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Help in figuring out the cause behind TypeError while fitting with tensor libraries #446

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SunandanAdhikary opened this issue Jan 2, 2024 · 7 comments
Closed

Help in figuring out the cause behind TypeError while fitting with tensor libraries #446

SunandanAdhikary opened this issue Jan 2, 2024 · 7 comments

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@SunandanAdhikary
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SunandanAdhikary commented Jan 2, 2024 •
edited by Jacob-Stevens-Haas
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I am getting an error while trying to fit a linear parameter-varying model that looks like this:

image

The error :

TypeError: ufunc 'bitwise_xor' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

Note that the x_train and u_train are numpy ndarrays of 251x2 dimension with float64 elements. Here is the whole code.
Can you please help in figuring out the problem here?

Reproducing code example:

import numpy as np
import pysindy as ps

x_train = np.random.normal(size=(251,2))
u_train = np.random.normal(size=(251,2))

states = ['beta','r']
inputs = ['delta', 'vx']
ensemble_opt = ps.STLSQ()#(threshold = 0.5)
# identity_lib = ps.IdentityLibrary()
poly_lib = ps.PolynomialLibrary(degree=1)
lib_func = [lambda vx: 1/vx,
            lambda vx: 1/vx^2]
lib_func_names = [lambda vx: '1/(vx= '+vx+')',
                  lambda vx: '1/(vx= '+vx+')^2']

custom_lib = ps.CustomLibrary(library_functions=lib_func, 
                              function_names=lib_func_names)

# 2 states: beta, r + 2 inputs: delta, vx
varuse_libwise_tmp = np.tile([0, 1, 2, 3],2)
varuse_libwise = np.reshape(varuse_libwise_tmp,[2,4])


varuse_libwise[0, 3] = 2        # vx: no identity lib
varuse_libwise[1, 0] = 3        # custom lib : only vx
varuse_libwise[1, 1] = 3
varuse_libwise[1, 2] = 3
'''[[0, 1, 2, 2],--i.e. 3rd variable won't be used in lib1
    [3, 3, 3, 3]]--i.e. only 3rd variable will be used in lib2'''


tensoring_libs = [[1, 1]]       # tensoring between library 1 and 2 variables for fitting
                                # i.e. multiplying library 1 and library 2 variables

gen_lib = ps.GeneralizedLibrary([poly_lib, custom_lib],
                                tensor_array=tensoring_libs,
                                inputs_per_library=varuse_libwise)

model = ps.SINDy(feature_names=states, optimizer=ensemble_opt, feature_library=gen_lib)

model.fit(x_train, u=u_train, t = float(dp.Ts), quiet=True)

model.get_feature_names()

model.print()

Error message:

` TypeError: ufunc 'bitwise_xor' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''` 
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[14], [line 37](vscode-notebook-cell:?execution_count=14&line=37)
     [31](vscode-notebook-cell:?execution_count=14&line=31) gen_lib = ps.GeneralizedLibrary([poly_lib, custom_lib],
     [32](vscode-notebook-cell:?execution_count=14&line=32)                                 tensor_array=tensoring_libs,
     [33](vscode-notebook-cell:?execution_count=14&line=33)                                 inputs_per_library=varuse_libwise)
     [35](vscode-notebook-cell:?execution_count=14&line=35) model = ps.SINDy(feature_names=states, optimizer=ensemble_opt, feature_library=gen_lib)
---> [37](vscode-notebook-cell:?execution_count=14&line=37) model.fit(x_train, u=u_train, t = float(dp.Ts), quiet=True)
     [39](vscode-notebook-cell:?execution_count=14&line=39) model.get_feature_names()
     [41](vscode-notebook-cell:?execution_count=14&line=41) model.print()

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\pysindy.py:414](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:414), in SINDy.fit(self, x, t, x_dot, u, multiple_trajectories, unbias, quiet, ensemble, library_ensemble, replace, n_candidates_to_drop, n_subset, n_models, ensemble_aggregator)
    [412](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:412)     warnings.filterwarnings(action, category=LinAlgWarning)
    [413](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:413)     warnings.filterwarnings(action, category=UserWarning)
--> [414](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:414)     self.model.fit(x, x_dot)
    [416](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:416) # New version of sklearn changes attribute name
    [417](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:417) if float(__version__[:3]) >= 1.0:

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\pipeline.py:402](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:402), in Pipeline.fit(self, X, y, **fit_params)
    [376](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:376) """Fit the model.
    [377](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:377) 
    [378](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:378) Fit all the transformers one after the other and transform the
   (...)
    [399](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:399)     Pipeline with fitted steps.
    [400](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:400) """
    [401](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:401) fit_params_steps = self._check_fit_params(**fit_params)
--> [402](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:402) Xt = self._fit(X, y, **fit_params_steps)
    [403](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:403) with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)):
    [404](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:404)     if self._final_estimator != "passthrough":

