我很难理解数组中哪些维度错误导致其无法正确运行回归模型。
这是到目前为止我尝试过的代码。
x = df_new[["Orig. X", "Orig. Y", "Orig Z", "x (Inches)", "y (Inches)",
"z (Inches)", "Volume (orig. units)"]]
y = df_new["runtime (min)"]
x, y = np.array(X), np.array(y)
print ('Fitting model...')
model = build_model()
x, y = make_regression(n_features=7, n_targets=1)
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)
print(x.shape)
print(y.shape)
model.fit(x,y)
score = model.score(X_test, y_test)
print('Score:', score)
我如何处理x和y以使回归模型顺利运行？ x和y均为100行。 X有7列，而Y当然只有1列。
编辑：下面的build_model（）代码
def build_model():
ridge_transformer = Pipeline(steps=[
('scaler', StandardScaler()),
('poly_feats', PolynomialFeatures()),
('ridge', RidgeTransformer())
])
pred_union = FeatureUnion(
transformer_list=[
('ridge', ridge_transformer),
('rand_forest', RandomForestTransformer()),
('knn', KNeighborsTransformer())
],
n_jobs=2
)
model = Pipeline(steps=[
('pred_union', pred_union),
('lin_regr', LinearRegression())
])
return model
编辑：以下回溯错误
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-127-775526fc779a> in <module>
25 print(x.shape)
26 print(y.shape)
---> 27 model.fit(x,y)
28
29 score = model.score(X_test, y_test)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)
265 Xt, fit_params = self._fit(X, y, **fit_params)
266 if self._final_estimator is not None:
--> 267 self._final_estimator.fit(Xt, y, **fit_params)
268 return self
269
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\linear_model\base.py in fit(self, X, y, sample_weight)
456 n_jobs_ = self.n_jobs
457 X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
--> 458 y_numeric=True, multi_output=True)
459
460 if sample_weight is not None and np.atleast_1d(sample_weight).ndim > 1:
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
754 ensure_min_features=ensure_min_features,
755 warn_on_dtype=warn_on_dtype,
--> 756 estimator=estimator)
757 if multi_output:
758 y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,
~\AppData\Local\Continuum\anaconda3\lib\site-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)
550 "Reshape your data either using array.reshape(-1, 1) if "
551 "your data has a single feature or array.reshape(1, -1) "
--> 552 "if it contains a single sample.".format(array))
553
554 # in the future np.flexible dtypes will be handled like object dtypes
ValueError: Expected 2D array, got 1D array instead:
array=[ 28.51328986 15.04637032 93.37898607 -98.63740483 105.3785245
-46.01294375 -110.93503958 -144.79637899 -59.35923549 62.36778776
-88.85236254 93.0756314 101.64029368 218.01391173 101.26137798
54.72891455 -133.71211185 -125.99472368 29.94269368 71.59822427
212.59784734 -33.55787108 184.89287736 -232.48041924 135.26734855
-12.12651132 -172.7038076 -97.8749558 314.46956576 146.3874269
102.80820968 76.93644443 16.96718666 134.04031743 -102.1777084
-253.68761765 180.2381539 85.20069473 -140.62502367 47.03879138
156.93511131 67.7676657 -9.04138323 123.93189227 174.12355414
-56.19912667 61.63232531 2.04173984 34.86750124 -5.96328448
-126.81900755 -174.96317239 111.01260932 -22.89424944 15.59595122
280.37346561 -101.39356531 -176.98811589 88.49720305 -39.27647122
-25.98321465 -33.27364379 -60.42319159 -48.1694774 -32.0730914
138.79010141 -11.27634536 -74.92271316 -86.59070448 118.17802672
64.50120432 -28.88942322 177.00001615 -75.84108743 -58.35393161
-144.03754366 274.49491635 116.30453855 -123.67954762 -30.89047884
-23.5174034 -1.00726339 -1.88196999 166.40349424 -137.95350454
71.25835091 -64.09838143 -31.63507257 133.10292084 -67.9354037
63.85237459 142.25572131 -108.63072303 -5.7313783 -50.98668871
59.25002692 -13.43424531 -17.82269722 -45.83104936 -148.90728362
1.50193106 17.87438824 81.92662239 -83.19388204 86.83775258
-46.85608104 -84.17690659 -94.8195309 -42.46441727 48.86628343
-86.59104641 98.88486117 65.52270744 190.27987169 86.45217583
31.46603065 -116.71038463 -117.22039948 55.95260753 60.34723681
136.14328873 -66.13663513 170.37393394 -234.74000246 142.4215913
-15.46199011 -143.03607653 -106.87052936 239.01861784 141.20861316
42.36405844 70.85384876 2.41030437 125.38478863 -49.55470155
-175.47492103 146.60430159 83.0396612 -117.86866863 22.03673312
111.04275639 68.28721289 -30.41667672 92.39030512 156.57403451
-39.37562231 79.81213791 -7.6069786 39.83725999 12.14887626
-68.94858082 -142.67522236 115.35903901 -29.70325292 27.43727563
197.58564756 -93.72749402 -129.75046815 46.87218775 -28.13066948
-32.2957201 -32.28477183 -68.89313453 -45.34605254 12.35023878
99.43077668 -4.66376816 -81.9798013 -52.13944475 89.84264889
25.88417774 -63.57461905 192.73937579 -43.27281399 -36.70804327
-140.76773381 242.04620261 115.63767405 -101.22487977 -62.77992477
-44.18638662 2.16920856 -24.62620964 162.50686903 -127.07490443
31.10421595 -57.55304524 -74.66626193 131.84190948 -73.94748475
59.1205745 146.03675545 -109.33328323 -27.49279807 19.15473012
62.80408203 33.36949411 -1.9273022 -51.49779114 -111.2861183
34.06674269 18.05670255 46.43729984 -140.21078294 64.59153281
11.74490721 -57.39114509 -114.55901385 -13.53130554 -13.82323384
-0.49549397 53.19906766 90.57809032 151.70561796 122.69049054
72.98809521 -154.14476693 -74.05733629 32.41234415 50.97790246
116.93388344 -63.94235318 133.64933024 -139.94909392 109.72026197
11.56169254 -138.87962453 -59.10262835 174.53934447 156.64912468
65.03610007 76.4964515 22.04764677 115.84366991 -46.86259747
-161.7681963 158.8401824 6.38833818 -105.66046837 30.75080677
109.00053199 49.33338858 -13.05038717 111.00662647 122.23128993
-40.06503916 71.0503438 -8.47825212 -3.03806969 -11.35250807
-90.35772487 -86.08357992 131.31669463 -7.10243146 65.56581852
177.63529127 -20.92617293 -161.7681963 55.26073005 30.13803328
-8.53039856 -2.46567779 -73.25040534 -21.31245277 -34.32898768
67.07418455 -5.87635605 -70.95576416 -78.20779048 111.04994216
-4.35860823 6.44163718 141.88283904 -55.03298105 -59.06454078
-56.6655874 152.89861136 88.89354733 -32.13338662 10.56350227
-36.24515621 15.19991518 27.09160819 122.94643958 -99.11494519
89.08121089 -50.14558081 -22.37651747 141.88283904 -39.79718785
55.29960662 125.56984308 -74.99872018 -20.25735477 -42.91528289
51.09138403 -8.37011402 6.79676896 -49.05931304 -117.99910408].
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.