本文整理汇总了Python中sklearn.preprocessing.RobustScaler.join方法的典型用法代码示例。如果您正苦于以下问题：Python RobustScaler.join方法的具体用法？Python RobustScaler.join怎么用？Python RobustScaler.join使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.preprocessing.RobustScaler的用法示例。
在下文中一共展示了RobustScaler.join方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: RobustScaler
# 需要导入模块: from sklearn.preprocessing import RobustScaler [as 别名]
# 或者: from sklearn.preprocessing.RobustScaler import join [as 别名]
u'people', u'perfect', u'performance', u'performances', u'picture',
u'place', u'played', u'plot', u'point', u'pretty', u'probably',
u'quite', u'read', u'real', u'really', u'reason', u'right', u'role',
u'said', u'saw', u'say', u'scene', u'scenes', u'score', u'screen',
u'script', u'second', u'seeing', u'seen', u'sense', u'set',
u'shows', u'simply', u'special', u'special effects', u'star',
u'star wars', u'start', u'story', u'sure', u'takes', u'thats',
u'theres', u'thing', u'things', u'think', u'thought', u'time',
u'times', u'trilogy', u'true', u'truly', u'trying', u'understand',
u'use', u'used', u'violence', u'want', u'war', u'wars', u'wasnt',
u'watch', u'watched', u'watching', u'way', u'wife', u'wonderful',
u'work', u'world', u'worth', 'year_tfidf', u'years', u'young']
X_prescale = X[features_to_scale]
X_scaled = RobustScaler().fit_transform(X_prescale)
X_scaled = pd.DataFrame(X_scaled, columns = features_to_scale, index = X_prescale.index)
X_final_scaled = X_scaled.join(X[features_to_not_scale])
X_final_scaled.info()
X.info()
#Train Test Split the scaled data
X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled = train_test_split(X_final_scaled, y, test_size = .2, random_state = 31)
#So what is the baseline prediction?
print y.mean()
y.value_counts()
baseline_not10 = (1-y[y== 10].count()/float(y.count()))
'''There are at least two possibilities I can think of for testing with the Classifier:
示例2: RobustScaler
# 需要导入模块: from sklearn.preprocessing import RobustScaler [as 别名]
# 或者: from sklearn.preprocessing.RobustScaler import join [as 别名]
'''
'''Just an FYI that age is a vastly different scale than the rest of the variables.
I am showing the plot and considering scaling it.'''
newdata.age.plot(kind = 'hist', alpha = .3)
#Scaling age and fare.
from sklearn.preprocessing import RobustScaler
X_scaled = RobustScaler().fit_transform(X[['age', 'fare']])
X_scaled = pd.DataFrame(X_scaled, columns = ['age', 'fare'], index = X.index)
#join with rest of Data
X_scaled = X_scaled.join(dummies)
X_scaled = X_scaled.join(X[['sibsp', 'parch']])
X.info()
#Train Test Split on Scaled Data...
X_train_scaled, X_test_scaled, y_train, y_test = train_test_split(X_scaled, y, test_size = .25, stratify = y, random_state = 31)
#Grid Search Logistic regression
from sklearn.cross_validation import StratifiedKFold
grid_lr_scaled = GridSearchCV(lr, logreg_parameters, cv = StratifiedKFold(y_train, n_folds = 5, shuffle = True), n_jobs = -1, verbose = 1)
grid_lr_scaled.fit(X_train_scaled, y_train)
print grid_lr_scaled.best_estimator_
print grid_lr_scaled.best_params_