from sklearn.linear_model import PassiveAggressiveRegressor from sklearn.datasets import make_regression from importation_pandas import importcsv from sklearn.model_selection import train_test_split setX, setY = importcsv() X_train, X_test, y_train, y_test = train_test_split(setX, setY, test_size=0.01, random_state=42) regr = PassiveAggressiveRegressor(max_iter=100, random_state=0,tol=1e-3) regr.fit(X_train, y_train) #PassiveAggressiveRegressor(C=1.0, average=False, early_stopping=False,epsilon=0.1, fit_intercept=True, loss='epsilon_insensitive', max_iter=100, n_iter_no_change=5, random_state=0,shuffle=True, tol=0.001, validation_fraction=0.1,verbose=0, warm_start=False) print(regr.score(X_test, y_test)) regr.densify() pred = regr.predict(X_test) result=[] for k in range(len(pred)): if pred[k]<0.1: value = 0 else: value =1 if value == y_test[k]: result.append('O') else:
暂时未知 对于算法的具体过程还不是很清楚,所以暂时作为一个黑箱吧 ''' rg = PassiveAggressiveRegressor(C=1.0, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, loss='epsilon_insensitive', epsilon=0.1, random_state=None, warm_start=False) rg.fit(X_train, Y_train) rg.partial_fit(X_train, Y_train) # 增量学习 Y_pre = rg.predict(X_test) rg.score(X_test, Y_test) rg.coef_ rg.intercept_ ''' C 正则化项系数 fit_intercept 是否计算截距 n_iter 迭代次数 shuffle 是否洗牌 verbose 哈 loss 损失函数 epsilon 阈值 random_state 随机器 warm_start=False 新的迭代开始后,是否用上一次的最后结果作为初始化 '''
from sklearn.linear_model import PassiveAggressiveClassifier, PassiveAggressiveRegressor pac = PassiveAggressiveClassifier() pac2 = PassiveAggressiveClassifier() par = PassiveAggressiveRegressor() par2 = PassiveAggressiveRegressor() # Now fit pac.fit(train_bow, train_ratings) par.fit(train_bow, train_ratings) pac2.fit(train_bigram, train_ratings) par2.fit(train_bigram, train_ratings) # Record and desplay results pac_bow_train = pac.score(train_bow, train_ratings) pac_bow_test = pac.score(test_bow, test_ratings) pac_bigram_train = pac2.score(train_bigram, train_ratings) pac_bigram_test = pac2.score(test_bigram, test_ratings) par_bow_train = par.score(train_bow, train_ratings) par_bow_test = par.score(test_bow, test_ratings) par_bigram_train = par2.score(train_bigram, train_ratings) par_bigram_test = par2.score(test_bigram, test_ratings) # pac = par = pac2 = par2 = 1 del pac, par, pac2, par2 # Results print('Passive Aggressive Classifier') print('BOW Results: ' + per(pac_bow_train) + ' training accuracy, ' + per(pac_bow_test) + ' testing accuracy') print('Bigram Results: ' + per(pac_bigram_train) + ' training accuracy, ' + per(pac_bigram_test) + ' testing accuracy') print('Passive Aggressive Regressor') print('BOW Results: ' + per(par_bow_train) + ' training accuracy, ' + per(par_bow_test) + ' testing accuracy') print('Bigram Results: ' + per(par_bigram_test) + ' training accuracy, ' +
#Perceptron Model from sklearn.linear_model import PassiveAggressiveRegressor regr = PassiveAggressiveRegressor(random_state=0, C=1.0, average=False, epsilon=0.1, fit_intercept=True, loss='epsilon_insensitive', max_iter=None, n_iter=None, shuffle=True, tol=None, verbose=0, warm_start=False) regr.fit(X, y) print(regr.score(x_train, y_train)) #Train Error: 32.86 #PassiveAggressiveRegressor() predictions = regr.predict(x_test) for i, prediction in enumerate(predictions): print 'Predicted: %s' % (prediction) ############################################################################################################ #Support Vector Machine Regression from sklearn import svm clf1 = svm.SVR(C=1.0, cache_size=200, coef0=0.0, degree=8, epsilon=0.1, gamma='auto',