Hi,
I ran into this error when running Lesson 01 from fast book.
When looking at source, saw it was the path parameters, so tried fixing that in the Jupyter Book.
Ran into a new error.
Using latest fastai2 code, and latest from fast book.
Any suggestions?
Can you show the entire stack trace? We need the whole thing to know what’s going on 
Hi @muellerzr,
TypeError Traceback (most recent call last)
in
6 ‘relationship’, ‘race’],
7 cont_names = [‘age’, ‘fnlwgt’, ‘education-num’],
----> 8 procs = [Categorify, FillMissing, Normalize])
9
10 learn = tabular_learner(dls, metrics=accuracy)
~/Developer/fastai2/fastai2/tabular/data.py in from_csv(cls, csv, **kwargs)
25 def from_csv(cls, csv, **kwargs):
26 "Create from csv file in path using procs"
—> 27 return cls.from_df(pd.read_csv(csv), **kwargs)
28
29 @delegates(TabDataLoader.init)
~/Developer/fastai2/fastai2/tabular/data.py in from_df(cls, df, path, procs, cat_names, cont_names, y_names, y_block, valid_idx, **kwargs)
19 if cont_names is None: cont_names = list(set(df)-set(cat_names)-set(y_names))
20 splits = RandomSplitter()(df) if valid_idx is None else IndexSplitter(valid_idx)(df)
—> 21 to = TabularPandas(df, procs, cat_names, cont_names, y_names, splits=splits, y_block=y_block)
22 return to.dataloaders(path=path, **kwargs)
23
~/Developer/fastai2/fastai2/tabular/core.py in init(self, df, procs, cat_names, cont_names, y_names, y_block, splits, do_setup, device, inplace, reduce_memory)
115 if y_block is None and self.y_names:
116 # Make ys categorical if they’re not numeric
–> 117 ys = df[self.y_names]
118 if len(ys.select_dtypes(include=‘number’).columns)!=len(ys.columns): y_block = CategoryBlock()
119 else: y_block = RegressionBlock()
~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in getitem(self, key)
2686 return self._getitem_multilevel(key)
2687 else:
-> 2688 return self._getitem_column(key)
2689
2690 def _getitem_column(self, key):
~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in _getitem_column(self, key)
2693 # get column
2694 if self.columns.is_unique:
-> 2695 return self._get_item_cache(key)
2696
2697 # duplicate columns & possible reduce dimensionality
~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
2485 “”“Return the cached item, item represents a label indexer.”""
2486 cache = self._item_cache
-> 2487 res = cache.get(item)
2488 if res is None:
2489 values = self._data.get(item)
TypeError: unhashable type: ‘L’
TypeError Traceback (most recent call last)
in
1 from fastai2.collab import *
2 path = untar_data(URLs.ML_SAMPLE)
----> 3 dls = CollabDataLoaders.from_csv(path/‘ratings.csv’)
4 learn = collab_learner(dls, y_range=(0.5,5.5))
5 learn.fine_tune(10)
~/Developer/fastai2/fastai2/collab.py in from_csv(cls, csv, **kwargs)
29 def from_csv(cls, csv, **kwargs):
30 “Create a DataLoaders suitable for collaborative filtering from csv.”
—> 31 return cls.from_df(pd.read_csv(csv), **kwargs)
32
33 CollabDataLoaders.from_csv = delegates(to=CollabDataLoaders.from_df)(CollabDataLoaders.from_csv)
~/Developer/fastai2/fastai2/collab.py in from_df(cls, ratings, valid_pct, user_name, item_name, rating_name, seed, path, **kwargs)
23 cat_names = [user_name,item_name]
24 splits = RandomSplitter(valid_pct=valid_pct, seed=seed)(range_of(ratings))
—> 25 to = TabularCollab(ratings, [Categorify], cat_names, y_names=[rating_name], y_block=TransformBlock(), splits=splits)
26 return to.dataloaders(path=path, **kwargs)
27
~/Developer/fastai2/fastai2/tabular/core.py in init(self, df, procs, cat_names, cont_names, y_names, y_block, splits, do_setup, device, inplace, reduce_memory)
124 self.split = len(df) if splits is None else len(splits[0])
125 if reduce_memory:
–> 126 if len(self.cat_names) > 0: self.reduce_cats()
127 if len(self.cont_names) > 0: self.reduce_conts()
128 if do_setup: self.setup()
~/Developer/fastai2/fastai2/tabular/core.py in reduce_cats(self)
136 def decode(self): return self.procs.decode(self)
137 def decode_row(self, row): return self.new(pd.DataFrame(row).T).decode().items.iloc[0]
–> 138 def reduce_cats(self): self.train[self.cat_names] = self.train[self.cat_names].astype(‘category’)
139 def reduce_conts(self): self[self.cont_names] = self[self.cont_names].astype(np.float32)
140 def show(self, max_n=10, **kwargs): display_df(self.new(self.all_cols[:max_n]).decode().items)
~/anaconda3/lib/python3.7/site-packages/fastcore/foundation.py in getitem(self, k)
276 def init(self, items): self.items = items
277 def len(self): return len(self.items)
–> 278 def getitem(self, k): return self.items[k]
279 def setitem(self, k, v): self.items[list(k) if isinstance(k,CollBase) else k] = v
280 def delitem(self, i): del(self.items[i])
~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in getitem(self, key)
2686 return self._getitem_multilevel(key)
2687 else:
-> 2688 return self._getitem_column(key)
2689
2690 def _getitem_column(self, key):
~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in _getitem_column(self, key)
2693 # get column
2694 if self.columns.is_unique:
-> 2695 return self._get_item_cache(key)
2696
2697 # duplicate columns & possible reduce dimensionality
~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
2485 “”“Return the cached item, item represents a label indexer.”""
