\n", 71 | "myarray = numpy.array([[1, 2, 3], [4, 5, 6]])\n", 72 | "rownames = ['a', 'b']\n", 73 | "colnames = ['one', 'two', 'three']\n", 74 | "mydataframe = pandas.DataFrame(myarray, index=rownames, columns=colnames)\n", 75 | "\n", 76 | "print(mydataframe)" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 3, 82 | "metadata": { 83 | "collapsed": false 84 | }, 85 | "outputs": [ 86 | { 87 | "name": "stdout", 88 | "output_type": "stream", 89 | "text": [ 90 | "(768, 9)\n" 91 | ] 92 | } 93 | ], 94 | "source": [ 95 | "## 第三課：從 CSV 中讀取資料\n", 96 | "\n", 97 | "# Load CSV using Pandas from URL\n", 98 | "import pandas\n", 99 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n", 100 | "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", 101 | "data = pandas.read_csv(url, names=names)\n", 102 | "print(data.shape)" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 1, 108 | "metadata": { 109 | "collapsed": false 110 | }, 111 | "outputs": [ 112 | { 113 | "name": "stdout", 114 | "output_type": "stream", 115 | "text": [ 116 | " preg plas pres skin test mass \\\n", 117 | "count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 \n", 118 | "mean 3.845052 120.894531 69.105469 20.536458 79.799479 31.992578 \n", 119 | "std 3.369578 31.972618 19.355807 15.952218 115.244002 7.884160 \n", 120 | "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 121 | "25% 1.000000 99.000000 62.000000 0.000000 0.000000 27.300000 \n", 122 | "50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 \n", 123 | "75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 \n", 124 | "max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 \n", 125 | "\n", 126 | " pedi age class \n", 127 | "count 768.000000 768.000000 768.000000 \n", 128 | "mean 0.471876 33.240885 0.348958 \n", 129 | "std 0.331329 11.760232 0.476951 \n", 130 | "min 0.078000 21.000000 0.000000 \n", 131 | "25% 0.243750 24.000000 0.000000 \n", 132 | "50% 0.372500 29.000000 0.000000 \n", 133 | "75% 0.626250 41.000000 1.000000 \n", 134 | "max 2.420000 81.000000 1.000000 \n" 135 | ] 136 | } 137 | ], 138 | "source": [ 139 | "## 第四課：透過敘述統計的方法來瞭解資料\n", 140 | "\n", 141 | "import pandas\n", 142 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n", 143 | "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", 144 | "data = pandas.read_csv(url, names=names)\n", 145 | "description = data.describe()\n", 146 | "print(description)" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 4, 152 | "metadata": { 153 | "collapsed": false 154 | }, 155 | "outputs": [ 156 | { 157 | "data": { 158 | "image/png": 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POTMZSIPiPyB0sx5RxjMQIdgPdNDUFOPs2U4gj1jsELFYHSkpDurrF19jK+tm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\nDDnimYacSDqJrOc6RHakYhg78PkK2Ls3m337TpKcPOt8ue0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Lllyc4In1nXL2/Wc/28Wp\nU52cPt2Lrn/M+v565FRLFsL6HdZrU4jXKihElNVNCBCxY8u1wAoMow6nM8bs2VU89tgj73OIoAy7\ncZ9pNhBv+FWA7KGQ9RzlyB4rRua4DVEYS5EIpxuxLIfJzgaXy8+2bSFOnJhGYWEDOTkeUlLOEI2+\nQHZ2A1/+8pd48sknAVGsu3btuupCYoYxg9bWdhoaGscNMPbs2cM//MPLNDQkI7H+HkQudFv/ZyFr\ntwZZpyHrub0I+MpDRFU+wpeSjOjxXEdZWRIdHe+RkLCY9eufvmQX1ZFjuVCJ2X0iZiAAQiEAR05L\nyHcnWmPNscZ2GpFnTcAgpmkgHRqmEI0m09SUBUQIhVwEg3l0dByhqGgmnZ1ZGMZ03n77t+TmdjI4\nOPV8XsYTT6zmK19ZO66Q1Vgyzum0n3E2wu/NCP83Wz9pyF49jKiIMmQvOBAZPsf67UGMyHREJmuI\nrLb3nWbNSzOGsQRNuwvD2G2tT7Z1j3NAA0ppFBf343LV0dp6O0VFX6ap6ScUFNSN+Xyjuypftb39\nIaBSRGe+OOFPTgpgmKbZjPitLn59NKS7MoWJZ4XdMaIOxseQ0Mt0ZId4R3zGJN59awI0DxH61yEO\njwjigrsfaVuukJYl1QjjHkLc1IUIMu5HULENJN5ANmoeoqBC1jVeZBPbsWkvYrksAvpITk7C6y1m\naKgdw1jAokV/T03N9zh1SrpnftDJazC65sXFIEJo7M6w1+boahdg4HZ7rnjlxVRWVoLb/Q4nTx7D\nNHMxzRkIW9USBxIrkfisneiWjQid9xDvyXoEpdcgR/mCyMmIE4hgyyAUKiAaDeNwOIjFFpKQkIjL\nlX0JALgSUabvEg5Pw+FQ6LpdQTSK8JCfeCXIfGscJUgYoQ3ZEvOsZ6lB+DIN4av7Mc2t9PdvYeHC\nhz4AD9daZC7eQwROGOHp6xFlmYDsn04EdGRZr2UCfTid3eh6OeAnIWEN0WgLhrEPv38AuJOkpOuA\nY9x+ewkzZgxYdQcu9FpcK8/evn3/SEJCjN7eR8f9mYaGRk6e1IlGk6y5SEdK8aQg4Yj9yF6fY83H\nCWS/32S912jNWyGi6E8htUKOc+7cTDRtMcPDZ3nhhc+QktKP3z/1kgmVo5VYxPru/QioSECU7xoE\nIO9EwHWHNY5pSA5JD/Hc+BgCQpIxzWWIV8CJabYSiQwDMfz+dqLRFRQW5jI42Ibb7Sc19XrrBNMz\nbN++gzlz5owL/I0l4+JNxHzEq6QaCDjNRPZkEQIw0rFDHQKa5hLPbzmLyNyRbSFusJ71NWt+JI9J\n1xPRtDNoWisOB+h6utXheRpwEI8nH48ng6ysAB0dFXR1bSItrZmpU0t45pnnRj3r6KJuc7n4uPpH\nixTCxyv5wADGBV+vVAJ220iLTEkxHy9dqg7G/wT2m6Z5l1JqOfAHpVSZVSp8knUwZiKA4ATSKrkc\n2fBzkA25zbp1LSIE7MJLn0KS3KS8uDDqW4ggbQNuAf4rkpvxa4SxgwhaPoyAmmUkJs7BMGrJz9d4\n9NF/5Pe//y5tba9TU/M9XK4T52PMH3Ty2qVqXowmH/L878fRVTl54/ONwxE1ggzDwDAMlKqlu/sk\nhlFkFcHyI4I/ghxyegNZh7OIVXQD8DXEIqpHYrwpyHp3WtccQ6zPGUg4ZTZOZw2pqScwDMjKmkph\noZuysrFO2dRY9zLx+d7jzJl6615DCHAJWPfvQxyBs7G7LoqgP2LNid1KficClBQSVw8DnSQkeEhJ\nuSZpT1cgu58GiJAfQsJAcxF+b0fmSDrlyhy2Ij1MzhCL1eJyHcXjKSY5OYJpNjFtWgLDw0sxDI2+\nvn2kpNQyY8bnLgkarpVnLxabRiDQyhtvvDHufJ+BAR9+/3GER/YiMsBhPXMJ8U6qw9bfA4hi/Azx\nDriLkXUvQ7w5KUSjjYTDjaxa9VmOH/89kcgUQqEuotEZl3zO0UosA5FZJxEDqQSpQ3IQ4e0BhM/b\nEZvsGAIOP4Uo6rPIfhm0ns2DAPB+oAjTdBEOl9PdPYto9E3q60tITp5JOBxhcPAgFRUaAwMHOHQo\nSHX13HGBv7Fk3KZNm6x3u5D9E7aebYY1zlSE38LI/qizxnwCARKnrWfMRcBVhTXvldZ9woiXaY61\nFgqowDDSUCqGxxMlEtmPYTyIps1H0wymTo3R3R0AllFYeJSCgoNMnVpCV1cmW7aMBoCji7rdgRiq\nF6u3jwqZyD6+UnPzsWmydTCSEVDwEGKqXEzjzgi5TB2Mh7DavZumeUQp1YaYDjuZdB2M1xBA0IsI\n/0FkI/0U2ZyJCFayY8n3IMVF6hDroJW4e9SFCJcmBKh8H2GkdEQI6cRbNmcjzL8dl6sG04yydev3\nyMyUuGhX108pLZ3K448/Dkw+eW2yNHbNi9eA71ziE+/H0dVbgCPEYlfOVL64QNn+/T727s3C789A\nBPjbyDr5EIvNjot3IoAjB1mrHyMW+RACDmPW5+2kuCjSW6MFEXgayclnuO22YjRNo7g4nTvuWHMJ\nAGgfpYOWlhrC4SpEcPussTgRT8luRCF5EB5Mtn7mWeNts347EC9HOjL/1YhSyGPnzreZMWPapEMG\n46NdyJzoiKfHjVhmg8h8xRBBvwrZO69b42sEakhNnYLLlU1ZWQ8pKe+waNE87r33a/ziF/s4dy5C\ncnIFDz98qbkUulaePU37Ioaxg66uHeO63jAMWlvPIXzSjRgP1db/mYiCHkJkwRnE4ktFPBn/hBgZ\nUUQpOpD1/xiyrk7CYR+NjS/i8Sxg3brHaWj4dyKRiks+52glFkZAQzoCPgcQmWXnKdi1NwaJg4lS\nBIBkWtevtt5zIUrlsPUM09G0Hlyu1SQlTcHv30ksloHTuZSkpCmsXNnPnDkmVVVeqqquHzf4u7yM\nCyKifxAxaE5bz4Y1/03W7zcQmV2HALtZ1vw6kJCKCwEa+5Cw9wJkf5+wnrkKe5+bZj7B4HpgKw7H\nfjStn4SEdnw+nUBgAXl5t5OYWMZ998kotmxRYz7r6KJu25A98FFN8gSZ38mV2Z+sB+N/Iz7lrwLP\nILC5EPgykoF2VaSUygScdpKnRSNrXUyyDkYjoihuQVw+BxBF40bAw/0IdnkRAQ2PIIy8GREQ3Ygg\nNRBGtUGHF9nMQ4hwXY0wdSdiQURRapj09CkEAp0MDBwlFnub5uYWBgamoNQnOHLkIJ/+9Kf5xCfu\nv6r48tXRSODw/lcLvZBeAVpJT78yUh7pKm9r243bfTOgY5qlOBzz0PV9xIVlIsIaaxDB/zLifq1A\nBL4XyYWoQeKuBvHy3OsREOAAXsfh6CIxMYnOTg9e7600NTWgaRqapo2ReLcc4Y0Wq4NrEgImSqzv\nmI3wYT+itPsQ3mpBrK0W67VexGNwEwKO+pFwXT2alkA0Op/Dh7uIxXZy4sSJcTfXmzh5rPGBAAuF\