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\pipeline.py:360](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:360), in Pipeline._fit(self, X, y, **fit_params_steps)
    [358](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:358)     cloned_transformer = clone(transformer)
    [359](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:359) # Fit or load from cache the current transformer
--> [360](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:360) X, fitted_transformer = fit_transform_one_cached(
    [361](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:361)     cloned_transformer,
    [362](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:362)     X,
    [363](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:363)     y,
    [364](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:364)     None,
    [365](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:365)     message_clsname="Pipeline",
    [366](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:366)     message=self._log_message(step_idx),
    [367](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:367)     **fit_params_steps[name],
    [368](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:368) )
    [369](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:369) # Replace the transformer of the step with the fitted
    [370](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:370) # transformer. This is necessary when loading the transformer
    [371](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:371) # from the cache.
    [372](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:372) self.steps[step_idx] = (name, fitted_transformer)

File [c:\Users\sunan\miniconda3\lib\site-packages\joblib\memory.py:349](file:///C:/Users/sunan/miniconda3/lib/site-packages/joblib/memory.py:349), in NotMemorizedFunc.__call__(self, *args, **kwargs)
    [348](file:///C:/Users/sunan/miniconda3/lib/site-packages/joblib/memory.py:348) def __call__(self, *args, **kwargs):
--> [349](file:///C:/Users/sunan/miniconda3/lib/site-packages/joblib/memory.py:349)     return self.func(*args, **kwargs)

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\pipeline.py:894](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:894), in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
    [892](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:892) with _print_elapsed_time(message_clsname, message):
    [893](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:893)     if hasattr(transformer, "fit_transform"):
--> [894](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:894)         res = transformer.fit_transform(X, y, **fit_params)
    [895](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:895)     else:
    [896](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:896)         res = transformer.fit(X, y, **fit_params).transform(X)

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\utils\_set_output.py:142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [140](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:140) @wraps(f)
    [141](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:141) def wrapped(self, X, *args, **kwargs):
--> [142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142)     data_to_wrap = f(self, X, *args, **kwargs)
    [143](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:143)     if isinstance(data_to_wrap, tuple):
    [144](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:144)         # only wrap the first output for cross decomposition
    [145](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:145)         return (
    [146](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:146)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [147](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:147)             *data_to_wrap[1:],
    [148](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:148)         )

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\utils\_set_output.py:142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [140](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:140) @wraps(f)
    [141](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:141) def wrapped(self, X, *args, **kwargs):
--> [142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142)     data_to_wrap = f(self, X, *args, **kwargs)
    [143](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:143)     if isinstance(data_to_wrap, tuple):
    [144](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:144)         # only wrap the first output for cross decomposition
    [145](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:145)         return (
    [146](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:146)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [147](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:147)             *data_to_wrap[1:],
    [148](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:148)         )

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\utils\_set_output.py:142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [140](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:140) @wraps(f)
    [141](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:141) def wrapped(self, X, *args, **kwargs):
--> [142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142)     data_to_wrap = f(self, X, *args, **kwargs)
    [143](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:143)     if isinstance(data_to_wrap, tuple):
    [144](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:144)         # only wrap the first output for cross decomposition
    [145](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:145)         return (
    [146](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:146)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [147](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:147)             *data_to_wrap[1:],
    [148](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:148)         )

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\base.py:851](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/base.py:851), in TransformerMixin.fit_transform(self, X, y, **fit_params)
    [848](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/base.py:848)     return self.fit(X, **fit_params).transform(X)
    [849](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/base.py:849) else:
    [850](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/base.py:850)     # fit method of arity 2 (supervised transformation)
--> [851](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/base.py:851)     return self.fit(X, y, **fit_params).transform(X)

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\utils\_set_output.py:142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [140](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:140) @wraps(f)
    [141](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:141) def wrapped(self, X, *args, **kwargs):
--> [142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142)     data_to_wrap = f(self, X, *args, **kwargs)
    [143](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:143)     if isinstance(data_to_wrap, tuple):
    [144](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:144)         # only wrap the first output for cross decomposition
    [145](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:145)         return (
    [146](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:146)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [147](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:147)             *data_to_wrap[1:],
    [148](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:148)         )

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\feature_library\base.py:191](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:191), in x_sequence_or_item.<locals>.func(self, x, *args, **kwargs)
    [189](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:189) if isinstance(x, Sequence):
    [190](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:190)     xs = [AxesArray(xi, comprehend_axes(xi)) for xi in x]
--> [191](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:191)     result = wrapped_func(self, xs, *args, **kwargs)
    [192](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:192)     if isinstance(result, Sequence):  # e.g. transform() returns x
    [193](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:193)         return [AxesArray(xp, comprehend_axes(xp)) for xp in result]

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\feature_library\generalized_library.py:267](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:267), in GeneralizedLibrary.transform(self, x_full)
    [264](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:264) if i < self.inputs_per_library_.shape[0]:
    [265](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:265)     if i not in self.exclude_libs_:
    [266](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:266)         xps.append(
--> [267](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:267)             lib.transform(
    [268](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:268)                 [x[..., np.unique(self.inputs_per_library_[i, :])]]
    [269](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:269)             )[0]
    [270](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:270)         )
    [271](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:271) else:
    [272](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:272)     xps.append(lib.transform([x])[0])