2486 cache = self._item_cache
-> 2487 res = cache.get(item)
2488 if res is None:
2489 values = self._data.get(item)
TypeError: unhashable type: ‘L’
You need to put the stack trace between two ``` for it to be properly formatted, it’s very hard to read otherwise.
In this case, I have fixed the example and confirmed it works on my side.
Hi @sgugger,
Thanks, just did a pull, saw that you fixed the path error on the paramater.
Still getting this though when running.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-d44927d4a8d6> in <module>
6 'relationship', 'race'],
7 cont_names = ['age', 'fnlwgt', 'education-num'],
----> 8 procs = [Categorify, FillMissing, Normalize])
9
10 learn = tabular_learner(dls, metrics=accuracy)
~/Developer/fastai2/fastai2/tabular/data.py in from_csv(cls, csv, **kwargs)
25 def from_csv(cls, csv, **kwargs):
26 "Create from `csv` file in `path` using `procs`"
---> 27 return cls.from_df(pd.read_csv(csv), **kwargs)
28
29 @delegates(TabDataLoader.__init__)
~/Developer/fastai2/fastai2/tabular/data.py in from_df(cls, df, path, procs, cat_names, cont_names, y_names, y_block, valid_idx, **kwargs)
19 if cont_names is None: cont_names = list(set(df)-set(cat_names)-set(y_names))
20 splits = RandomSplitter()(df) if valid_idx is None else IndexSplitter(valid_idx)(df)
---> 21 to = TabularPandas(df, procs, cat_names, cont_names, y_names, splits=splits, y_block=y_block)
22 return to.dataloaders(path=path, **kwargs)
23
~/Developer/fastai2/fastai2/tabular/core.py in __init__(self, df, procs, cat_names, cont_names, y_names, y_block, splits, do_setup, device, inplace, reduce_memory)
115 if y_block is None and self.y_names:
116 # Make ys categorical if they're not numeric
--> 117 ys = df[self.y_names]
118 if len(ys.select_dtypes(include='number').columns)!=len(ys.columns): y_block = CategoryBlock()
119 else: y_block = RegressionBlock()
~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key)
2686 return self._getitem_multilevel(key)
2687 else:
-> 2688 return self._getitem_column(key)
2689
2690 def _getitem_column(self, key):
~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in _getitem_column(self, key)
2693 # get column
2694 if self.columns.is_unique:
-> 2695 return self._get_item_cache(key)
2696
2697 # duplicate columns & possible reduce dimensionality
~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
2485 """Return the cached item, item represents a label indexer."""
2486 cache = self._item_cache
-> 2487 res = cache.get(item)
2488 if res is None:
2489 values = self._data.get(item)
TypeError: unhashable type: 'L'
Going to give it a try on another system, will update thread after.
Are you on fastai2 master? It works there.
You have an old version of the dataset then, you should remove it locally and re-download it.
@sgugger did a fresh install of conda env with fastai2 env, can confirm it’s working now. Thanks for fixing typo in path.
Hi,
I’m running into an error on the TabularDataLoaders in NB1. It worked fine last week, but I tried it again today, and am running into an error. I just git pulled today. Is anyone else running into this?
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-17-1e20bc7da2d6> in <module>
----> 1 from fastai2.tabular.all import *
2 path = untar_data(URLs.ADULT_SAMPLE)
3
4 dls = TabularDataLoaders.from_csv(path/'adult.csv', path, y_names="salary",
5 cat_names = ['workclass', 'education', 'marital-status', 'occupation',
~/Desktop/python/fastai2/fastai2/tabular/all.py in <module>
2 from ..callback.all import *
3 from .core import *
----> 4 from .data import *
5 from .model import *
6 from .learner import *
~/Desktop/python/fastai2/fastai2/tabular/data.py in <module>
9
10 # Cell
---> 11 class TabularDataLoaders(DataLoaders):
12 "Basic wrapper around several `DataLoader`s with factory methods for tabular data"
13 @classmethod
~/Desktop/python/fastai2/fastai2/tabular/data.py in TabularDataLoaders()
12 "Basic wrapper around several `DataLoader`s with factory methods for tabular data"
13 @classmethod
---> 14 @delegates(Tabular.dataloaders, but=["dl_type", "dl_kwargs"])
15 def from_df(cls, df, path='.', procs=None, cat_names=None, cont_names=None, y_names=None, y_block=None,
16 valid_idx=None, **kwargs):
TypeError: delegates() got an unexpected keyword argument 'but'
You need the last version of fastcore too, not just fastai.
Thanks, it worked.