Scikit-Learn 备忘录
Scikit-Learn 备忘录整理自Scikit_Learn_Cheat_Sheet_Python,归属于笔者的程序猿的数据科学与机器学习实战手册,前置阅读 Python语法速览与机器学习开发环境搭建。
Scikit-Learn
Scikit-learn是开源的Python机器学习库,提供了数据预处理、交叉验证、算法与可视化算法等一系列接口。
Basic Example:基本用例
>>> from sklearn import neighbors, datasets, preprocessing >>> from sklearn.cross_validation import train_test_split >>> from sklearn.metrics import accuracy_score >>> iris = datasets.load_iris() >>> X, y = iris.data[:, :2], iris.target >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33) >>> scaler = preprocessing.StandardScaler().fit(X_train) >>> X_train = scaler.transform(X_train) >>> X_test = scaler.transform(X_test) >>> knn = neighbors.KNeighborsClassifier(n_neighbors=5) >>> knn.fit(X_train, y_train) >>> y_pred = knn.predict(X_test) >>> accuracy_score(y_test, y_pred)
数据加载与切分
我们一般使用NumPy中的数组或者Pandas中的DataFrame等数据结构来存放数据:
>>> import numpy as np >>> X = np.random.random((10,5)) >>> y = np.array(['M','M','F','F','M','F','M','M','F','F','F']) >>> X[X < 0.7] = 0
NumPy还提供了方便的接口帮我们划分训练数据与测试数据:
>>> from sklearn.cross_validation import train_test_split >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
Model:模型
模型创建
监督学习
Linear Regression
>>> from sklearn.linear_model import LinearRegression >>> lr = LinearRegression(normalize=True)
Support Vector Machines
>>> from sklearn.svm import SVC >>> svc = SVC(kernel='linear')
Naive Bayes
>>> from sklearn.naive_bayes import GaussianNB >>> gnb = GaussianNB()
KNN
>>> from sklearn import neighbors >>> knn = neighbors.KNeighborsClassifier(n_neighbors=5)
无监督学习
Principal Component Analysis
>>> from sklearn.decomposition import PCA >>> pca = PCA(n_components=0.95)
KMeans
>>> from sklearn.cluster import KMeans >>> k_means = KMeans(n_clusters=3, random_state=0)
模型拟合
有监督学习
>>> lr.fit(X, y) >>> knn.fit(X_train, y_train) >>> svc.fit(X_train, y_train)
无监督学习
>>> k_means.fit(X_train) >>> pca_model = pca.fit_transform(X_train)
模型预测
有监督预测
>>> y_pred = svc.predict(np.random.random((2,5))) >>> y_pred = lr.predict(X_test) >>> y_pred = knn.predict_proba(X_test)
无监督预测
>>> y_pred = k_means.predict(X_test)
模型评估
分类度量
Accuracy Scope
>>> knn.score(X_test, y_test) >>> from sklearn.metrics import accuracy_score >>> accuracy_score(y_test, y_pred)
Classification Report
>>> from sklearn.metrics import classification_report >>> print(classification_report(y_test, y_pred))
Confusion Matrix
>>> from sklearn.metrics import confusion_matrix >>> print(confusion_matrix(y_test, y_pred))
回归度量
Mean Absolute Error
>>> from sklearn.metrics import mean_absolute_error >>> y_true = [3, -0.5, 2] >>> mean_absolute_error(y_true, y_pred)
Mean Squared Error
>>> from sklearn.metrics import mean_squared_error >>> mean_squared_error(y_test, y_pred)
R2 Score
>>> from sklearn.metrics import r2_score >>> r2_score(y_true, y_pred)
聚类度量
Adjusted Rand Index
>>> from sklearn.metrics import adjusted_rand_score >>> adjusted_rand_score(y_true, y_pred)
Homogeneity
>>> from sklearn.metrics import homogeneity_score >>> homogeneity_score(y_true, y_pred)
V-measure
>>> from sklearn.metrics import v_measure_score >>> metrics.v_measure_score(y_true, y_pred)
交叉验证
>>> from sklearn.cross_validation import cross_val_score >>> print(cross_val_score(knn, X_train, y_train, cv=4)) >>> print(cross_val_score(lr, X, y, cv=2))
数据预处理
标准化
>>> from sklearn.preprocessing import StandardScaler >>> scaler = StandardScaler().fit(X_train) >>> standardized_X = scaler.transform(X_train) >>> standardized_X_test = scaler.transform(X_test)
归一化
>>> from sklearn.preprocessing import Normalizer >>> scaler = Normalizer().fit(X_train) >>> normalized_X = scaler.transform(X_train) >>> normalized_X_test = scaler.transform(X_test)
二值化
>>> from sklearn.preprocessing import Binarizer >>> binarizer = Binarizer(threshold=0.0).fit(X) >>> binary_X = binarizer.transform(X)
类条件编码
>>> from sklearn.preprocessing import LabelEncoder >>> enc = LabelEncoder() >>> y = enc.fit_transform(y)
缺失值推导
>>> from sklearn.preprocessing import Imputer >>> imp = Imputer(missing_values=0, strategy='mean', axis=0) >>> imp.fit_transform(X_train)
多项式属性生成
>>> from sklearn.preprocessing import PolynomialFeatures >>> poly = PolynomialFeatures(5) >>> poly.fit_transform(X)
模型调优
Grid Search
>>> from sklearn.grid_search import GridSearchCV >>> params = {"n_neighbors": np.arange(1,3), "metric": ["euclidean", "cityblock"]} >>> grid = GridSearchCV(estimator=knn, param_grid=params) >>> grid.fit(X_train, y_train) >>> print(grid.best_score_) >>> print(grid.best_estimator_.n_neighbors)
Randomized Parameter Optimization
>>> from sklearn.grid_search import RandomizedSearchCV >>> params = {"n_neighbors": range(1,5), "weights": ["uniform", "distance"]} >>> rsearch = RandomizedSearchCV(estimator=knn, param_distributions=params, cv=4, n_iter=8, random_state=5) >>> rsearch.fit(X_train, y_train) >>> print(rsearch.best_score_)
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