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\utils\_set_output.py:142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [140](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:140) @wraps(f)
    [141](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:141) def wrapped(self, X, *args, **kwargs):
--> [142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142)     data_to_wrap = f(self, X, *args, **kwargs)
    [143](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:143)     if isinstance(data_to_wrap, tuple):
    [144](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:144)         # only wrap the first output for cross decomposition
    [145](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:145)         return (
    [146](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:146)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [147](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:147)             *data_to_wrap[1:],
    [148](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:148)         )

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\feature_library\base.py:191](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:191), in x_sequence_or_item.<locals>.func(self, x, *args, **kwargs)
    [189](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:189) if isinstance(x, Sequence):
    [190](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:190)     xs = [AxesArray(xi, comprehend_axes(xi)) for xi in x]
--> [191](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:191)     result = wrapped_func(self, xs, *args, **kwargs)
    [192](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:192)     if isinstance(result, Sequence):  # e.g. transform() returns x
    [193](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:193)         return [AxesArray(xp, comprehend_axes(xp)) for xp in result]

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\feature_library\custom_library.py:230](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:230), in CustomLibrary.transform(self, x_full)
    [226](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:226) for f in self.functions:
    [227](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:227)     for c in self._combinations(
    [228](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:228)         n_input_features, f.__code__.co_argcount, self.interaction_only
    [229](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:229)     ):
--> [230](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:230)         xp[..., library_idx] = f(*[x[..., j] for j in c])
    [231](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:231)         library_idx += 1
    [233](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:233) xp = AxesArray(xp, comprehend_axes(xp))

Cell In[14], [line 8](vscode-notebook-cell:?execution_count=14&line=8)
      [5](vscode-notebook-cell:?execution_count=14&line=5) # identity_lib = ps.IdentityLibrary()
      [6](vscode-notebook-cell:?execution_count=14&line=6) poly_lib = ps.PolynomialLibrary(degree=1)
      [7](vscode-notebook-cell:?execution_count=14&line=7) lib_func = [lambda vx: 1/vx,
----> [8](vscode-notebook-cell:?execution_count=14&line=8)             lambda vx: 1/vx^2]
      [9](vscode-notebook-cell:?execution_count=14&line=9) lib_func_names = [lambda vx: '1/(vx= '+vx+')',
     [10](vscode-notebook-cell:?execution_count=14&line=10)                   lambda vx: '1/(vx= '+vx+')^2']
     [12](vscode-notebook-cell:?execution_count=14&line=12) custom_lib = ps.CustomLibrary(library_functions=lib_func, 
     [13](vscode-notebook-cell:?execution_count=14&line=13)                               function_names=lib_func_names)

File [c:\Users\sunan\miniconda3\lib\site-packages\numpy\lib\mixins.py:21](file:///C:/Users/sunan/miniconda3/lib/site-packages/numpy/lib/mixins.py:21), in _binary_method.<locals>.func(self, other)
     [19](file:///C:/Users/sunan/miniconda3/lib/site-packages/numpy/lib/mixins.py:19) if _disables_array_ufunc(other):
     [20](file:///C:/Users/sunan/miniconda3/lib/site-packages/numpy/lib/mixins.py:20)     return NotImplemented
---> [21](file:///C:/Users/sunan/miniconda3/lib/site-packages/numpy/lib/mixins.py:21) return ufunc(self, other)

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\utils\axes.py:83](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:83), in AxesArray.__array_ufunc__(self, ufunc, method, out, *inputs, **kwargs)
     [81](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:81) else:
     [82](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:82)     outputs = (None,) * ufunc.nout
---> [83](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:83) results = super().__array_ufunc__(ufunc, method, *args, **kwargs)
     [84](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:84) if results is NotImplemented:
     [85](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:85)     return NotImplemented

TypeError: ufunc 'bitwise_xor' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

PySINDy/Python version information:

Using python 3.9.15 and pysindy version: 1.7.5
@Ipsitakoley
@Aditya16828
@SunandanAdhikary
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SunandanAdhikary commented Jan 3, 2024 •
edited by Jacob-Stevens-Haas
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Here is the detailed error for your reference : pysindy_error.txt