ngLggItizrOfbicz7oDX2LbhcU/B6HyAQOEMweITS0s/T09OA0+nkq19db1mxG6445mvl2TOM54Gz\npKcnj+v6d955h1/96kUikRCyx223sdP60RHLuIu4MzcHCa82IfOTgsiCVsSz0YIo+uvRtEZmzhwm\nFPLh9++hoMDFjTcuwesdu2DaaCVmlwcvRUJoe4kfoZ2DKIiTCJ/bSnsGomRPIwbQKWTPzwScKJVD\nWppGJLKd5OQl5OZupKlpO6Zp4HYP4vMdpr+/hTvv/Dpr1661cmOuVVjXjvn3IF49+zRMP/HeQfcg\nYP0c8R4vjyAnAusRgNJhrcdsBIA1IXLbYc1XgXXvWuALaNqn0PUYLtdWTPMsul5HJJKMpqVx8uRW\nZs/2UVb2EMAEgO4uLl0v8qNCg8g+nzhNFmDcA3zGNM13lVK/AvaYplmnJG3/UST+8CGkZQiTpSBM\nlmr9fAk5OdCOCIkeBIw8ZV2fimxc+7hjn3WtfZRqBZLf+jrwrtUgbQgRtC4ghsuVhNdbytDQDoaG\ninE47sfnexnDyKS4+O85d+4v2bPnXWAiZ6z/M5EcbSwsLLrilSNd5T09Z4hE5LiwUjlo2mJ0/Szx\nrPFZiKI7iAijKkTw2xn/s5BY7g5kI92F3adBrNRE4AAOR4w5c54gHO7G7/ezZs3YZavjiXdfRk4W\nHCQQ2G7d7wFrXLsRoXMGEfZ2PkMOAmp+h1g8NyNC0k6irEFAbgCYiWk2EovVE4tN5cyZHhobT1JY\neP/7xD+zkX1xHAEaSxHQVIAosLMIOC1FwLUOZJKWVoZhLKe//3UyM7vJyrr5vOXX3NzKZz/72Lg9\nddfKs+d27wESmDHjxnFd/8tf/ore3hTE2s9GLOCpyLMfRRRfJ8JPdh7TEkRu5AFPEK9AGUD4qwkB\n9DFMs4bbb//M+Z4yZWXrJwgQB4jnXAxb//daY5iKKFm77s80RMYNYB/DjvfysD00OqaZxtBQKgkJ\nCq83SjT6FgkJ+9C0Mrzeuxgc3MrSpamsXr36fJiyrCwMnGHDhtuuMqzbYo2xHQkfrkLm/kVkjy5F\nHNwnkH07jHj63kCA0hzEMOhC5nkaoON2ryQSOYmAFLvr7A5E1v+BaLQbp7Maw8jA5VqAri8hGm1n\n5cqbOHv2HRITxZs1MaC7AAFCzVcxH39MUkioeT6SPjAxmizAyCSeuTJg/Q8CnX86yXueJ9M0+5RS\nMaVU7ggvRhnxVZpkHYyTyCPvsf7uJ95dNQtRKC8imy0dQdFdiFD5JHGr3k71+DPiwqLKuk8Xpvma\nNbTbEKXiBn5HMPgrnM4mAoFVdHd7rK6BR+jo+A5KHSQ1ddEot5tdaGskxZOh/jPRXwAHOXv22THf\nHRkW6e/vxecrZ+vWfaSnh3jggcW0t3fwhz+cIRLZT2trBbpuImvairBqA7KuXgRgzkHAxSDiGZiC\nrLNdBtiu+7AZpcKkpKyiry+Crp8hNTU2ynoZnXj3d9jn30MhJ6Kg70f4bg/CE0esMZ5BlHIJIuAz\nkWhgN7LBu5BQTjcCRm5CqaVoWgyXqx6fTwMq8HrXvI8nj7qIZ8RPsf6+C7F4uxAP33rrOQPW3J3C\nMBaSmXk/AwN/YObMQbKz3R9o8vJYpOs343S2kJyccOWLAb9/EAGaOYhMqEX2vW3ZvYQYHksQQdxI\nvM7EQUQ02bkQZQjYtMNcL5OcrEhPz2Dt2rWsXi18PlYC4aWpE7H5ypBjw1GkdHg1wverEI/tbkQp\nl1jP04h4cJdZzxZF+L4YWImm5ROJ7KKvr56srFSKi7MAE03rYubMIr70pQfRNI1du3bx85/vIRye\ni8cT9+pNnpKJ89GrxI/1zrbGWIMk2DYTd93beW9S3l88NF3IPl8AaEQiu1Gq03qvG9OMIOuyAWjF\n4diP15uC0xkhEskmM3MpQ0N7aWx8h3DYg99/Iz//+Z4JAt3Sq5iHDwOZyDymX+nCMWmyAKMBgcbN\niGZ9COFU2291LeglJATzd0qpFYi5YB+YnmQdjDuR5MzpSPb7fkS4m4inoRGxciNI1OdzSJbyNutz\njYhlNmxd8x7xI2hhhMEXkp7+ZwwM/B9MswVN68IwelHKTVbWrQwMvIhp1luthtsoKBhm+vT3yMrK\nw+UqHSV8H374YR5++OELnuI/Y6EtCVm0YJpjxypHhkV8vlp8vn6UWolSbSxevJivf/3rfOxje2ho\naOSb33yO7m67euTPEYG+ELFkyhFQeQMifA4jR9oC1s+7iAJJBO5Gqe1kZjrJyChm/vxienuHWbXK\nZO7cC93XoxPvvNidVt3uFWhaDYZh94hwWe9nIhbwOUQJTEdCDOsQa3SPNZ5iazzNwGo0TeF02p19\nZ6DUOZYsycHtdr2PyvsWRGG1EC8dbJ8esXOT9iLejBrAhdvtAs6SlNSOrg+zfPky7rpr/QeWvHwp\ncjhCOJ1pJCaOL0Syfv0atm37IfG8EztJ8ADy7AuQ9bETCO1TPx4EzL6KeH0UMl8PWq+Xo2k+SkpW\nUlYmEd7JnZTJRsBLBQJ87BDwECK3UoifWJqH8N0xa7zziHvGTGSfSD8lSZieSSjURzS6kuHhQ6xY\nobjttkKmTVt1fv0aGhppbU0lK2shra3dEzr+OzYVISrFhYQ8XkfChv8F8ZTZ1ZKnI3xnh+7aEE9z\nJ7AVmetGJOmzHqezkYSEdDQNDMOPYRwgFisCvkM0+gLJyTUUFy9h7twz1Nc343J5cbmCJCWZ+P03\ncsstUtBuYuB9EaIzPsq1MGzDZuI0WYDxK2SH7UJOe2xVSv05whF/OZEbjVUHwzTNWUguxzNKKTud\n+FHrBAlMug7GfuIdDzsQz0MxYnl0Iej+GwioOIHgmDPIBJ9FNvH1iBuuDaUqMM0AwtAdQAcOxxoS\nE28gFDpJOPwuhvEymtZMfv4N3HPPt/jpT1vRtBMkJBQSiRRQVBThC1/43AVN0P6YwvePR+IxyMzM\nGPPdkWGRrVubUcrgnnu+dd7VblsVhmEQCLgQpX0MYY9cJCfiOsT7dByxLGMIDzQgQmAGojTsME0z\nmrYMt/s4JSUeNE1RVOThrrvWjhKgoxPvbkOE/WESE/vo6+tBqnZOQZTRScR7YR