mfrezap
commented
1 year ago I got the same issue,
ValueError: Expected 2D array, got 1D array instead:
Open lcwheeler opened 1 year ago
mfrezap
commented
1 year ago I got the same issue,
ValueError: Expected 2D array, got 1D array instead:
I am attempting to run through the very simple MBPLS example from the Docs page. I'm running the code exactly as shown below and getting an error in the mbpls.plot() function. Not sure if this might be an issue of compatibility with the version of a dependency? Thanks!
`import numpy as np from mbpls.mbpls import MBPLS
num_samples = 40 num_features_x1 = 200 num_features_x2 = 250
Generate two random data matrices X1 and X2 (two blocks)
x1 = np.random.rand(num_samples, num_features_x1) x2 = np.random.rand(num_samples, num_features_x2)
Generate random reference vector y
y = np.random.rand(num_samples, 1)
Establish prediction model using 3 latent variables (components)
mbpls = MBPLS(n_components=3) mbpls.fit([x1, x2],y) y_pred = mbpls.predict([x1, x2])
Use built-in plot method for exploratory analysis of multiblock pls models
mbpls.plot(num_components=3)
Environment (please complete the following information):
Additional information
Error message is as follows:
ValueError Traceback (most recent call last) Cell In[41], line 2 1 # Use built-in plot method for exploratory analysis of multiblock pls models ----> 2 mbpls.plot(num_components=3)
File ~/mambaforge/envs/py310_env/lib/python3.10/site-packages/mbpls/mbpls.py:1483, in MBPLS.plot(self, num_components) 1480 for block in range(self.numblocks): 1481 # Inverse transforming weights/loadings 1482 if self.standardize: -> 1483 P_inv_trans.append(self.xscalers[block].inversetransform(self.P[block][:, comp])) 1484 else: 1485 P_invtrans.append(self.P[block][:, comp])
File ~/mambaforge/envs/py310_env/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:1034, in StandardScaler.inverse_transform(self, X, copy) 1031 check_is_fitted(self) 1033 copy = copy if copy is not None else self.copy -> 1034 X = check_array( 1035 X, 1036 accept_sparse="csr", 1037 copy=copy, 1038 dtype=FLOAT_DTYPES, 1039 force_all_finite="allow-nan", 1040 ) 1042 if sparse.issparse(X): 1043 if self.with_mean:
File ~/mambaforge/envs/py310_env/lib/python3.10/site-packages/sklearn/utils/validation.py:902, 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, estimator, input_name) 900 # If input is 1D raise error 901 if array.ndim == 1: --> 902 raise ValueError( 903 "Expected 2D array, got 1D array instead:\narray={}.\n" 904 "Reshape your data either using array.reshape(-1, 1) if " 905 "your data has a single feature or array.reshape(1, -1) " 906 "if it contains a single sample.".format(array) 907 ) 909 if dtype_numeric and array.dtype.kind in "USV": 910 raise ValueError( 911 "dtype='numeric' is not compatible with arrays of bytes/strings." 912 "Convert your data to numeric values explicitly instead." 913 )
ValueError: Expected 2D array, got 1D array instead: array=[ 0.16242773 -0.90119075 0.05693651 -1.39487254 0.52740615 1.53864419 0.19384363 -1.04456622 1.33753475 -0.12001207 -2.63750884 -1.25099171 0.46053789 1.46558114 -0.57801252 0.52272024 0.37772636 -2.30847845 0.93480207 0.44214023 -0.44434544 -0.08997732 -0.1186683 2.01285023 -0.98363685 0.16022649 -1.16944362 1.72728776 1.34994501 2.50601505 -0.23280568 -2.02817317 -0.7787971 -0.36583488 0.52913013 -1.31727876 -0.66529331 -0.14586002 0.12121093 1.10357558 0.45660313 -2.30975725 0.88526315 -0.15688676 0.36485809 -0.34201796 -0.38742152 0.03129468 -1.45172671 -0.44566978 1.06682479 -0.20197725 -0.19454723 0.39258781 0.90435152 -0.63915512 1.58018959 1.8360178 0.03612573 -0.10075124 1.57287608 -2.16566173 1.94442864 -0.63692063 -0.99461265 0.93788319 -0.38755028 0.26529161 -1.75552239 -0.44738291 1.17226965 0.77052395 -0.4260226 0.18251133 0.89851425 -0.23944746 0.04028808 0.44832614 -0.54714231 -1.13502509 0.37087471 -1.25212905 -0.95237723 -0.07714901 -0.63079535 -1.3229635 2.21617018 0.27867475 -0.77521484 0.38658397 2.00861021 -0.12747727 -0.95342378 -1.6529634 -0.25206374 -0.16668305 -0.21098895 -1.35220926 -0.77684738 -0.91144111 -1.50654517 0.05666307 -0.39321412 -0.3387659 -0.61008176 0.81017022 -0.45387348 1.51662983 -0.05679999 -0.57791232 -0.91545596 0.16725033 -0.85116323 -0.76413108 0.3340035 -0.32600628 0.53474856 -0.51572487 1.21295054 0.19976358 0.33056191 0.62291484 -0.15561658 -1.12258708 1.74775337 -0.58084756 0.38960204 1.24972012 1.0377948 1.91234132 1.27835914 0.50352515 -0.94301361 0.37051374 -0.6044645 1.15750204 -0.36847713 -0.39267774 0.85437988 -1.19265517 -0.7386583 -0.285102 0.7977028 0.17117149 -2.41819117 1.65545797 -1.82631145 -0.90465321 0.42059891 0.04521615 0.08294255 1.14001634 3.29792127 0.73195648 1.34766419 -1.29008346 2.14243689 -0.91052722 -2.56618403 -0.10871541 -2.94942164 0.8296527 0.52906363 1.72234762 0.47726349 0.38223241 -1.42525341 -0.88165607 0.16920468 -0.60258708 1.36064972 -0.25423957 -0.18568209 -2.29655989 1.36165476 1.21798649 0.17978893 1.40157221 0.54140441 -1.15602939 0.12731688 1.71642263 0.76208638 0.61265948 0.61727227 -0.27165942 -1.36591625 0.36943787 -1.27815774 -0.41023464 1.66012597 1.58381415 -0.15379111 -1.99536391 -1.2228214 -1.15689281 0.34514969 0.14252237 1.35108987 -1.68321132]. 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.