├── requirements.txt ├── saved-stock-pipeline.PyData ├── plots ├── performance-simple-linreg.png └── performance-more-variables.png ├── digits-knn.py ├── digits-linear-regression.py ├── iris-pipeline.py ├── README.md ├── .gitignore ├── timeseriesutil.py ├── stock-model.py └── LICENSE /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | scipy 3 | matplotlib 4 | pandas 5 | cairocffi 6 | joblib 7 | sklearn 8 | -------------------------------------------------------------------------------- /saved-stock-pipeline.PyData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/real-time-machine-learning/2-scikit-learn-intro/HEAD/saved-stock-pipeline.PyData -------------------------------------------------------------------------------- /plots/performance-simple-linreg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/real-time-machine-learning/2-scikit-learn-intro/HEAD/plots/performance-simple-linreg.png -------------------------------------------------------------------------------- /plots/performance-more-variables.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/real-time-machine-learning/2-scikit-learn-intro/HEAD/plots/performance-more-variables.png -------------------------------------------------------------------------------- /digits-knn.py: -------------------------------------------------------------------------------- 1 | # 这里我们用K-近邻估计来判断手写数字的扫描图像来判断数字是多少。 2 | 3 | from sklearn import datasets 4 | from sklearn.neighbors import KNeighborsClassifier 5 | from sklearn.model_selection import train_test_split 6 | 7 | # 导入数据 8 | digits = datasets.load_digits() 9 | X_digits = digits.data 10 | y_digits = digits.target 11 | 12 | n_samples = len(X_digits) 13 | 14 | # 拆分训练集和测试集 15 | X_train, X_test, y_train, y_test = train_test_split( 16 | X_digits, 17 | y_digits, 18 | test_size=0.1) 19 | 20 | model = KNeighborsClassifier() 21 | 22 | # 训练模型 23 | model.fit(X_train, y_train) 24 | 25 | # 进行预测 26 | prediction = model.predict(X_test) 27 | 28 | score = model.score(X_test, y_test) 29 | print(score) 30 | -------------------------------------------------------------------------------- /digits-linear-regression.py: -------------------------------------------------------------------------------- 1 | # 这里我们用逻辑回归来判断手写数字的扫描图像来判断数字是多少。 2 | 3 | from sklearn import datasets 4 | from sklearn.linear_model import LogisticRegression 5 | from sklearn.model_selection import train_test_split 6 | 7 | # 导入数据 8 | digits = datasets.load_digits() 9 | X_digits = digits.data 10 | y_digits = digits.target 11 | 12 | n_samples = len(X_digits) 13 | 14 | # 拆分训练集和测试集 15 | 16 | # 这里因为我们的测试集和训练集不存在时间先后关系，所以可以使用Scikit 17 | # Learn自带的 train_test_split 函数自动化拆分数据集 18 | 19 | X_train, X_test, y_train, y_test = train_test_split( 20 | X_digits, 21 | y_digits, 22 | test_size=0.1) 23 | 24 | model = LogisticRegression() 25 | 26 | # 训练模型 27 | model.fit(X_train, y_train) 28 | 29 | # 进行预测 30 | prediction = model.predict(X_test) 31 | 32 | score = model.score(X_test, y_test) 33 | print(score) 34 | -------------------------------------------------------------------------------- /iris-pipeline.py: -------------------------------------------------------------------------------- 1 | 2 | ## 这里我们利用体征数据对Iris鲜花分类数据进行研究，由于其含有多项自变量 3 | ## 需要预处理，我们通过Pipeline 模块进行整合，简化流程。 4 | 5 | from sklearn.pipeline import Pipeline, FeatureUnion 6 | from sklearn.svm import SVC 7 | from sklearn.datasets import load_iris 8 | from sklearn.decomposition import PCA 9 | from sklearn.feature_selection import SelectKBest 10 | from sklearn.externals import joblib 11 | 12 | ## 准备Iris鲜花数据 13 | iris = load_iris() 14 | X, y = iris.data, iris.target 15 | 16 | """ 对数据进行预处理 17 | """ 18 | ## 我们发现大量自变量是高度相关的，所以用主成分分析的方法提取最显著的两 19 | ## 个主成分进行预测。 20 | pca = PCA(n_components=2) 21 | 22 | ## 同时我们也希望用已有的变量直接进行预测，这里我们选取预测效果最好的一 23 | ## 个进行预测。 