97lt4pYlFVIOGH\ngwjo0RCrcxA58ukhJeUImpbDrFmfRtNO8vGPZzB9+tT3UXk3IW73fERBKUR4g33EWKmzCIAbIDNz\nPoWFZWhaFE3bR0GB4o47NnygycuXItNMJRrto61t/J5Al0sRjUr9DpEB1yPzcQQ57pyLgCv7ePM5\n4r2N7NYEich6vo6s9zw8nhQcjnje0eROysxAwgbbkfBtC8Izdp2X/QgYLLSuP4gA3VWI7HMjoYLb\nrOsTUaoMTTuBy9VFQoJGW9s2PJ7ptLdrTJtWdgH/SxO//dTXB3G5TjAwcGHF1YlTK8JHNmBwWr/t\nUGcU8Z5dh4Dwcut57XICldY9Qsh+q8Ph0Fmx4jNoWg5e7y4WLpzH4GAhL77YhM/3DJp2BKUcZGSc\n40tf+iKapll76ToMw+DnP99DZeVzkwDvxxAw9NFM8lRKwzSDTBYqTLYOxg9H/L1DKTUH8bPV2XUs\nJnCvr1zi9S7gDqVUI8L1P1ZKmcD/ME3zJaXU14jXyfhbpVSXaZp7Lv9t9vHABQiz2kdwNiMuuJuB\nTyPu6hCyUV1ILDUbsU6WYyeFzpr1EPX1tcRiorSUCuBy1RAMbiYhoZmEhAixWDmJiRolJflUVDxL\nYaGfSARMsxOXq5FIZKp13GkPX/nKWj772ccmMn0fGrr64ltSHTI7e2yrcuQJAq+3DQiPaa1v376D\nYNDOZ2hFAIXd0bMBUQgBxEvhIW7RtSLCNg1xNe9BCiEl8cADt/LJT36SxsZmBgbSaWhoBC7swzA6\n8e4VBHiarFxp8sorHnR9KfGKgwNIYtpy67pjiPJqt37/DsHcaxDn3Tzrcy8xfXoAj6eIrq4pNDW9\ny+zZPqZPf+h9Vt67Ec+PC8k50pE9VAukkJjYy333raKkpIS8vNzzyabA+SqdHx7QPBNRSONr4FRV\nVUty8iP4fL8hXk31BUSxDSEGRyECAI8i/GUn6tpN66YiSlEMF6XmkZh4PVlZi9C0apqbBexM7qRM\nJQJwkhEwcxYBOonW+6nWe31IvR/dem8fSg2hVC1yiM+DaR7E7V7OwoX34fPtJhR6nf5+D6a5iJSU\nG4lGm0eBnrQ0L6WlM8nMLKavb5i0tJEliyZDZxEwbldRTkTAmo7kLNnF3ey6NXZBrg0I0Ioha2Hi\ncARJTDxDLBZicLCY+fMT+OpX/ytr164lFosxY8ZP2L79bSIRB8uWLT/fLFD2s4zGMIwRgGOi4L3W\nGutHk2bOnEVNzRQ+0GOqF5MprQCbrnjh5MgAHjJNs/Ki1y9XJ+MS9DnEvdaJAIYIcdf4MGJ1/BhR\nNor09OUMDZ1B1/MQJu5DqUwSEurQtGw6OmpwuXSczkV4PKsJh/eQldXD8uVLOHLkAMPDqykuvpfB\nwbcpKhpiwwaT4uIvUl5ezunT1QQC0xkevv1DW7FzfHRtim+5XI9hmpUUFIydDDUysaqk5EHg0t4e\nEZZRRCgZyHrXWb+jiKDKR6xKA1l3P7LuEWSdz5CR0c8jj9zLD3/4Q5xOJ7t27bLc11njcF+7EbCi\nyMrKJT19NoFAAqFQDFE2R4j3HElEFFQ54i1biCgEP/G232lANykpqdxzzz3U1Mxl8eJSKitfYOXK\nzA9AeUcRhZlq/V1ujW86KSkhvvvdJ/iLv/iLP8LJp4lTWlofSg1RUjK++PiCBXP5j/94BvE6rEF4\nqRoBWLcgim4bEoqYhuT2NCJKvwU4ilLNeL0VKNWM2z2L/v5uotEGenpOkp0NZWUSBp3cSZlWJJQ2\njXjuR5k1zuWIVy6G5H1Ms65PQCkfKSn7mT59JjfcsIyWlnOEQlkYhpP09F6mTUvE4VjI4cOJRKMO\nQqFqotFayso+d8G3SxO/JsJhRWGhh2nTysY1r5cm+4RSJmLUdWEf1da0IA6H3/ImHUd4MYzkvrQR\nb//wMRyOA3i9MYLBmTgchcRi7dx4Y8b5OXU6nTz11FM89dRTlx3N1SUXdzDZVucfBnriiS/x3e/+\nnlDIQyx25esvpnEDDKXU18d7rWma/zTxoVz6qxk7gHW5OhljksMxDV33I9ZlPuK+XIwIfJBkrAaU\nUpSWTiczM8DAgJPu7hwMYzmmuZn584+wbNkS9u2rx+d7j2DQxDBmkZ19Bz09nTgc52hpacHhCON0\nliAxuDOUlHDeO3HLLdKS3FZYf+ykt6uja1N8y+PZS1JSHxs2bBjz/fFu8g0bbuOll96lsbEDXe9A\nKb9VeMtOstyHhCFuRgRYOeIZmIm4kjOA00yfnsTPfvZL1q9ff15pTsx9fRtwipSUUyxYMJc339xL\nKBTC4ZhKUtJcgsFhPJ7dhMONxGKPAI9Zc/gCwu59pKZGycrqprPTSSx2HJerg8985hPceecGmpv3\nMDCgMWtWPnfeufYDUOyfQED2bsRqTATCzJ7dy7/+6z+zbt26jwS4AMjJqaegII077hib1y6mjRs3\nsnXrNnbvziManYvw+yEcDmnPLhVaTxNv9maXgO8GykhLa+Vv//aLZGRkMTCwkIqKUxw+7CI1dQrd\n3f1s2JAzZvvy8VJKSiZDQ6mI5X89EuJ7DQH/S4FcPJ5ODGMahrEQXVe43YfIy8vi5ptv5PHHv3Te\nar+wCdk6DGMNur6Lc+eihMNHx6xVcu0LAxYh+7EPsVtDwFlyc7P5q7/aiKZp/PjHv6Wvz0DT5uDx\nmAQCEYLBMIZxDLgbTVtKSkqUQKAch+N2lJqDru/A682cEJ+O1ZRtYnxuV+I9O4HPfHjoySefxOl0\n8u67u9m8eeKfn4gHY7xlwE2kusy1pGesOlyHkNwMk8vXyRiTnM59pKb2kpGRTiBwBqUU3d1hDCMR\npQbIzb2e66//F3p7d3LTTV3MmzePoqIHqays5NSpKhYseIKNGzfy3HObaG+/kUWLPs3bb3+L3t5q\nEhNfpbi4HdPMAVrIzMwhK6sdpV6loMDHhg0PjhrPH6Pd9PtHV1d867bbgqxbd8dVN7Rau3Yt//Zv\nT/PLX/6K+voGenuLCQRK6egoxzC243J1MXPmXBoba4lG52EYATQtGV1vxzDySUi4nvz8GH/912tH\ntY2fmPt6D0qdY8OG288/01tvvc25cwFKSrIZHMxg5crPMjjo