Hi,
I m working on elo merchant recommendation competition.
I trying to replicate rossman notebook
I have merged multiple diff csv file to bring all relations in train & test df.
-
I getting
infvalue in rmse calculation
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Im not sure why
learn.predict(test)is failing
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3077 try:
-> 3078 return self._engine.get_loc(key)
3079 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
KeyError: 'feature_1'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-39-af36fc1167bc> in <module>()
----> 1 learn.predict(test)
/opt/conda/lib/python3.6/site-packages/fastai/basic_train.py in predict(self, item, **kwargs)
249 "Return prect class, label and probabilities for `item`."
250 self.callbacks.append(RecordOnCPU())
--> 251 batch = self.data.one_item(item)
252 res = self.pred_batch(batch=batch)
253 pred = res[0]
/opt/conda/lib/python3.6/site-packages/fastai/basic_data.py in one_item(self, item, detach, denorm)
146 "Get `item` into a batch. Optionally `detach` and `denorm`."
147 ds = self.single_ds
--> 148 with ds.set_item(item):
149 return self.one_batch(ds_type=DatasetType.Single, detach=detach, denorm=denorm)
150
/opt/conda/lib/python3.6/contextlib.py in __enter__(self)
79 def __enter__(self):
80 try:
---> 81 return next(self.gen)
82 except StopIteration:
83 raise RuntimeError("generator didn't yield") from None
/opt/conda/lib/python3.6/site-packages/fastai/data_block.py in set_item(self, item)
464 def set_item(self,item):
465 "For inference, will replace the dataset with one that only contains `item`."
--> 466 self.item = self.x.process_one(item)
467 yield None
468 self.item = None
/opt/conda/lib/python3.6/site-packages/fastai/data_block.py in process_one(self, item, processor)
72 if processor is not None: self.processor = processor
73 self.processor = listify(self.processor)
---> 74 for p in self.processor: item = p.process_one(item)
75 return item
76
/opt/conda/lib/python3.6/site-packages/fastai/tabular/data.py in process_one(self, item)
44 def process_one(self, item):
45 df = pd.DataFrame([item,item])
---> 46 for proc in self.procs: proc(df, test=True)
47 if len(self.cat_names) != 0:
48 codes = np.stack([c.cat.codes.values for n,c in df[self.cat_names].items()], 1).astype(np.int64) + 1
/opt/conda/lib/python3.6/site-packages/fastai/tabular/transform.py in __call__(self, df, test)
30 "Apply the correct function to `df` depending on `test`."
31 func = self.apply_test if test else self.apply_train
---> 32 func(df)
33
34 def apply_train(self, df:DataFrame):
/opt/conda/lib/python3.6/site-packages/fastai/tabular/transform.py in apply_test(self, df)
49 def apply_test(self, df:DataFrame):
50 for n in self.cat_names:
---> 51 df.loc[:,n] = pd.Categorical(df[n], categories=self.categories[n], ordered=True)
52
53 FillStrategy = IntEnum('FillStrategy', 'MEDIAN COMMON CONSTANT')
/opt/conda/lib/python3.6/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):
/opt/conda/lib/python3.6/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
/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
2487 res = cache.get(item)
2488 if res is None:
-> 2489 values = self._data.get(item)
2490 res = self._box_item_values(item, values)
2491 cache[item] = res
/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py in get(self, item, fastpath)
4113
4114 if not isna(item):
-> 4115 loc = self.items.get_loc(item)
4116 else:
4117 indexer = np.arange(len(self.items))[isna(self.items)]
/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
3078 return self._engine.get_loc(key)
3079 except KeyError:
-> 3080 return self._engine.get_loc(self._maybe_cast_indexer(key))
3081
3082 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
KeyError: 'feature_1'
feature_1 is a category variable & it is present inside test df. But here is failing to find feature_1.
My notebook https://www.kaggle.com/nikhilikhar/elo-fastai-pytorch?scriptVersionId=8606358