实际上，我正在尝试将培训和部署工作分开，因为编译该程序需要很长时间。
有人建议我使用酱菜堆放和装载，但要分开训练和部署部分。我尝试使用它，但是没有用。
def main():
print "Fetching data..."
train_df = util.get_training_data('../data/training_set_rel3.tsv')
valid_df = util.get_validation_data('../data/valid_set.tsv')
print "Standardizing scores..."
train_df, valid_df = util.append_standardized_column(train_df, valid_df, 'score')
print "Calculating perplexity feature..."
train_df, valid_df = Perplexity().fill_perplexity_columns(train_df, valid_df)
print "Calculating number of sentences feature..."
train_df, valid_df = fill_sentence_column(train_df, valid_df)
print "Cleaning for spelling and word count..."
# cleaned up data for spelling feature
vectorizer_train_spelling = util.vectorizer_clean_spelling(train_df)
train_essays_spelling = vectorizer_train_spelling['essay'].values
vectorizer_valid_spelling = util.vectorizer_clean_spelling(valid_df)
valid_essays_spelling = vectorizer_valid_spelling['essay'].values
print "Calculating total words feature..."
train_df, valid_df = fill_total_words_column(train_df, valid_df, train_essays_spelling, valid_essays_spelling)
print "Calculating unique words feature..."
train_df, valid_df = fill_unique_words_column(train_df, valid_df, train_essays_spelling, valid_essays_spelling)
print "Calculating spelling feature..."
# spelling feature
train_df, valid_df = fill_spelling_column(train_df, valid_df, train_essays_spelling, valid_essays_spelling)
print "Calculating pos tags features..."
train_df, valid_df = fill_pos_columns(train_df, valid_df)
print "Cleaning for TFIDF..."
# cleaned up data for tfidf vector feature
vectorizer_train = util.vectorizer_clean(train_df)
train_essays = vectorizer_train['essay'].values
vectorizer_valid = util.vectorizer_clean(valid_df)
valid_essays = vectorizer_valid['essay'].values
print "Calculating TFIDF features with unigram..."
train_df, valid_df = fill_tfidf_column(train_df, valid_df, train_essays, valid_essays, 1)
# print "Calculating TFIDF features with trigram..."
# train_df, valid_df = fill_tfidf_column(train_df, valid_df, train_essays, valid_essays, 3)
print train_df.head()
print valid_df.head()
COLS = ['essay_set', 'spelling_correct', 'std_sentence_count', 'std_unique_words', 'std_total_words',
'std_unique_words',
'ADJ', 'ADP', 'ADV', 'CONJ', 'DET', 'NOUN', 'NUM', 'PRT', 'PRON', 'VERB', '.', 'X', 'std_perplexity',
'std_score']
train_df = train_df[COLS].join(train_df.filter(regex=("tfidf_*")))
valid_df = valid_df[COLS].join(valid_df.filter(regex=("tfidf_*")))
print train_df.shape
print valid_df.shape
max_essay_set = max(train_df['essay_set'])
linreg_scores_df = pd.DataFrame(columns=['essay_set', 'p', 'spearman'])
lasso_scores_df = pd.DataFrame(columns=['essay_set', 'alpha', 'p', 'spearman'])
ridge_scores_df = pd.DataFrame(columns=['essay_set', 'alpha', 'p', 'spearman'])
alphas = [x * 1.0 / 20 for x in range(20, 0, -1)]
for i in range(1, max_essay_set + 1):
print ""
train_x = np.asarray((train_df[train_df['essay_set'] == i]).drop(['essay_set', 'std_score'], axis=1))
train_std_scores = np.asarray((train_df[train_df['essay_set'] == i])['std_score'], dtype="|S6").astype(np.float)
regr = LinReg(fit_intercept=False, copy_X=False)
regr.fit(train_x, train_std_scores)
valid_x = np.asarray((valid_df[valid_df['essay_set'] == i]).drop(['essay_set', 'std_score'], axis=1))
valid_pred_std_scores = regr.predict(valid_x)
linreg_spear, p = Spearman(a=(valid_df[valid_df['essay_set'] == i])["std_score"], b=valid_pred_std_scores)
linreg_scores_df = linreg_scores_df.append({'essay_set': i, 'p': p, 'spearman': linreg_spear},
ignore_index=True)
print "Linear for Essay Set " + str(i) + ":", linreg_spear
for a in alphas:
ridge = linear_model.Ridge(alpha=a)
ridge.fit(train_x, train_std_scores)
valid_pred_std_scores_ridge = ridge.predict(valid_x)
ridge_spear, p = Spearman(a=(valid_df[valid_df['essay_set'] == i])["std_score"],
b=valid_pred_std_scores_ridge)
ridge_scores_df = ridge_scores_df.append({'essay_set': i, 'alpha': a, 'p': p, 'spearman': ridge_spear},
ignore_index=True)
print "Alpha = " + str(a) + " Ridge for Essay Set " + str(i) + ":", ridge_spear
lasso = linear_model.Lasso(alpha=a)
lasso.fit(train_x, train_std_scores)
valid_pred_std_scores_lasso = lasso.predict(valid_x)
lasso_spear, p = Spearman(a=(valid_df[valid_df['essay_set'] == i])["std_score"],
b=valid_pred_std_scores_lasso)
lasso_scores_df = lasso_scores_df.append({'essay_set': i, 'alpha': a, 'p': p, 'spearman': lasso_spear},
ignore_index=True)
print "Alpha = " + str(a) + "Lasso for Essay Set " + str(i) + ":", lasso_spear
print linreg_scores_df
print ridge_scores_df
print lasso_scores_df
linreg_scores_df.to_pickle('linreg_scores-01.pickle')
ridge_scores_df.to_pickle('ridge_scores-01.pickle')
lasso_scores_df.to_pickle('lasso_scores-01.pickle')
s1 = pickle.dumps(linreg_scores_df)
clf1 = pickle.loads(s)
s2= pickle.dumps(ridge_scores_df)
clf2 = pickle.loads(s)
s3= pickle.dumps(lasso_scores_df)
clf3 = pickle.loads(s)
这不是使用加载和转储的正确方法。运行代码时出现内存错误，并且每次都开始训练。如何分开培训和部署？
答案 0 :(得分：1)
问题：
解决方案：
将数据训练部分放入功能中。
您正在腌制熊猫数据帧，需要使用正确的（熊猫）腌制/装载方法-而不是module pickle的“原始”腌制方法
您可以阅读有关腌制熊猫功能的更多信息（当前0.24.x）here：
original_df = pd.DataFrame({"foo": range(5), "bar": range(5, original_df.to_pickle("./dummy.pkl") unpickled_df = pd.read_pickle("./dummy.pkl") # read pickle print(unpickled_df)输出：
foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9
您正在使用泡菜本身的loads method。它用于腌制作为字符串提供的内容（而不是包含文件名的字符串）。
更改为pandas方法后，它应该可以工作：将（df.to_pickle）处理过的东西放入一个jar（ file ）中，以将其存储在架子上（< em> harddisc ），如果饿了（用于数据处理），则将其取出并打开（pd.read_pickle）并使用。