本文整理汇总了Python中sklearn.pipeline.Pipeline.stats['recall']方法的典型用法代码示例。如果您正苦于以下问题：Python Pipeline.stats['recall']方法的具体用法？Python Pipeline.stats['recall']怎么用？Python Pipeline.stats['recall']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.pipeline.Pipeline的用法示例。
在下文中一共展示了Pipeline.stats['recall']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: build_psq_classifier
# 需要导入模块: from sklearn.pipeline import Pipeline [as 别名]
# 或者: from sklearn.pipeline.Pipeline import stats['recall'] [as 别名]
#.........这里部分代码省略.........
some basic data metrics.
Note that we only use posts written after 2013-06-25, the date on which
the first such closure reason was instituted.
"""
if end_date_str == None:
ts = time() - 60 * 60 * 24 * 14
end_date_str = from_timestamp(ts)
con = connect_db()
cur = con.cursor()
trf = TfidfVectorizer(
ngram_range=(2,6),
stop_words='english',
analyzer='char',
preprocessor=preprocess_post
)
reg = LogisticRegression()
clf = Pipeline([('vectorizer', trf), ('reg', reg)])
X_raw = []
Y_raw = []
# Fetch closed questions from database
query = """SELECT * FROM questions WHERE creation_date < '{}' AND
closed_reason='off-topic' AND (closed_desc LIKE '%context%'
OR closed_desc LIKE '%homework%');""".format(end_date_str)
cur.execute(query)
for q in cur:
X_raw.append(q['body_html'])
Y_raw.append(1)
num_closed = len(X_raw)
# Fetch an equal number of un-closed questions
query = """SELECT * FROM questions WHERE creation_date < %s AND
closed_reason IS NULL ORDER BY creation_date LIMIT %s"""
cur.execute(query, [end_date_str, num_closed])
for q in cur:
X_raw.append(q['body_html'])
Y_raw.append(0)
X_raw = [X_raw[i] for i in shuff]
Y_raw = [Y_raw[i] for i in shuff]
# Hold back 20% of examples as test set
X_train, X_test, Y_train, Y_test = train_test_split(
X_raw, Y_raw, test_size=0.2)
test_size = len(X_test)
train_size = len(X_train)
# Perform grid search to tune parameters for F1-score
params = [
{
'vectorizer__ngram_range': [(2,2), (2,4), (2,6), (2,8)],
'reg__penalty': ['l1', 'l2'],
'reg__C': [.01, .03, .1, .3, 1, 3, 10, 30, 100],
'reg__intercept_scaling': [.1,1,10,100]
}
]
gridsearch = GridSearchCV(clf, params, scoring='f1', n_jobs=4, \
pre_dispatch=8)
gridsearch.fit(X_train, Y_train)
clf = gridsearch.best_estimator_
print("Done training classifier!")
print("Parameters from CV:")
for k,v in gridsearch.best_params_.items():
print("{}: {}".format(k,v))
preds = clf.predict(X_test)
print("Done making predictions for test set.")
print("Results:")
clf.stats = dict()
clf.stats['train_size'] = train_size
clf.stats['train_pos'] = np.sum(Y_train)
clf.stats['train_neg'] = train_size - np.sum(Y_train)
clf.stats['test_size'] = test_size
clf.stats['test_pos'] = np.sum(Y_test)
clf.stats['test_neg'] = test_size - np.sum(Y_test)
clf.stats['accuracy'] = clf.score(X_test, Y_test)
clf.stats['precision'] = precision_score(Y_test, preds)
clf.stats['recall'] = recall_score(Y_test, preds)
for k in clf.stats:
print(" {}: {}".format(k, clf.stats[k]))
with open('psq.pickle', 'wb') as f:
pickle.dump(clf, f)