pysindy_error.txt 
{
	"name": "TypeError",
	"message": "ufunc 'bitwise_xor' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
	"stack": "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)\nCell \u001b[1;32mIn[102], line 36\u001b[0m\n\u001b[0;32m     30\u001b[0m gen_lib \u001b[38;5;241m=\u001b[39m ps\u001b[38;5;241m.\u001b[39mGeneralizedLibrary([poly_lib, custom_lib],\n\u001b[0;32m     31\u001b[0m                                 tensor_array\u001b[38;5;241m=\u001b[39mtensoring_libs,\n\u001b[0;32m     32\u001b[0m                                 inputs_per_library\u001b[38;5;241m=\u001b[39mvaruse_libwise)\n\u001b[0;32m     34\u001b[0m model \u001b[38;5;241m=\u001b[39m ps\u001b[38;5;241m.\u001b[39mSINDy(feature_names\u001b[38;5;241m=\u001b[39mstates, optimizer\u001b[38;5;241m=\u001b[39mensemble_opt, feature_library\u001b[38;5;241m=\u001b[39mgen_lib)\n\u001b[1;32m---> 36\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mu_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquiet\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m     38\u001b[0m model\u001b[38;5;241m.\u001b[39mget_feature_names()\n\u001b[0;32m     40\u001b[0m model\u001b[38;5;241m.\u001b[39mprint()\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\pysindy\\pysindy.py:414\u001b[0m, in \u001b[0;36mSINDy.fit\u001b[1;34m(self, x, t, x_dot, u, multiple_trajectories, unbias, quiet, ensemble, library_ensemble, replace, n_candidates_to_drop, n_subset, n_models, ensemble_aggregator)\u001b[0m\n\u001b[0;32m    412\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(action, category\u001b[38;5;241m=\u001b[39mLinAlgWarning)\n\u001b[0;32m    413\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(action, category\u001b[38;5;241m=\u001b[39m\u001b[38;5;167;01mUserWarning\u001b[39;00m)\n\u001b[1;32m--> 414\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_dot\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    416\u001b[0m \u001b[38;5;66;03m# New version of sklearn changes attribute name\u001b[39;00m\n\u001b[0;32m    417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mfloat\u001b[39m(__version__[:\u001b[38;5;241m3\u001b[39m]) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m:\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\sklearn\\pipeline.py:402\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m    376\u001b[0m \u001b[38;5;124;03m\"\"\"Fit the model.\u001b[39;00m\n\u001b[0;32m    377\u001b[0m \n\u001b[0;32m    378\u001b[0m \u001b[38;5;124;03mFit all the transformers one after the other and transform the\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    399\u001b[0m \u001b[38;5;124;03m    Pipeline with fitted steps.\u001b[39;00m\n\u001b[0;32m    400\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    401\u001b[0m fit_params_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_fit_params(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[1;32m--> 402\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params_steps)\n\u001b[0;32m    403\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)):\n\u001b[0;32m    404\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\sklearn\\pipeline.py:360\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[1;34m(self, X, y, **fit_params_steps)\u001b[0m\n\u001b[0;32m    358\u001b[0m     cloned_transformer \u001b[38;5;241m=\u001b[39m clone(transformer)\n\u001b[0;32m    359\u001b[0m \u001b[38;5;66;03m# Fit or load from cache the current transformer\u001b[39;00m\n\u001b[1;32m--> 360\u001b[0m X, fitted_transformer \u001b[38;5;241m=\u001b[39m fit_transform_one_cached(\n\u001b[0;32m    361\u001b[0m     cloned_transformer,\n\u001b[0;32m    362\u001b[0m     X,\n\u001b[0;32m    363\u001b[0m     y,\n\u001b[0;32m    364\u001b[0m     \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    365\u001b[0m     message_clsname\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    366\u001b[0m     message\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(step_idx),\n\u001b[0;32m    367\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params_steps[name],\n\u001b[0;32m    368\u001b[0m )\n\u001b[0;32m    369\u001b[0m \u001b[38;5;66;03m# Replace the transformer of the step with the fitted\u001b[39;00m\n\u001b[0;32m    370\u001b[0m \u001b[38;5;66;03m# transformer. This is necessary when loading the transformer\u001b[39;00m\n\u001b[0;32m    371\u001b[0m \u001b[38;5;66;03m# from the cache.\u001b[39;00m\n\u001b[0;32m    372\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[step_idx] \u001b[38;5;241m=\u001b[39m (name, fitted_transformer)\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\joblib\\memory.py:349\u001b[0m, in \u001b[0;36mNotMemorizedFunc.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 349\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\sklearn\\pipeline.py:894\u001b[0m, in \u001b[0;36m_fit_transform_one\u001b[1;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[0;32m    892\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[0;32m    893\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(transformer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_transform\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 894\u001b[0m         res \u001b[38;5;241m=\u001b[39m transformer\u001b[38;5;241m.\u001b[39mfit_transform(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[0;32m    895\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    896\u001b[0m         res \u001b[38;5;241m=\u001b[39m transformer\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\sklearn\\utils\\_set_output.py:142\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m    141\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 142\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    143\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m    144\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m    145\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m    146\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    147\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[0;32m    148\u001b[0m         )\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\sklearn\\utils\\_set_output.py:142\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m    141\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 142\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    143\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m    144\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m    145\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m    146\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    147\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[0;32m    148\u001b[0m         )\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\sklearn\\utils\\_set_output.py:142\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m    141\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 142\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    143\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m    144\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m    145\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m    146\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    147\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[0;32m    148\u001b[0m         )\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\sklearn\\base.py:851\u001b[0m, in \u001b[0;36mTransformerMixin.fit_transform\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m    848\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n\u001b[0;32m    849\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    850\u001b[0m     \u001b[38;5;66;03m# fit method of arity 2 (supervised transformation)\u001b[39;00m\n\u001b[1;32m--> 851\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\sklearn\\utils\\_set_output.py:142\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m    141\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 142\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    143\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m    144\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m    145\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m    146\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    147\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[0;32m    148\u001b[0m         )\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\pysindy\\feature_library\\base.py:191\u001b[0m, in \u001b[0;36mx_sequence_or_item.<locals>.func\u001b[1;34m(self, x, *args, **kwargs)\u001b[0m\n\u001b[0;32m    189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, Sequence):\n\u001b[0;32m    190\u001b[0m     xs \u001b[38;5;241m=\u001b[39m [AxesArray(xi, comprehend_axes(xi)) \u001b[38;5;28;01mfor\u001b[39;00m xi \u001b[38;5;129;01min\u001b[39;00m x]\n\u001b[1;32m--> 191\u001b[0m     result \u001b[38;5;241m=\u001b[39m wrapped_func(\u001b[38;5;28mself\u001b[39m, xs, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    192\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, Sequence):  \u001b[38;5;66;03m# e.g. transform() returns x\u001b[39;00m\n\u001b[0;32m    193\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m [AxesArray(xp, comprehend_axes(xp)) \u001b[38;5;28;01mfor\u001b[39;00m xp \u001b[38;5;129;01min\u001b[39;00m result]\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\pysindy\\feature_library\\generalized_library.py:267\u001b[0m, in \u001b[0;36mGeneralizedLibrary.transform\u001b[1;34m(self, x_full)\u001b[0m\n\u001b[0;32m    264\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minputs_per_library_\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m    265\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexclude_libs_:\n\u001b[0;32m    266\u001b[0m         xps\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m--> 267\u001b[0m             \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    268\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minputs_per_library_\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    269\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m    270\u001b[0m         )\n\u001b[0;32m    271\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    272\u001b[0m     xps\u001b[38;5;241m.\u001b[39mappend(lib\u001b[38;5;241m.\u001b[39mtransform([x])[\u001b[38;5;241m0\u001b[39m])\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\sklearn\\utils\\_set_output.py:142\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m    141\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 142\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    143\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m    144\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m    145\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m    146\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    147\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[0;32m    148\u001b[0m         )\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\pysindy\\feature_library\\base.py:191\u001b[0m, in \u001b[0;36mx_sequence_or_item.<locals>.func\u001b[1;34m(self, x, *args, **kwargs)\u001b[0m\n\u001b[0;32m    189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, Sequence):\n\u001b[0;32m    190\u001b[0m     xs \u001b[38;5;241m=\u001b[39m [AxesArray(xi, comprehend_axes(xi)) \u001b[38;5;28;01mfor\u001b[39;00m xi \u001b[38;5;129;01min\u001b[39;00m x]\n\u001b[1;32m--> 191\u001b[0m     result \u001b[38;5;241m=\u001b[39m wrapped_func(\u001b[38;5;28mself\u001b[39m, xs, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    192\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, Sequence):  \u001b[38;5;66;03m# e.g. transform() returns x\u001b[39;00m\n\u001b[0;32m    193\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m [AxesArray(xp, comprehend_axes(xp)) \u001b[38;5;28;01mfor\u001b[39;00m xp \u001b[38;5;129;01min\u001b[39;00m result]\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\pysindy\\feature_library\\custom_library.py:230\u001b[0m, in \u001b[0;36mCustomLibrary.transform\u001b[1;34m(self, x_full)\u001b[0m\n\u001b[0;32m    226\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunctions:\n\u001b[0;32m    227\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_combinations(\n\u001b[0;32m    228\u001b[0m         n_input_features, f\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__code__\u001b[39m\u001b[38;5;241m.\u001b[39mco_argcount, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minteraction_only\n\u001b[0;32m    229\u001b[0m     ):\n\u001b[1;32m--> 230\u001b[0m         xp[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, library_idx] \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    231\u001b[0m         library_idx \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    233\u001b[0m xp \u001b[38;5;241m=\u001b[39m AxesArray(xp, comprehend_axes(xp))\n\nCell \u001b[1;32mIn[102], line 7\u001b[0m, in \u001b[0;36m<lambda>\u001b[1;34m(vx)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# identity_lib = ps.IdentityLibrary()\u001b[39;00m\n\u001b[0;32m      5\u001b[0m poly_lib \u001b[38;5;241m=\u001b[39m ps\u001b[38;5;241m.\u001b[39mPolynomialLibrary(degree\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      6\u001b[0m lib_func \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;01mlambda\u001b[39;00m vx: \u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39mvx,\n\u001b[1;32m----> 7\u001b[0m             \u001b[38;5;28;01mlambda\u001b[39;00m vx: \u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43mvx\u001b[49m\u001b[38;5;241;43m^\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m]\n\u001b[0;32m      8\u001b[0m lib_func_names \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;01mlambda\u001b[39;00m vx: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1/(vx= \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39mvx\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      9\u001b[0m                   \u001b[38;5;28;01mlambda\u001b[39;00m vx: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1/(vx= \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39mvx\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)^2\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     11\u001b[0m custom_lib \u001b[38;5;241m=\u001b[39m ps\u001b[38;5;241m.\u001b[39mCustomLibrary(library_functions\u001b[38;5;241m=\u001b[39mlib_func, \n\u001b[0;32m     12\u001b[0m                               function_names\u001b[38;5;241m=\u001b[39mlib_func_names)\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\numpy\\lib\\mixins.py:21\u001b[0m, in \u001b[0;36m_binary_method.<locals>.func\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _disables_array_ufunc(other):\n\u001b[0;32m     20\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m---> 21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mufunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n\nFile \u001b[1;32mc:\\Users\\sunan\\miniconda3\\lib\\site-packages\\pysindy\\utils\\axes.py:83\u001b[0m, in \u001b[0;36mAxesArray.__array_ufunc__\u001b[1;34m(self, ufunc, method, out, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     82\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28;01mNone\u001b[39;00m,) \u001b[38;5;241m*\u001b[39m ufunc\u001b[38;5;241m.\u001b[39mnout\n\u001b[1;32m---> 83\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m__array_ufunc__(ufunc, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     84\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m results \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[0;32m     85\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\n\u001b[1;31mTypeError\u001b[0m: ufunc 'bitwise_xor' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
}

@Jacob-Stevens-Haas
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Jacob-Stevens-Haas commented Jan 3, 2024 •
edited
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This seems like an issue with AxesArray. FWIW, rather than uploading files, if they're long and distracting, you can put them in a spoiler dropdown. I hope you don't mind, I edited your comment to show you how. But it looks like the way you copied and saved the error lost line endings and messed up the encoding, so it's a little bit unreadable.

I tried running your code (adding

import numpy

x_train = np.arange(2* 251).reshape((251,2))
u_train = np.arange(2* 251).reshape((251,2))

) but I don't know what dp is (in model.fit(x_train, u=u_train, t = float(dp.Ts), quiet=True)), so I'm getting a NameError.

@SunandanAdhikary
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SunandanAdhikary commented Jan 3, 2024 •
edited
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Excuse me. I have hurriedly uploaded it in the wrong format. PFA the error here.
dp is an object that I created. You can use 0.04 as the sampling interval.

error 
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[14], [line 37](vscode-notebook-cell:?execution_count=14&line=37)
     [31](vscode-notebook-cell:?execution_count=14&line=31) gen_lib = ps.GeneralizedLibrary([poly_lib, custom_lib],
     [32](vscode-notebook-cell:?execution_count=14&line=32)                                 tensor_array=tensoring_libs,
     [33](vscode-notebook-cell:?execution_count=14&line=33)                                 inputs_per_library=varuse_libwise)
     [35](vscode-notebook-cell:?execution_count=14&line=35) model = ps.SINDy(feature_names=states, optimizer=ensemble_opt, feature_library=gen_lib)
---> [37](vscode-notebook-cell:?execution_count=14&line=37) model.fit(x_train, u=u_train, t = float(dp.Ts), quiet=True)
     [39](vscode-notebook-cell:?execution_count=14&line=39) model.get_feature_names()
     [41](vscode-notebook-cell:?execution_count=14&line=41) model.print()

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\pysindy.py:414](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:414), in SINDy.fit(self, x, t, x_dot, u, multiple_trajectories, unbias, quiet, ensemble, library_ensemble, replace, n_candidates_to_drop, n_subset, n_models, ensemble_aggregator)
    [412](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:412)     warnings.filterwarnings(action, category=LinAlgWarning)
    [413](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:413)     warnings.filterwarnings(action, category=UserWarning)
--> [414](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:414)     self.model.fit(x, x_dot)
    [416](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:416) # New version of sklearn changes attribute name
    [417](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/pysindy.py:417) if float(__version__[:3]) >= 1.0:

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\pipeline.py:402](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:402), in Pipeline.fit(self, X, y, **fit_params)
    [376](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:376) """Fit the model.
    [377](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:377) 
    [378](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:378) Fit all the transformers one after the other and transform the
   (...)
    [399](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:399)     Pipeline with fitted steps.
    [400](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:400) """
    [401](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:401) fit_params_steps = self._check_fit_params(**fit_params)
--> [402](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:402) Xt = self._fit(X, y, **fit_params_steps)
    [403](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:403) with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)):
    [404](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:404)     if self._final_estimator != "passthrough":

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\pipeline.py:360](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:360), in Pipeline._fit(self, X, y, **fit_params_steps)
    [358](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:358)     cloned_transformer = clone(transformer)
    [359](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:359) # Fit or load from cache the current transformer
--> [360](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:360) X, fitted_transformer = fit_transform_one_cached(
    [361](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:361)     cloned_transformer,
    [362](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:362)     X,
    [363](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:363)     y,
    [364](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:364)     None,
    [365](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:365)     message_clsname="Pipeline",
    [366](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:366)     message=self._log_message(step_idx),
    [367](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:367)     **fit_params_steps[name],
    [368](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:368) )
    [369](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:369) # Replace the transformer of the step with the fitted
    [370](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:370) # transformer. This is necessary when loading the transformer
    [371](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:371) # from the cache.
    [372](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:372) self.steps[step_idx] = (name, fitted_transformer)

