向量化后无法匹配训练数据特征以匹配标签数据

Question

我有一个学校项目要求我使用机器学习，经过几次故障排除后我遇到了死胡同，不知道如何解决它。

我有这个代码：

db_connection = 'mysql+pymysql://root:@localhost/databases'
conn = create_engine(db_connection)

df = pd.read_sql("SELECT * from barang", conn)

cth_data = pd.DataFrame(df)

#print(cth_data.head())
cth_data = cth_data.dropna()

y = cth_data['kode_aset']
x = cth_data[['merk','ukuran','bahan','harga']]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
clf=RandomForestClassifier(n_estimators=100)


vectorizer = CountVectorizer( max_features = 50000, ngram_range = ( 1,50 ) )

d_feture = vectorizer.fit_transform(x_train)
#d_label = vectorizer.transform(y_train)

clf.fit(d_feture, y_train)
t_data = vectorizer.transform(x_test)

y_pred=clf.predict(t_data)
print ("Model_Accuracy: " + str(np.mean(y_pred == y_test)))

我从mysql数据库中获取了数据这里是数据库：

数据库截图：

以这种错误告终：

File "Machine_learn_V_0.0.1.py", line 41, in <module>
    clf.fit(d_feture, y_train)
  File "C:\Python35\lib\site-packages\sklearn\ensemble\forest.py", line 333, in fit
    for i, t in enumerate(trees))
  File "C:\Python35\lib\site-packages\sklearn\externals\joblib\parallel.py", line 917, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Python35\lib\site-packages\sklearn\externals\joblib\parallel.py", line 759, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Python35\lib\site-packages\sklearn\externals\joblib\parallel.py", line 716, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Python35\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 182, in apply_async
    result = ImmediateResult(func)
  File "C:\Python35\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 549, in __init__
    self.results = batch()
  File "C:\Python35\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in __call__
    for func, args, kwargs in self.items]
  File "C:\Python35\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in <listcomp>
    for func, args, kwargs in self.items]
  File "C:\Python35\lib\site-packages\sklearn\ensemble\forest.py", line 119, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\Python35\lib\site-packages\sklearn\tree\tree.py", line 801, in fit
    X_idx_sorted=X_idx_sorted)
  File "C:\Python35\lib\site-packages\sklearn\tree\tree.py", line 236, in fit
    "number of samples=%d" % (len(y), n_samples))
ValueError: Number of labels=223 does not match number of samples=4

Answer 1

CountVectorizer 接受字符串，它不能像您希望的那样处理列，这意味着您应该将 cth_data[['merk','ukuran','bahan','harga']] 中的字符串连接成一个列，例如：

cols = ['merk','ukuran','bahan','harga']
cth_data['combined'] = cth_data[cols].apply(lambda row: '_'.join(row.values.astype(str)), axis=1)

x = cth_data["combined"]

从那里你的代码应该可以工作

向量化后无法匹配训练数据特征以匹配标签数据

Can not fit training data feature to match label data after Vectorizing

python

machine-learning

vectorization

sklearn-pandas