A TabularLearner object has no property called weights. It does have a method parameters but that is a generator and I don’t know how to get the embeddings out of it.
If you check each embed IE learn.model.embeds[0] it has a .weight attribute. Also we’re dealing in raw PyTorch now 
That being said, if you do:
list(learn.model.embeds.parameters())
This also grabs it. IE I can do li = list(learn.model.embeds.parameters()) and if you check li[0].shape it’s the [10,6]
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Thanks Zachary. Figured it out. The weights are gotten for the k’th embedding by model.embeds[k].weights
But I’d forgotten that you can get all the values out of a generator by using the list method!
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I’m having a problem with the pre-processor Normalize when creating a TabularPandas object.
I’m running the notebook “09_tabular” in the fastbook repo, and I get this error when running the eighth cell under the section " Using a Neural Network":
procs_nn = [Categorify, FillMissing, Normalize]
to_nn = TabularPandas(df_nn_final, procs_nn, cat_nn, cont_nn,
splits=splits, y_names=dep_var)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-150-cd0965f9989a> in <module>
1 to_nn = TabularPandas(df_nn_final, procs_nn, cat_nn, cont_nn,
----> 2 y_names=dep_var, splits=splits)
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/tabular/core.py in __init__(self, df, procs, cat_names, cont_names, y_names, y_block, splits, do_setup, device, inplace, reduce_memory)
165 self.cat_names,self.cont_names,self.procs = L(cat_names),L(cont_names),Pipeline(procs)
166 self.split = len(df) if splits is None else len(splits[0])
--> 167 if do_setup: self.setup()
168
169 def new(self, df):
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/tabular/core.py in setup(self)
176 def decode_row(self, row): return self.new(pd.DataFrame(row).T).decode().items.iloc[0]
177 def show(self, max_n=10, **kwargs): display_df(self.new(self.all_cols[:max_n]).decode().items)
--> 178 def setup(self): self.procs.setup(self)
179 def process(self): self.procs(self)
180 def loc(self): return self.items.loc
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastcore/transform.py in setup(self, items, train_setup)
195 tfms = self.fs[:]
196 self.fs.clear()
--> 197 for t in tfms: self.add(t,items, train_setup)
198
199 def add(self,t, items=None, train_setup=False):
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastcore/transform.py in add(self, t, items, train_setup)
198
199 def add(self,t, items=None, train_setup=False):
--> 200 t.setup(items, train_setup)
201 self.fs.append(t)
202
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastcore/transform.py in setup(self, items, train_setup)
76 def setup(self, items=None, train_setup=False):
77 train_setup = train_setup if self.train_setup is None else self.train_setup
---> 78 return self.setups(getattr(items, 'train', items) if train_setup else items)
79
80 def _call(self, fn, x, split_idx=None, **kwargs):
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastcore/dispatch.py in __call__(self, *args, **kwargs)
97 if not f: return args[0]
98 if self.inst is not None: f = MethodType(f, self.inst)
---> 99 return f(*args, **kwargs)
100
101 def __get__(self, inst, owner):
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/tabular/core.py in setups(self, to)
276 self.means,self.stds = dict(getattr(to, 'train', to).conts.mean()),dict(getattr(to, 'train', to).conts.std(ddof=0)+1e-7)
277 self.store_attrs = 'means,stds'
--> 278 return self(to)
279
280 @Normalize
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastcore/transform.py in __call__(self, x, **kwargs)
70 @property
71 def name(self): return getattr(self, '_name', _get_name(self))
---> 72 def __call__(self, x, **kwargs): return self._call('encodes', x, **kwargs)
73 def decode (self, x, **kwargs): return self._call('decodes', x, **kwargs)
74 def __repr__(self): return f'{self.name}:\nencodes: {self.encodes}decodes: {self.decodes}'
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastcore/transform.py in _call(self, fn, x, split_idx, **kwargs)
80 def _call(self, fn, x, split_idx=None, **kwargs):
81 if split_idx!=self.split_idx and self.split_idx is not None: return x
---> 82 return self._do_call(getattr(self, fn), x, **kwargs)
83
84 def _do_call(self, f, x, **kwargs):
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastcore/transform.py in _do_call(self, f, x, **kwargs)
86 if f is None: return x
87 ret = f.returns_none(x) if hasattr(f,'returns_none') else None
---> 88 return retain_type(f(x, **kwargs), x, ret)
89 res = tuple(self._do_call(f, x_, **kwargs) for x_ in x)
90 return retain_type(res, x)
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastcore/dispatch.py in __call__(self, *args, **kwargs)
97 if not f: return args[0]
98 if self.inst is not None: f = MethodType(f, self.inst)
---> 99 return f(*args, **kwargs)
100
101 def __get__(self, inst, owner):
~/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/tabular/core.py in encodes(self, to)
280 @Normalize
281 def encodes(self, to:Tabular):
--> 282 to.conts = (to.conts-self.means) / self.stds
283 return to
284
~/anaconda3/envs/fastai/lib/python3.7/site-packages/pandas/core/ops/__init__.py in f(self, other, axis, level, fill_value)
645 # TODO: why are we passing flex=True instead of flex=not special?
646 # 15 tests fail if we pass flex=not special instead
--> 647 self, other = _align_method_FRAME(self, other, axis, flex=True, level=level)
648
649 if isinstance(other, ABCDataFrame):
~/anaconda3/envs/fastai/lib/python3.7/site-packages/pandas/core/ops/__init__.py in _align_method_FRAME(left, right, axis, flex, level)
501 elif is_list_like(right) and not isinstance(right, (ABCSeries, ABCDataFrame)):
502 # GH17901
--> 503 right = to_series(right)
504
505 if flex is not None and isinstance(right, ABCDataFrame):
~/anaconda3/envs/fastai/lib/python3.7/site-packages/pandas/core/ops/__init__.py in to_series(right)
464 if len(left.columns) != len(right):
465 raise ValueError(
--> 466 msg.format(req_len=len(left.columns), given_len=len(right))
467 )
468 right = left._constructor_sliced(right, index=left.columns)
ValueError: Unable to coerce to Series, length must be 1: given 0
However, if I remove Normalize from the pre-processing steps, the code runs without issue:
to_nn = TabularPandas(df_nn_final, procs=[Categorify, FillMissing], # removed Normalize from `procs`
cat_names = cat_nn, cont_names = cont_nn,
splits=splits, y_names=dep_var)
And the NN will also train without issue after removing the Normalize preprocessing. I also get results for learning rates and losses comparable to those in the repo’s notebook. My notebook is on the left, the right is from fastai’s GitHub repo:
I’ve run this notebook on several machines (Windows/Ubuntu/Google Colab), and have had the exact same issue on every machine. I’m running fastai 2.0.6, pandas 1.1.1, and Python 3.7.7.
Has anyone else had this issue?
5 Likes
I’m having the same problem, but am getting a different error message, see image. In ptic TypeError: Object with dtype category cannot perform the numpy op subtract.
Yes, when I remove the Normalize argument the code runs.
How strange! Explanation would me much appreciated!
@Sturzgefahr @wlw If you inspect df_nn_final[cont_nn[0]] you’ll see that the dtype is “object”. When the dataframe was read in, Pandas wasn’t confident that the column contained integers, so assigned it a more general type. Fastai needs numeric types to perform the normalization. To fix this, modify df_nn_final such that the continuous variable is numeric:
df_nn_final = df_nn_final.astype({cont_nn[0]: 'int32'})
to_nn = TabularPandas(df_nn_final, procs_nn, cat_nn, cont_nn,
splits=splits, y_names=dep_var)
9 Likes
Thanks for the insight!
