Question

我有一个想要分类为A或B的图像。为此，我将它们加载并调整大小为160x160，然后将2D数组转换为1D并将它们添加到pandas数据帧中：

from pandas import DataFrame
from scipy.misc import imread, imresize
rows = []
for product in products:
    try:
        relevant = product.categoryrelevant.all()[0].relevant
    except IndexError:
        relevant = False
    if relevant:
        relevant = "A"
    else:
        relevant = "B"
    # this exists for all pictures
    image_array = imread("{}/{}".format(MEDIA_ROOT, product.picture_file.url))
    image_array = imresize(image_array, (160, 160))
    image_array = image_array.reshape(-1)
    print(image_array)
    # [254 254 252 ..., 255 255 253]
    print(image_array.shape)
    # (76800,)
    rows.append({"id": product.pk, "image": image_array, "class": relevant})
    index.append(product)
df = DataFrame(rows, index=index)

我想要的不仅仅是以后用于分类的图像（例如，产品描述），所以我使用的是具有FeatureUnion的管道（即使它现在只有图像）。 ItemSelector取自这里：

http://scikit-learn.org/stable/auto_examples/hetero_feature_union.html

它采用“图像”列中的值。或者，可以执行train_X = df.iloc[train_indices]["image"].values，但我想稍后添加其他列。

def randomforest_image_pipeline():
    """Returns a RandomForest pipeline."""
    return Pipeline([
        ("union", FeatureUnion(
            transformer_list=[
                ("image", Pipeline([
                    ("selector", ItemSelector(key="image")),
                ]))
            ],
            transformer_weights={
                "image": 1.0
            },
        )),
        ("classifier", RandomForestClassifier()),
    ])

然后用KFold分类：

from sklearn.model_selection import KFold
kfold(tested_pipeline=randomforest_image_pipeline(), df=df)
def kfold(tested_pipeline=None, df=None, splits=6):
    k_fold = KFold(n_splits=splits)
    for train_indices, test_indices in k_fold.split(df):
        # training set
        train_X = df.iloc[train_indices]
        train_y = df.iloc[train_indices]['class'].values
        # test set
        test_X = df.iloc[test_indices]
        test_y = df.iloc[test_indices]['class'].values
        for val in train_X["image"]:
            print(len(val), val.dtype, val.shape)
            # 76800 uint8 (76800,) for all
        tested_pipeline.fit(train_X, train_y) # crashes in this call
        pipeline_predictions = tested_pipeline.predict(test_X)
        ...

但对于.fit，我收到以下错误：

Traceback (most recent call last):
  File "<path>/project/classifier/classify.py", line 362, in <module>
    best = best_pipeline(dataframe=data, f1_scores=f1_dict, get_fp=True)
  File "<path>/project/classifier/classify.py", line 351, in best_pipeline
    confusion_list=confusion_list, get_fp=get_fp)
  File "<path>/project/classifier/classify.py", line 65, in kfold
    tested_pipeline.fit(train_X, train_y)
  File "/usr/local/lib/python3.5/dist-packages/sklearn/pipeline.py", line 270, in fit
    self._final_estimator.fit(Xt, y, **fit_params)
  File "/usr/local/lib/python3.5/dist-packages/sklearn/ensemble/forest.py", line 247, in fit
    X = check_array(X, accept_sparse="csc", dtype=DTYPE)
  File "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py", line 382, in check_array
    array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: setting an array element with a sequence.

我发现其他人有同样的问题，对他们来说问题是他们的行长度不一样。这对我来说似乎并非如此，因为所有行都是1维的，长度为76800：

    for val in train_X["image"]:
        print(len(val), val.dtype, val.shape)
        # 76800 uint8 (76800,) for all

崩溃行中的

array看起来像这样（从调试器中复制）：

[array([ 255.,  255.,  255., ...,  255.,  255.,  255.])
 array([ 255.,  255.,  255., ...,  255.,  255.,  255.])
 array([ 255.,  255.,  255., ...,  255.,  255.,  255.]) ...,
 array([ 255.,  255.,  255., ...,  255.,  255.,  255.])
 array([ 255.,  255.,  255.

我该怎么做才能解决这个问题？

Answer 1

错误是因为您要保存图像的所有数据，即列表中的76800个要素，该列表将保存到dataFrame的单个列中。

因此，当您使用ItemSelector选择该列时，其输出将是形状(Train_len, )的单维数组。 FeatureUnion或后续估算器无法看到76800的内部维度。

更改ItemSelector的transform()函数以返回具有形状（Train_len，76800）的正确2维数据数组。只有这样才能奏效。

更改为：

def transform(self, data_dict):
    return np.array([np.array(x) for x in data_dict[self.key]])

随意询问是否有任何理解。

用于sklearn管道中分类的图像数组 - ValueError：使用序列设置数组元素

1 个答案: