NotImplementedError: Cannot convert a symbolic Tensor to a numpy array.

[YukunYangNPF]

When I run the model, ci = CausalImpact(protests['count'], pre_period, post_period). This error happened

`--------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last)

in 2 post_period = [73, len(protests)-1] 3 ----> 4 ci = CausalImpact(protests['count'], pre_period, post_period) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/causalimpact/main.py in __init__(self, data, pre_period, post_period, model, model_args, alpha) 217 self.normed_post_data = processed_input['normed_post_data'] 218 self.mu_sig = processed_input['mu_sig'] --> 219 self._fit_model() 220 self._process_posterior_inferences() 221 self._summarize_inferences() /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/causalimpact/main.py in _fit_model(self) 296 # if operation `iloc` returns a pd.Series, cast it back to pd.DataFrame 297 observed_time_series = pd.DataFrame(observed_time_series.iloc[:, 0]) --> 298 model_samples, model_kernel_results = cimodel.fit_model( 299 self.model, 300 observed_time_series, /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/causalimpact/model.py in fit_model(model, observed_time_series, method) 358 optimizer = tf.optimizers.Adam(learning_rate=0.1) 359 variational_steps = 200 # Hardcoded for now --> 360 variational_posteriors = tfp.sts.build_factored_surrogate_posterior(model=model) 361 362 @tf.function() /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/sts/fitting.py in build_factored_surrogate_posterior(model, batch_shape, seed, name) 201 variational_posterior = collections.OrderedDict() 202 for param in model.parameters: --> 203 variational_posterior[param.name] = _build_posterior_for_one_parameter( 204 param, batch_shape=batch_shape, seed=seed()) 205 return joint_distribution_named_lib.JointDistributionNamed( /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/sts/fitting.py in _build_posterior_for_one_parameter(param, batch_shape, seed) 85 86 # Build a trainable Normal distribution. ---> 87 initial_loc = sample_uniform_initial_state( 88 param, init_sample_shape=batch_shape, 89 return_constrained=False, seed=seed) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/sts/fitting.py in sample_uniform_initial_state(parameter, return_constrained, init_sample_shape, seed) 67 parameter.prior.sample(init_sample_shape))) 68 param_shape = ( ---> 69 unconstrained_prior_sample_fn.get_concrete_function().output_shapes) 70 if not tensorshape_util.is_fully_defined(param_shape): 71 param_shape = tf.shape(unconstrained_prior_sample_fn()) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in get_concrete_function(self, *args, **kwargs) 1297 ValueError: if this object has not yet been called on concrete values. 1298 """ -> 1299 concrete = self._get_concrete_function_garbage_collected(*args, **kwargs) 1300 concrete._garbage_collector.release() # pylint: disable=protected-access 1301 return concrete /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _get_concrete_function_garbage_collected(self, *args, **kwargs) 1203 if self._stateful_fn is None: 1204 initializers = [] -> 1205 self._initialize(args, kwargs, add_initializers_to=initializers) 1206 self._initialize_uninitialized_variables(initializers) 1207 /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to) 723 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph) 724 self._concrete_stateful_fn = ( --> 725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access 726 *args, **kwds)) 727 /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs) 2967 args, kwargs = None, None 2968 with self._lock: -> 2969 graph_function, _ = self._maybe_define_function(args, kwargs) 2970 return graph_function 2971 /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs) 3359 3360 self._function_cache.missed.add(call_context_key) -> 3361 graph_function = self._create_graph_function(args, kwargs) 3362 self._function_cache.primary[cache_key] = graph_function 3363 /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) 3194 arg_names = base_arg_names + missing_arg_names 3195 graph_function = ConcreteFunction( -> 3196 func_graph_module.func_graph_from_py_func( 3197 self._name, 3198 self._python_function, /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes) 988 _, original_func = tf_decorator.unwrap(python_func) 989 --> 990 func_outputs = python_func(*func_args, **func_kwargs) 991 992 # invariant: `func_outputs` contains only Tensors, CompositeTensors, /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds) 632 xla_context.Exit() 633 else: --> 634 out = weak_wrapped_fn().__wrapped__(*args, **kwds) 635 return out 636 /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs) 975 except Exception as e: # pylint:disable=broad-except 976 if hasattr(e, "ag_error_metadata"): --> 977 raise e.ag_error_metadata.to_exception(e) 978 else: 979 raise NotImplementedError: in user code: /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/sts/fitting.py:66 None * parameter.prior.sample(init_sample_shape)) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/distributions/distribution.py:1002 sample ** return self._call_sample_n(sample_shape, seed, name, **kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/distributions/transformed_distribution.py:331 _call_sample_n x = self.distribution.sample(sample_shape=[n], seed=seed, /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/distributions/distribution.py:1002 sample return self._call_sample_n(sample_shape, seed, name, **kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/distributions/distribution.py:979 _call_sample_n samples = self._sample_n( /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/internal/distribution_util.py:1364 _fn return fn(*args, **kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/distributions/inverse_gamma.py:209 _sample_n return tf.math.exp(-gamma_lib.random_gamma( /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/distributions/gamma.py:660 random_gamma return random_gamma_with_runtime( /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/distributions/gamma.py:654 random_gamma_with_runtime return _random_gamma_gradient( /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/internal/custom_gradient.py:104 none_wrapper return f_wrapped(*trimmed_args, **kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/custom_gradient.py:261 __call__ return self._d(self._f, a, k) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/custom_gradient.py:217 decorated return _graph_mode_decorator(wrapped, args, kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/custom_gradient.py:330 _graph_mode_decorator result, grad_fn = f(*args) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/internal/custom_gradient.py:92 f_wrapped val, aux = vjp_fwd(*reconstruct_args, **kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/distributions/gamma.py:541 _random_gamma_fwd samples, impl = _random_gamma_no_gradient( /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/internal/implementation_selection.py:83 f_wrapped return f(*args, **kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:828 __call__ result = self._call(*args, **kwds) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:862 _call results = self._stateful_fn(*args, **kwds) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2941 __call__ filtered_flat_args) = self._maybe_define_function(args, kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3361 _maybe_define_function graph_function = self._create_graph_function(args, kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3196 _create_graph_function func_graph_module.func_graph_from_py_func( /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:990 func_graph_from_py_func func_outputs = python_func(*func_args, **func_kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:634 wrapped_fn out = weak_wrapped_fn().__wrapped__(*args, **kwds) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/distributions/gamma.py:483 _random_gamma_no_gradient return sampler_impl( /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow_probability/python/internal/implementation_selection.py:162 impl_selecting_fn function.register(defun_cpu_fn, **kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3390 register concrete_func.add_gradient_functions_to_graph() /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2057 add_gradient_functions_to_graph self._delayed_rewrite_functions.forward_backward()) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py:631 forward_backward forward, backward = self._construct_forward_backward(num_doutputs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py:674 _construct_forward_backward func_graph_module.func_graph_from_py_func( /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:990 func_graph_from_py_func func_outputs = python_func(*func_args, **func_kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/eager/function.py:665 _backprop_function return gradients_util._GradientsHelper( # pylint: disable=protected-access /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/gradients_util.py:683 _GradientsHelper in_grads = _MaybeCompile(grad_scope, op, func_call, /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/gradients_util.py:340 _MaybeCompile return grad_fn() # Exit early /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/gradients_util.py:684 lambda: grad_fn(op, *out_grads)) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/random_grad.py:114 _StatelessRandomGammaV2Grad alpha_broadcastable = add_leading_unit_dimensions(alpha, /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/random_grad.py:35 add_leading_unit_dimensions [array_ops.ones([num_dimensions], dtype=dtypes.int32), /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:201 wrapper return target(*args, **kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:3120 ones output = _constant_if_small(one, shape, dtype, name) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:2804 _constant_if_small if np.prod(shape) < 1000: <__array_function__ internals>:5 prod /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3030 prod Returns /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/fromnumeric.py:87 _wrapreduction return ufunc.reduce(obj, axis, dtype, out, **passkwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:852 __array__ raise NotImplementedError( NotImplementedError: Cannot convert a symbolic Tensor (gradients/stateless_random_gamma/StatelessRandomGammaV2_grad/sub:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported`

[WillianFuks]

Hi @YukunYangNPF ,

Not sure what's going on there. The error message wasn't helpful unfortunately.

I just tested the latest tfcausalimpact on a docker py38 image and it worked fine so as it seems this error is happening maybe because of some version incompatibility or something in the input data is not correct.

Could you try running the same code inside a Docker py38 environment to confirm if it works? If it does then we'll know this is something related to your environment.

Also, if you are working with public datasets and could share them it could be helpful, I'd try running here to see if I get the same error.