Could you please anyone help on how to resolve the following error?
cluster_columns(xs_imp)
ValueError Traceback (most recent call last)
in
----> 1 cluster_columns(xs_imp)
D:\Miniconda3\envs\fastai\lib\site-packages\fastbook_init_.py in cluster_columns(df, figsize, font_size)
70 z = hc.linkage(corr_condensed, method=‘average’)
71 fig = plt.figure(figsize=figsize)
—> 72 hc.dendrogram(z, labels=df.columns, orientation=‘left’, leaf_font_size=font_size)
73 plt.show()
74
D:\Miniconda3\envs\fastai\lib\site-packages\scipy\cluster\hierarchy.py in dendrogram(Z, p, truncate_mode, color_threshold, get_leaves, orientation, labels, count_sort, distance_sort, show_leaf_counts, no_plot, no_labels, leaf_font_size, leaf_rotation, leaf_label_func, show_contracted, link_color_func, ax, above_threshold_color)
3275 “‘bottom’, or ‘right’”)
3276
-> 3277 if labels and Z.shape[0] + 1 != len(labels):
3278 raise ValueError(“Dimensions of Z and labels must be consistent.”)
3279
D:\Miniconda3\envs\fastai\lib\site-packages\pandas\core\indexes\base.py in nonzero(self)
2413
2414 def nonzero(self):
-> 2415 raise ValueError(
2416 f"The truth value of a {type(self).name} is ambiguous. "
2417 “Use a.empty, a.bool(), a.item(), a.any() or a.all().”
ValueError: The truth value of a Index is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
Chapter 9 says " In an online-only chapter on the book’s website we show how to replicate it from scratch and attain the same accuracy shown in the paper."
referring to The paper, “Entity Embeddings of Categorical Variables”
Where is this found?
Has anyone figured out how to incorporate this from chapter 8 End Sidebar?
Although the results of
EmbeddingNNare a bit worse than the dot product approach (which shows the power of carefully constructing an architecture for a domain), it does allow us to do something very important: we can now directly incorporate other user and movie information, date and time information, or any other information that may be relevant to the recommendation. That’s exactly whatTabularModeldoes.
I am trying to figure out how to incorporate other user and movie data into the NN collaborative filtering model.
I have the same error as Vilthy cites above: cluster_columns fails with “ValueError: The truth value of a Index is ambiguous”. Does anyone know how to remedy this? Perhaps unrelated though there’s an open issue relating to hc.dendrogram here
I’m also unable to run the cell just above with plot_fi (NameError: name ‘plot_fi’ is not defined).
Edit: both errors went away after a restart and a rerun of the notebook.
I got the following error today and it worked before. Any idea what’s wrong? This was run on google colab.
cred_path = Path(’~/.kaggle/kaggle.json’).expanduser()
if not cred_path.exists():
cred_path.parent.mkdir(exist_ok=True)
cred_path.write(creds)
cred_path.chmod(0o600)
AttributeError Traceback (most recent call last)
in ()
2 if not cred_path.exists():
3 cred_path.parent.mkdir(exist_ok=True)
----> 4 cred_path.write(creds)
5 cred_path.chmod(0o600)
AttributeError: ‘PosixPath’ object has no attribute ‘write’
Didn’t work for me either.
Running cred_path.parent.exists() returns True, so it is definitely able to create the directory.
I pressed Tab and replaced .write() with .write_text() option that showed up and it seems to work.
“None of [Index([‘SalePrice’], dtype=‘object’)] are in the [columns]”
Keep getting above error when creating the Tabular Object, would anyone know why? Thanks
Thanks. It worked when I changed to ‘.write_text()’.
I got the similar error and it worked before. This is the 09_tabular.ipynb and run on google Colab. Any fastai update on the fastbook?
//
(path/‘to.pkl’).save(to)
AttributeError Traceback (most recent call last)
in () ----> 1 (path/‘to.pkl’).save(to)
AttributeError: ‘PosixPath’ object has no attribute ‘save’
//
Same problem with collab and fastai == 2.0.16.
I’m not sure if it is the right way to do it, but I found save_pickle and load_pickle functions in fastcore/utils.py by first trying to unsuccessfully lookup save method in github search and then searching github for “pickle” directly.
save_pickle(path/‘to.pkl’, to)
to = load_pickle(path/‘to.pkl’)
2 Likes