File [c:\Users\sunan\miniconda3\lib\site-packages\joblib\memory.py:349](file:///C:/Users/sunan/miniconda3/lib/site-packages/joblib/memory.py:349), in NotMemorizedFunc.__call__(self, *args, **kwargs)
    [348](file:///C:/Users/sunan/miniconda3/lib/site-packages/joblib/memory.py:348) def __call__(self, *args, **kwargs):
--> [349](file:///C:/Users/sunan/miniconda3/lib/site-packages/joblib/memory.py:349)     return self.func(*args, **kwargs)

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\pipeline.py:894](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:894), in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
    [892](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:892) with _print_elapsed_time(message_clsname, message):
    [893](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:893)     if hasattr(transformer, "fit_transform"):
--> [894](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:894)         res = transformer.fit_transform(X, y, **fit_params)
    [895](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:895)     else:
    [896](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/pipeline.py:896)         res = transformer.fit(X, y, **fit_params).transform(X)

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\utils\_set_output.py:142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [140](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:140) @wraps(f)
    [141](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:141) def wrapped(self, X, *args, **kwargs):
--> [142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142)     data_to_wrap = f(self, X, *args, **kwargs)
    [143](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:143)     if isinstance(data_to_wrap, tuple):
    [144](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:144)         # only wrap the first output for cross decomposition
    [145](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:145)         return (
    [146](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:146)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [147](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:147)             *data_to_wrap[1:],
    [148](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:148)         )

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\utils\_set_output.py:142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [140](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:140) @wraps(f)
    [141](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:141) def wrapped(self, X, *args, **kwargs):
--> [142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142)     data_to_wrap = f(self, X, *args, **kwargs)
    [143](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:143)     if isinstance(data_to_wrap, tuple):
    [144](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:144)         # only wrap the first output for cross decomposition
    [145](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:145)         return (
    [146](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:146)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [147](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:147)             *data_to_wrap[1:],
    [148](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:148)         )

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\utils\_set_output.py:142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [140](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:140) @wraps(f)
    [141](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:141) def wrapped(self, X, *args, **kwargs):
--> [142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142)     data_to_wrap = f(self, X, *args, **kwargs)
    [143](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:143)     if isinstance(data_to_wrap, tuple):
    [144](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:144)         # only wrap the first output for cross decomposition
    [145](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:145)         return (
    [146](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:146)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [147](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:147)             *data_to_wrap[1:],
    [148](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:148)         )

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\base.py:851](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/base.py:851), in TransformerMixin.fit_transform(self, X, y, **fit_params)
    [848](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/base.py:848)     return self.fit(X, **fit_params).transform(X)
    [849](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/base.py:849) else:
    [850](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/base.py:850)     # fit method of arity 2 (supervised transformation)
--> [851](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/base.py:851)     return self.fit(X, y, **fit_params).transform(X)

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\utils\_set_output.py:142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [140](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:140) @wraps(f)
    [141](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:141) def wrapped(self, X, *args, **kwargs):
--> [142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142)     data_to_wrap = f(self, X, *args, **kwargs)
    [143](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:143)     if isinstance(data_to_wrap, tuple):
    [144](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:144)         # only wrap the first output for cross decomposition
    [145](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:145)         return (
    [146](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:146)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [147](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:147)             *data_to_wrap[1:],
    [148](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:148)         )

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\feature_library\base.py:191](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:191), in x_sequence_or_item.<locals>.func(self, x, *args, **kwargs)
    [189](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:189) if isinstance(x, Sequence):
    [190](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:190)     xs = [AxesArray(xi, comprehend_axes(xi)) for xi in x]
--> [191](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:191)     result = wrapped_func(self, xs, *args, **kwargs)
    [192](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:192)     if isinstance(result, Sequence):  # e.g. transform() returns x
    [193](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:193)         return [AxesArray(xp, comprehend_axes(xp)) for xp in result]

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\feature_library\generalized_library.py:267](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:267), in GeneralizedLibrary.transform(self, x_full)
    [264](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:264) if i < self.inputs_per_library_.shape[0]:
    [265](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:265)     if i not in self.exclude_libs_:
    [266](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:266)         xps.append(
--> [267](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:267)             lib.transform(
    [268](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:268)                 [x[..., np.unique(self.inputs_per_library_[i, :])]]
    [269](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:269)             )[0]
    [270](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:270)         )
    [271](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:271) else:
    [272](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/generalized_library.py:272)     xps.append(lib.transform([x])[0])