56WXmunv30Q0ehqHYxWm6SMzM43E\nxAwKC6eTkVFBQYGD229/jI0bN57P0v9g+aYSCTfNBXSczl4WLLieH/zgKdavX3+Fz37uIDj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5IifMiECjk5ioQEGzfcEH1OzZH+6B84ZLHsZOPGjTidngHR/n70X6g2W1uv8mKx7OTYsWNERcWc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\ndh9m82GamnaTlNQG0OdIpLy8ki9+8ZErnov3N1snJz9KTc327tw0V4Ku6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jiMUprxXZHS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SeKAPXlyDr+GaLOfPTL41Qwf8qb5kLy30Aq0FupEEqlo/VCDKMdkWGrrO91YhiN+P0eDGMq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AM+mX+eCDqvfk+VVDXNPbRUAHt00w0O1fJi0nih0EdbxbkjsA+AZNa1oXRl7NiNMtLTY1\nI24Z+lcicqCY1cqpmEAxN0GjqmsxJc89MVO6ZrMKyRFlwCFiZyn0IoMZYJo6COr4jHTt3SvmSo6H\nh0cLwgsYXy3EMr1V1ZnAlZjjl1mYlcYvQ3kVAndhL4CXseXU5twiybcZcbqlxabGRle2l9yzrweu\nVNUk/wJq7qlHY1tSj0bpzrQi/Blb8ZqDnQPRgfRmgKnq4N8uv/tc/FSX32HNxkn7gve549FsEBvb\nHu0dIvImMF2bQLekudGeePHw8PBor/ArGB4eHh4eHh55hxcwvjpoT0tV7YkXDw8Pj3YJv0Xi4eHh\n4eHhkXf4FQwPDw8PDw+PvMMLGB4eHh4eHh55hxcwPDw8PDw8PPIOL2B4eHh4eHh45B1ewPDw8PDw\n8PDIO7yA4eHh4eHh4ZF3eAHDw8PDw8PDI+/wAoaHh4eHh4dH3uEFDA8PDw8PD4+84/8AVKU2yJks\nxrEAAAAASUVORK5CYII=\n", 159 | "text/plain": [ 160 | "" 161 | ] 162 | }, 163 | "metadata": {}, 164 | "output_type": "display_data" 165 | } 166 | ], 167 | "source": [ 168 | "## 第五課：透過視覺化來了解資料\n", 169 | "\n", 170 | "# Scatter Plot Matrix\n", 171 | "import matplotlib.pyplot as plt\n", 172 | "import pandas\n", 173 | "\n", 174 | "# After pandas 0.22, the plotting is moving to pandas.plotting from pandas.tools.plotting\n", 175 | "# If error occured, try to load plotting from old version.\n", 176 | "try:\n", 177 | "\tfrom pandas.plotting import scatter_matrix\n", 178 | "except Exception as e:\n", 179 | "\tfrom pandas.tools.plotting import scatter_matrix\n", 180 | "\n", 181 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n", 182 | "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", 183 | "data = pandas.read_csv(url, names=names)\n", 184 | "scatter_matrix(data)\n", 185 | "plt.show()" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 2, 191 | "metadata": { 192 | "collapsed": false 193 | }, 194 | "outputs": [ 195 | { 196 | "name": "stdout", 197 | "output_type": "stream", 198 | "text": [ 199 | "[[ 0.64 0.848 0.15 0.907 -0.693 0.204 0.468 1.426]\n", 200 | " [-0.845 -1.123 -0.161 0.531 -0.693 -0.684 -0.365 -0.191]\n", 201 | " [ 1.234 1.944 -0.264 -1.288 -0.693 -1.103 0.604 -0.106]\n", 202 | " [-0.845 -0.998 -0.161 0.155 0.123 -0.494 -0.921 -1.042]\n", 203 | " [-1.142 0.504 -1.505 0.907 0.766 1.41 5.485 -0.02 ]]\n" 204 | ] 205 | } 206 | ], 207 | "source": [ 208 | "## 第六課：針對資料進行前處理，準備進入建模階段\n", 209 | "\n", 210 | "# Standardize data (0 mean, 1 stdev)\n", 211 | "from sklearn.preprocessing import StandardScaler\n", 212 | "import pandas\n", 213 | "import numpy\n", 214 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n", 215 | "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", 216 | "dataframe = pandas.read_csv(url, names=names)\n", 217 | "array = dataframe.values\n", 218 | "# separate array into input and output components\n", 219 | "X = array[:,0:8]\n", 220 | "Y = array[:,8]\n", 221 | "scaler = StandardScaler().fit(X)\n", 