在此函数中,因子k返回字典a中的值,然后将该值保存在列表中,然后通过Fit_transit将fid_t转换为缩放器来返回列表,但是存在错误。有什么问题吗?
def determineRank2(t, target, bid_t, k):
encode = LabelEncoder()
scaler = MinMaxScaler()
# x = np.concatenate((t,n,bid_t,w,h,k),axis = 1).reshape(1,6,1)
t = categorize_time(t)
bid_t = scaler.fit_transform(bid_t)
a = {'a':0, 'b':1, 'c':2, 'd':3, 'e':4, 'f':5, 'g':6, 'h':7, 'i':8,
'j':9, 'k':10, 'l':11, 'm':12, 'n':13, 'o':14, 'p':15, 'q':16, 'r':17,'w':18,
'x':19, 'y':20, 'z':21, 'ab':22, 'cd':23, 'ef':24, 'gh':25, 'qw':26, 'er':27,
'dz':28, 'df':29}
new_list = []
new_list = [t,target,bid_t,k]
new_list = np.array(new_list)
new_list = new_list.reshape(1, 1, 4)
rank = model.predict(new_list)
rank = round(rank.item(0))
return rank
当我输入这样的值
determineRank2("21:30:04", 3620,2 , "a")
这样的错误显示
<ipython-input-76-cea3690890e8> in determineRank2(t, target, bid_t, k)
11 # x = np.concatenate((t,n,bid_t,w,h,k),axis = 1).reshape(1,6,1)
12 t = categorize_time(t)
---> 13 bid_t = scaler.fit_transform(bid_t)
/usr/local/lib/python3.6/dist-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
551 if y is None:
552 # fit method of arity 1 (unsupervised transformation)
--> 553 return self.fit(X, **fit_params).transform(X)
554 else:
555 # fit method of arity 2 (supervised transformation)
/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py in fit(self, X, y)
323 # Reset internal state before fitting
324 self._reset()
--> 325 return self.partial_fit(X, y)
326
327 def partial_fit(self, X, y=None):
/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py in partial_fit(self, X, y)
351 X = check_array(X, copy=self.copy,
352 estimator=self, dtype=FLOAT_DTYPES,
--> 353 force_all_finite="allow-nan")
354
355 data_min = np.nanmin(X, axis=0)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
512 "Reshape your data either using array.reshape(-1, 1) if "
513 "your data has a single feature or array.reshape(1, -1) "
--> 514 "if it contains a single sample.".format(array))
515 # If input is 1D raise error
516 if array.ndim == 1:
ValueError: Expected 2D array, got scalar array instead:
array=2.0.
Reshape your data either using array.reshape(-1, 1) if your data has a
single feature or array.reshape(1, -1) if it contains a single sample.
我该如何解决?
答案 0 :(得分:0)
如错误消息所述,您应该重塑bid_t
的值。
来自sklearn
的{{3}}:
参数:X:形状为[n_samples,n_features]的numpy数组
您的bid_t
甚至不是数组。
因此,您必须使其看起来像一个数组:
determineRank2("21:30:04", 3620, np.array([[2]]) , "a")