File [c:\Users\sunan\miniconda3\lib\site-packages\sklearn\utils\_set_output.py:142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142), in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
    [140](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:140) @wraps(f)
    [141](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:141) def wrapped(self, X, *args, **kwargs):
--> [142](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:142)     data_to_wrap = f(self, X, *args, **kwargs)
    [143](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:143)     if isinstance(data_to_wrap, tuple):
    [144](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:144)         # only wrap the first output for cross decomposition
    [145](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:145)         return (
    [146](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:146)             _wrap_data_with_container(method, data_to_wrap[0], X, self),
    [147](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:147)             *data_to_wrap[1:],
    [148](file:///C:/Users/sunan/miniconda3/lib/site-packages/sklearn/utils/_set_output.py:148)         )

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\feature_library\base.py:191](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:191), in x_sequence_or_item.<locals>.func(self, x, *args, **kwargs)
    [189](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:189) if isinstance(x, Sequence):
    [190](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:190)     xs = [AxesArray(xi, comprehend_axes(xi)) for xi in x]
--> [191](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:191)     result = wrapped_func(self, xs, *args, **kwargs)
    [192](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:192)     if isinstance(result, Sequence):  # e.g. transform() returns x
    [193](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/base.py:193)         return [AxesArray(xp, comprehend_axes(xp)) for xp in result]

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\feature_library\custom_library.py:230](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:230), in CustomLibrary.transform(self, x_full)
    [226](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:226) for f in self.functions:
    [227](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:227)     for c in self._combinations(
    [228](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:228)         n_input_features, f.__code__.co_argcount, self.interaction_only
    [229](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:229)     ):
--> [230](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:230)         xp[..., library_idx] = f(*[x[..., j] for j in c])
    [231](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:231)         library_idx += 1
    [233](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/feature_library/custom_library.py:233) xp = AxesArray(xp, comprehend_axes(xp))

Cell In[14], [line 8](vscode-notebook-cell:?execution_count=14&line=8)
      [5](vscode-notebook-cell:?execution_count=14&line=5) # identity_lib = ps.IdentityLibrary()
      [6](vscode-notebook-cell:?execution_count=14&line=6) poly_lib = ps.PolynomialLibrary(degree=1)
      [7](vscode-notebook-cell:?execution_count=14&line=7) lib_func = [lambda vx: 1/vx,
----> [8](vscode-notebook-cell:?execution_count=14&line=8)             lambda vx: 1/vx^2]
      [9](vscode-notebook-cell:?execution_count=14&line=9) lib_func_names = [lambda vx: '1/(vx= '+vx+')',
     [10](vscode-notebook-cell:?execution_count=14&line=10)                   lambda vx: '1/(vx= '+vx+')^2']
     [12](vscode-notebook-cell:?execution_count=14&line=12) custom_lib = ps.CustomLibrary(library_functions=lib_func, 
     [13](vscode-notebook-cell:?execution_count=14&line=13)                               function_names=lib_func_names)

File [c:\Users\sunan\miniconda3\lib\site-packages\numpy\lib\mixins.py:21](file:///C:/Users/sunan/miniconda3/lib/site-packages/numpy/lib/mixins.py:21), in _binary_method.<locals>.func(self, other)
     [19](file:///C:/Users/sunan/miniconda3/lib/site-packages/numpy/lib/mixins.py:19) if _disables_array_ufunc(other):
     [20](file:///C:/Users/sunan/miniconda3/lib/site-packages/numpy/lib/mixins.py:20)     return NotImplemented
---> [21](file:///C:/Users/sunan/miniconda3/lib/site-packages/numpy/lib/mixins.py:21) return ufunc(self, other)

File [c:\Users\sunan\miniconda3\lib\site-packages\pysindy\utils\axes.py:83](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:83), in AxesArray.__array_ufunc__(self, ufunc, method, out, *inputs, **kwargs)
     [81](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:81) else:
     [82](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:82)     outputs = (None,) * ufunc.nout
---> [83](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:83) results = super().__array_ufunc__(ufunc, method, *args, **kwargs)
     [84](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:84) if results is NotImplemented:
     [85](file:///C:/Users/sunan/miniconda3/lib/site-packages/pysindy/utils/axes.py:85)     return NotImplemented

TypeError: ufunc 'bitwise_xor' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

<\details>

@SunandanAdhikary
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@Jacob-Stevens-Haas Is this an error from my end, or is there an issue with the library?

@Jacob-Stevens-Haas
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When you typed

lib_func = [lambda vx: 1/vx,
            lambda vx: 1/vx^2]

Did you mean to do bitwise XOR (^) or exponentiation (**)? Bitwise XOR isn't supported for floats.

I'm going to assume exponentiation, but there's still another error. The SINDy object needs feature names for both the state variables and control variables. Thus,

model = ps.SINDy(feature_names=states, ...

should be:

model = ps.SINDy(feature_names=states + inputs, ...)

Correcting this and the exponentiation gives:

(beta)' = -0.335 1 + 0.209 beta + -0.536 r + 0.620 delta
(r)' = 0.105 1 + 0.539 beta + -0.280 delta

@Jacob-Stevens-Haas
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So not specifically a bug, but our documentation of the feature_names argument really should call out that it needs to handle state and control.

Also, if the change suggested in #387 is approved, this may be clearer.

@SunandanAdhikary
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Thanks, it runs without any error after adding inputs in feature_names.

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