222 | "rescaledX = scaler.transform(X)\n", 223 | "# summarize transformed data\n", 224 | "numpy.set_printoptions(precision=3)\n", 225 | "print(rescaledX[0:5,:])" 226 | ] 227 | }, 228 | { 229 | "cell_type": "code", 230 | "execution_count": 2, 231 | "metadata": { 232 | "collapsed": false 233 | }, 234 | "outputs": [ 235 | { 236 | "name": "stdout", 237 | "output_type": "stream", 238 | "text": [ 239 | "Accuracy: 76.951% (4.841%)\n" 240 | ] 241 | } 242 | ], 243 | "source": [ 244 | "## 第七課：透過重複抽樣(Resample method)的方法來進行演算法評估\n", 245 | "\n", 246 | "# Evaluate using Cross Validation\n", 247 | "from pandas import read_csv\n", 248 | "from sklearn.model_selection import KFold\n", 249 | "from sklearn.model_selection import cross_val_scoreß\n", 250 | "from sklearn.linear_model import LogisticRegression\n", 251 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n", 252 | "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", 253 | "dataframe = read_csv(url, names=names)\n", 254 | "array = dataframe.values\n", 255 | "X = array[:,0:8]\n", 256 | "Y = array[:,8]\n", 257 | "kfold = KFold(n_splits=10, random_state=7)\n", 258 | "model = LogisticRegression()\n", 259 | "results = cross_val_score(model, X, Y, cv=kfold)\n", 260 | "print(\"Accuracy: %.3f%% (%.3f%%)\") % (results.mean()*100.0, results.std()*100.0)" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": 3, 266 | "metadata": { 267 | "collapsed": false 268 | }, 269 | "outputs": [ 270 | { 271 | "name": "stdout", 272 | "output_type": "stream", 273 | "text": [ 274 | "Logloss: -0.493 (0.047)\n" 275 | ] 276 | } 277 | ], 278 | "source": [ 279 | "## 第八課：演算法評估指標\n", 280 | "\n", 281 | "# Cross Validation Classification LogLoss\n", 282 | "from pandas import read_csv\n", 283 | "from sklearn.model_selection import KFold\n", 284 | "from sklearn.model_selection import cross_val_score\n", 285 | "from sklearn.linear_model import LogisticRegression\n", 286 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n", 287 | "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", 288 | "dataframe = read_csv(url, names=names)\n", 289 | "array = dataframe.values\n", 290 | "X = array[:,0:8]\n", 291 | "Y = array[:,8]\n", 292 | "kfold = KFold(n_splits=10, random_state=7)\n", 293 | "model = LogisticRegression()\n", 294 | "scoring = 'neg_log_loss'\n", 295 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 296 | "print(\"Logloss: %.3f (%.3f)\") % (results.mean(), results.std())" 297 | ] 298 | }, 299 | { 300 | "cell_type": "code", 301 | "execution_count": 1, 302 | "metadata": { 303 | "collapsed": false 304 | }, 305 | "outputs": [ 306 | { 307 | "name": "stdout", 308 | "output_type": "stream", 309 | "text": [ 310 | "-107.28683898\n" 311 | ] 312 | } 313 | ], 314 | "source": [ 315 | "## 第九課：針對演算法進行抽樣做比較\n", 316 | "\n", 317 | "# KNN Regression\n", 318 | "from pandas import read_csv\n", 319 | "from sklearn.model_selection import KFold\n", 320 | "from sklearn.model_selection import cross_val_score\n", 321 | "from sklearn.neighbors import KNeighborsRegressor\n", 322 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data\"\n", 323 | "names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\n", 324 | "dataframe = read_csv(url, delim_whitespace=True, names=names)\n", 325 | "array = dataframe.values\n", 326 | "X = array[:,0:13]\n", 327 | "Y = array[:,13]\n", 328 | "kfold = KFold(n_splits=10, random_state=7)\n", 329 | "model = KNeighborsRegressor()\n", 330 | "scoring = 'neg_mean_squared_error'\n", 331 | "results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 332 | "print(results.mean())" 333 | ] 334 | }, 335 | { 336 | "cell_type": "code", 337 | "execution_count": 2, 338 | "metadata": { 339 | "collapsed": false 340 | }, 341 | "outputs": [ 342 | { 343 | "name": "stdout", 344 | "output_type": "stream", 345 | "text": [ 346 | "LR: 0.769515 (0.048411)\n", 347 | "LDA: 0.773462 (0.051592)\n" 348 | ] 349 | } 350 | ], 351 | "source": [ 352 | "## 第十課：模型的比較與選擇\n", 353 | "\n", 354 | "# Compare Algorithms\n", 355 | "from pandas import read_csv\n", 356 | "from sklearn.model_selection import KFold\n", 357 | "from sklearn.model_selection import cross_val_score\n", 358 | "from sklearn.linear_model import LogisticRegression\n", 359 | "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", 360 | "# load dataset\n", 361 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n", 362 | "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", 363 | "dataframe = read_csv(url, names=names)\n", 364 | "array = dataframe.values\n", 365 | "X = array[:,0:8]\n", 366 | "Y = array[:,8]\n", 367 | "# prepare models\n", 368 | "models = []\n", 369 | "models.append(('LR', LogisticRegression()))\n", 370 | "models.append(('LDA', LinearDiscriminantAnalysis()))\n", 371 | "# evaluate each model in turn\n", 372 | "results = []\n", 373 | "names = []\n", 374 | "scoring = 'accuracy'\n", 375 | "for name, model in models:\n", 376 | " kfold = KFold(n_splits=10, random_state=7)\n", 377 | " cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", 378 | " results.append(cv_results)\n", 379 | " names.append(name)\n", 380 | " msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n", 381 | " print(msg)" 382 | ] 383 | }, 384 | { 385 | "cell_type": "code", 386 | "execution_count": 1, 387 | "metadata": { 388 | "collapsed": false 389 | }, 390 | "outputs": [ 391 | { 392 | "name": "stdout", 393 | "output_type": "stream", 394 | "text": [ 395 | "0.279617559313\n", 396 | "1.0\n" 397 | ] 398 | } 399 | ], 400 | "source": [ 401 | "## 第十一課：透過演算法調優來改善準確率\n", 402 | "\n", 403 | "# Grid Search for Algorithm Tuning\n", 404 | "from pandas import read_csv\n", 405 | "import numpy\n", 406 | "from sklearn.linear_model import Ridge\n", 407 | "from sklearn.model_selection import GridSearchCV\n", 408 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n", 409 | "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", 410 | "dataframe = read_csv(url, names=names)\n", 411 | "array = dataframe.values\n", 412 | "X = array[:,0:8]\n", 413 | "Y = array[:,8]\n", 414 | "alphas = numpy.array([1,0.1,0.01,0.001,0.0001,0])\n", 415 | "param_grid = dict(alpha=alphas)\n", 416 | "model = Ridge()\n", 417 | "grid = GridSearchCV(estimator=model, param_grid=param_grid)\n", 418 | "grid.fit(X, Y)\n", 419 | "print(grid.best_score_)\n", 420 | "print(grid.best_estimator_.alpha)" 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": 2, 426 | "metadata": { 427 | "collapsed": false 428 | }, 429 | "outputs": [ 430 | { 431 | "name": "stdout", 432 | "output_type": "stream", 433 | "text": [ 434 | "0.765567327409\n" 435 | ] 436 | } 437 | ], 438 | "source": [ 439 | "## 第十二課：透過集成式預測方法 (Ensemble Predictions) 來改善準確率\n", 440 | "\n", 441 | "# Random Forest Classification\n", 442 | "from pandas import read_csv\n", 443 | "from sklearn.model_selection import KFold\n", 444 | "from sklearn.model_selection import cross_val_score\n", 445 | "from sklearn.ensemble import RandomForestClassifier\n", 446 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n", 447 | "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", 448 | "dataframe = read_csv(url, names=names)\n", 449 | "array = dataframe.values\n", 450 | "X = array[:,0:8]\n", 451 | "Y = array[:,8]\n", 452 | "num_trees = 100\n", 453 | "max_features = 3\n", 454 | "kfold = KFold(n_splits=10, random_state=7)\n", 455 | "model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features)\n", 456 | "results = cross_val_score(model, X, Y, cv=kfold)\n", 457 | "print(results.mean())" 458 | ] 459 | }, 460 | { 461 | "cell_type": "code", 462 | "execution_count": 1, 463 | "metadata": { 464 | "collapsed": false 465 | }, 466 | "outputs": [ 467 | { 468 | "name": "stdout", 469 | "output_type": "stream", 470 | "text": [ 471 | "0.755905511811\n" 472 | ] 473 | } 474 | ], 475 | "source": [ 476 | "## 第十三課：完成並保存你的模型\n", 477 | "\n", 478 | "# Save Model Using Pickle\n", 479 | "from pandas import read_csv\n", 480 | "from sklearn.model_selection import train_test_split\n", 481 | "from sklearn.linear_model import LogisticRegression\n", 482 | "import pickle\n", 483 | "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n", 484 | "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", 485 | "dataframe = read_csv(url, names=names)\n", 486 | "array = dataframe.values\n", 487 | "X = array[:,0:8]\n", 488 | "Y = array[:,8]\n", 489 | "test_size = 0.33\n", 490 | "seed = 7\n", 491 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)\n", 492 | "# Fit the model on 33%\n", 493 | "model = LogisticRegression()\n", 494 | "model.fit(X_train, Y_train)\n", 495 | "# save the model to disk\n", 496 | "filename = 'finalized_model.sav'\n", 497 | "pickle.dump(model, open(filename, 'wb'))\n", 498 | "\n", 499 | "# some time later...\n", 500 | "\n", 501 | "# load the model from disk\n", 502 | "loaded_model = pickle.load(open(filename, 'rb'))\n", 503 | "result = loaded_model.score(X_test, Y_test)\n", 504 | "print(result)" 505 | ] 506 | } 507 | ], 508 | "metadata": { 509 | "anaconda-cloud": {}, 510 | "kernelspec": { 511 | "display_name": "Python [conda env:anaconda]", 512 | "language": "python", 513 | "name": "conda-env-anaconda-py" 514 | }, 515 | "language_info": { 516 | "codemirror_mode": { 517 | "name": "ipython", 518 | "version": 2 519 | }, 520 | "file_extension": ".py", 521 | "mimetype": "text/x-python", 522 | "name": "python", 523 | "nbconvert_exporter": "python", 524 | "pygments_lexer": "ipython2", 525 | "version": "2.7.12" 526 | } 527 | }, 528 | "nbformat": 4, 529 | "nbformat_minor": 1 530 | } 531 | --------------------------------------------------------------------------------