本文整理汇总了Python中csv.DictReader.remove方法的典型用法代码示例。如果您正苦于以下问题：Python DictReader.remove方法的具体用法？Python DictReader.remove怎么用？Python DictReader.remove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csv.DictReader的用法示例。
在下文中一共展示了DictReader.remove方法的1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: main
# 需要导入模块: from csv import DictReader [as 别名]
# 或者: from csv.DictReader import remove [as 别名]
def main(args):
voting_methods = ['none', 'hard', 'soft']
assert args.vote in voting_methods, "--vote must be one of 'none', 'hard', 'soft'"
if 'small_yeast_data' in args.data_file:
out_file_name = '../logs/small_yeast_data_log.csv'
if 'large_yeast_data' in args.data_file:
out_file_name = '../logs/large_yeast_data_best_results_log.csv'
if args.classify:
# Store column names as features, except ORF and Essential
all_features = DictReader(open(args.train_file, 'r')).fieldnames
all_features.remove('ORF')
all_features.remove('Essential')
# Cast to list to keep it all in memory
train = list(DictReader(open(args.train_file, 'r')))
test = list(DictReader(open(args.test_file, 'r')))
labels = []
for line in train:
if line[ESSENTIAL] not in labels:
labels.append(int(line[ESSENTIAL]))
train_features = []
for example in train:
train_feat = []
for feature in args.features:
train_feat.append(example[feature])
train_features.append(train_feat)
x_train = np.array(train_features, dtype=float)
test_features = []
for example in test:
test_feature = []
for feature in args.features:
test_feature.append(example[feature])
test_features.append(test_feature)
global orfs
orfs.append(example[ORF])
x_test = np.array(test_features, dtype=float)
y_train = np.array([int(x[ESSENTIAL]) for x in train])
y_test = np.array([int(x[ESSENTIAL]) for x in test])
if args.scale:
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.fit_transform(x_test)
if args.vote == 'none':
for classifier in args.classifiers:
model = __get_classifier_model(classifier, args)
clf = model.fit(x_train, y_train)
print "Using classifier " + classifier
__print_and_log_results(clf, classifier, x_train, x_test, y_test,
out_file_name, all_features, args)
else:
model = __get_classifier_model('none', args)
clf = model.fit(x_train, y_train)
print "Using classifier: vote " + args.vote + " with ", args.classifiers
classifier = "vote-" + args.vote + "-with-classifiers_"
classifier += "_".join(args.classifiers)
__print_and_log_results(clf, classifier, x_train, x_test, y_test,
out_file_name, all_features, args)
elif args.cross_validate:
# Store column names as features, except ORF and Essential
all_features = DictReader(open(args.data_file, 'rU')).fieldnames
all_features.remove('ORF')
all_features.remove('Essential')
# Cast to list to keep it all in memory
data = list(DictReader(open(args.data_file, 'rU')))
labels = []
for line in data:
labels.append(int(line[ESSENTIAL]))
train_features = []
for example in data:
train_feat = []
for feature in args.features:
train_feat.append(example[feature])
train_features.append(train_feat)
x_train = np.array(train_features, dtype=float)
X_train, X_test, y_train, y_test = cross_validation.train_test_split (x_train, labels, test_size=0.1)
if args.scale:
scaler = StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
if args.vote == 'none':
for classifier in args.classifiers:
model = __get_classifier_model(classifier, args)
clf = model.fit(X_train, y_train)
print "Using classifier " + classifier
__print_and_log_results(clf, classifier, X_train, X_test, y_test,
out_file_name, all_features, args)
else:
#.........这里部分代码省略.........