24 | selection = SelectKBest(k=1) 25 | 26 | ## 这里我们将前面的变量整合起来 27 | combined_features = FeatureUnion([("pca", pca), 28 | ("univ_select", selection)]) 29 | 30 | ## 这里是最后起到分类作用的SVM 分类器模块 31 | svm = SVC(kernel="linear") 32 | 33 | """ 最后把所有模块整合起来，形成一个pipeline对象 34 | """ 35 | pipeline = Pipeline([("features", combined_features), ("svm", svm)]) 36 | 37 | ## 对pipeline对象进行训练。 38 | pipeline.fit(X,y) 39 | 40 | ## 利用训练好的pipeline对象进行预测 41 | pipeline.predict(X) 42 | 43 | """ pipeline对象可以被储存起来，用于后续工作 44 | """ 45 | joblib.dump(pipeline, 46 | 'iris-pipeline.pkl') 47 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 利用Scikit Learn机器学习模块进行建模和预测 2 | 3 | 这里我们学习Scikit Learn的安装和基本操作，并且尝试通过Scikit Learn对秒 4 | 级股票价格数据进行预测。 5 | 6 | ## 下载本章实例程序 7 | 8 | 下载本章节实例程序和数据，只需执行下面操作： 9 | 10 | ```shell 11 | git clone https://github.com/real-time-machine-learning/2-scikit-learn-intro 12 | ``` 13 | 14 | ## 安装配置软件环境 15 | 16 | 我们假设读者在Ubuntu或者Mac环境下进行学习。下面的步骤可以供Windows用户 17 | 参考，但可能需要稍作修改。 18 | 19 | ### 安装Python3 20 | 21 | 在Ubuntu 下面安装Python 3，只需执行下面操作： 22 | ```shell 23 | sudo apt-get install python3 python3-pip python3-dev build-essential libffi6 libffi-dev 24 | ``` 25 | 在Mac下利用Homebrew 安装Python 3，只需执行下面操作： 26 | ```shell 27 | brew install python3 28 | ``` 29 | Windows用户……安装一下Ubuntu好吗？ 30 | 31 | ### 安装Scikit Learn 32 | 33 | 这里我们通过Python的Pip配置文件的方法安装Scikit Learn。在后面的Docker学 34 | 习中，我们可以看到这样的配置方法非常利于自动化Docker操作。 35 | 36 | ```shell 37 | sudo pip3 install -r requirements.txt 38 | ``` 39 | 40 | 如果一切顺利，上面操作完成以后，我们可以启动Python3并且调用Pandas 41 | ```shell 42 | python3 43 | >>> import sklearn 44 | ``` 45 | 46 | ## Scikit Learn基本操作 47 | 48 | 本章具有多个实例模块： 49 | 50 | * `digits-knn.py`: 使用K-最近邻估计对扫描数字数据进行分类 51 | * `digits-linear-regression.py`: 使用逻辑回归对扫描数字数据进行分类 52 | * `iris-pipeline.py`: 使用pipeline对Iris鲜花数据进行分类 53 | * `stock-model.py`: 使用Scikit Learn对股价变化进行预测建模 54 | 55 | ## 鸣谢 56 | 57 | 感谢下面各位为本代码提出宝贵意见和指正： 58 | 59 | - [张野飞](https://github.com/351zyf) 60 | 61 | --- 62 | 《实时机器学习实战》 彭河森、汪涵 63 | -------------------------------------------------------------------------------- /.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 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | -------------------------------------------------------------------------------- /timeseriesutil.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | import pandas as pd 4 | import numpy as np 5 | from sklearn.base import BaseEstimator, TransformerMixin 6 | 7 | """数据整理 8 | 9 | Scikit learn 的preprocessing 模块目前只能将X, Y形态的数据整理成为需要的 10 | 形似。这里我们需要对时间序列数据进行重拍，将其整理成为X, y形式。 11 | 12 | 对于每一个观测，因变量y代表当前秒的变化率，自变量X代表前几秒的变化率和 13 | 成交量变化情况。 14 | 15 | 这里有两个办法进行这样的处理： 16 | 17 | 1) 通过for 循环，将观测逐个加入到X, y矩阵和向量中。这种方法短平快，但是 18 | 程序可重复利用率较低。 19 | 20 | 2) 撰写preprocessing 模块，通过preprocessing 模块的fit/transform模式对 21 | 数据进行转换。这样的方法看似麻烦，但是代码可重复利用率高，可以为后面操 22 | 作节省很多工作。 23 | 24 | """ 25 | 26 | def embed_time_series(x, k): 27 | """this function would transform an N dimensional time series into a 28 | tuple containing: 29 | 30 | 1) an (n - k) by k matrix that is [X[i], x[i+1], ... x[i+k-1]], 31 | for i from 0 to n-k-1 32 | 33 | 2) a vector of length (n - k) that is [x[k], x[k+1] ... x[n]] 34 | """ 35 | n = len(x) 36 | 37 | if k >= n: 38 | raise "Can not deal with k greater than the length of x" 39 | 40 | output_x = list(map(lambda i: list(x[i:(i+k)]), 41 | range(0, n-k))) 42 | return np.array(output_x) 43 | 44 | class TimeSeriesEmbedder(BaseEstimator, TransformerMixin): 45 | def __init__(self, k): 46 | self.k = k 47 | def fit(self, X, y= None): 48 | return self 49 | def transform(self, X, y = None): 50 | return embed_time_series(X, self.k) 51 | 52 | class ColumnExtractor(BaseEstimator, TransformerMixin): 53 | def __init__(self, column_name): 54 | self.column_name = column_name 55 | def fit(self, X, y=None): 56 | return self 57 | def transform(self, X, y=None): 58 | return X[self.column_name] 59 | 60 | class TimeSeriesDiff(BaseEstimator, TransformerMixin): 61 | def __init__(self, k=1): 62 | self.k = k 63 | def fit(self, X, y=None): 64 | return self 65 | def transform(self, X, y=None): 66 | if type(X) is pd.core.frame.DataFrame or type(X) is pd.core.series.Series: 67 | return X.diff(self.k) / X.shift(self.k) 68 | else: 69 | raise "Have to be a pandas data frame or Series object!" 70 | -------------------------------------------------------------------------------- /stock-model.py: -------------------------------------------------------------------------------- 1 | 2 | """《实时机器学习实战》　彭河森、汪涵 3 | 4 | 机器学习工具Scikit Learn介绍 5 | 6 | 这里我们将利用Scikit Learn对苹果公司的秒级股票报价进行建模，并以此为例 7 | 熟悉Scikit Learn的各个模块。 8 | """ 9 | 10 | import pandas as pd 11 | 12 | import numpy as np 13 | 14 | from sklearn.preprocessing import Imputer, PolynomialFeatures 15 | from sklearn.pipeline import Pipeline, FeatureUnion 16 | from sklearn.linear_model import LinearRegression 17 | from sklearn.metrics import r2_score, median_absolute_error 18 | from timeseriesutil import TimeSeriesDiff, TimeSeriesEmbedder, ColumnExtractor 19 | 20 | import matplotlib.pyplot as plt 21 | import matplotlib 22 | 23 | matplotlib.style.use('ggplot') 24 | plt.xticks(rotation=70) 25 | 26 | """数据导入 27 | 28 | 这里我们导入经过了预处理的苹果公司股票报价，该数据仅包含正常交易时间的 29 | 数据。 30 | """ 31 | 32 | data = pd.read_csv("aapl-trading-hour.csv", 33 | index_col = 0) 34 | 35 | y = data["Close"].diff() / data["Close"].shift() 36 | 37 | y[np.isnan(y)]=0 38 | 39 | n_total = data.shape[0] 40 | n_train = int(np.ceil(n_total*0.7)) 41 | 42 | data_train = data[:n_train] 43 | data_test = data[n_train:] 44 | 45 | y_train = y[10:n_train] 46 | y_test = y[(n_train+10):] 47 | 48 | """ 利用Pipeline实现建模 49 | """ 50 | 51 | pipeline = Pipeline([("ColumnEx", ColumnExtractor("Close")), 52 | ("Diff", TimeSeriesDiff()), 53 | ("Embed", TimeSeriesEmbedder(10)), 54 | ("ImputerNA", Imputer()), 55 | ("LinReg", LinearRegression())]) 56 | 57 | pipeline.fit(data_train, y_train) 58 | y_pred = pipeline.predict(data_test) 59 | 60 | """ 查看并评价结果 61 | """ 62 | 63 | print(r2_score(y_test, y_pred)) 64 | print(median_absolute_error(y_test, y_pred)) 65 | 66 | cc = np.sign(y_pred)*y_test 67 | cumulative_return = (cc+1).cumprod() 68 | cumulative_return.plot(rot=10) 69 | plt.savefig("plots/performance-simple-linreg.png") 70 | # plt.show() 71 | 72 | """更复杂的Pipeline 73 | 74 | 我们试图将成交量也纳入考虑，所以需要进行多个pipeline的融合。 75 | 同时，我们试图引入多远交互项，以考虑非线性相关关系。 76 | """ 77 | 78 | pipeline_closing_price = Pipeline([("ColumnEx", ColumnExtractor("Close")), 79 | ("Diff", TimeSeriesDiff()), 80 | ("Embed", TimeSeriesEmbedder(10)), 81 | ("ImputerNA", Imputer())]) 82 | 83 | pipeline_volume = Pipeline([("ColumnEx", ColumnExtractor("Volume")), 84 | ("Diff", TimeSeriesDiff()), 85 | ("Embed", TimeSeriesEmbedder(10)), 86 | ("ImputerNA", Imputer())]) 87 | 88 | merged_features = FeatureUnion([("ClosingPriceFeature", pipeline_closing_price), 89 | ("VolumeFeature", pipeline_volume)]) 90 | 91 | pipeline_2 = Pipeline([("MergedFeatures", merged_features), 92 | ("PolyFeature",PolynomialFeatures()), 93 | ("LinReg", LinearRegression())]) 94 | pipeline_2.fit(data_train, y_train) 95 | 96 | y_pred_2 = pipeline_2.predict(data_test) 97 | 98 | print(r2_score(y_test, y_pred_2)) 99 | print(median_absolute_error(y_test, y_pred_2)) 100 | 101 | cc_2 = np.sign(y_pred_2)*y_test 102 | cumulative_return_2 = (cc_2+1).cumprod() 103 | cumulative_return_2.plot(style="k--", rot=10) 104 | plt.savefig("plots/performance-more-variables.png") 105 | # plt.show() 106 | 107 | """ 预测运行时间有多长? 108 | """ 109 | 110 | import time 111 | start_time = time.clock() 112 | pipeline_2.predict(data_test[1:20]) 113 | print(time.clock() - start_time, "seconds") 114 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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