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>$ automl_gs square.csv square
/usr/local/lib/python3.7/site-packages/automl_gs/utils_automl.py:270: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
metrics = yaml.load(f)
Solving a classification problem, maximizing accuracy using tensorflow.
Modeling with field specifications:
real: numeric
fake1: numeric
fake2: numeric
fake3: numeric
fake4: numeric
text: categorical
bool: categorical
/usr/local/lib/python3.7/site-packages/automl_gs/utils_automl.py:126: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
hps = yaml.load(f)
0%| | 0/100 [00:00<?, ?trial/s/usr/local/lib/python3.7/site-packages/automl_gs/utils_automl.py:199: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
metrics = yaml.load(f)[problem_type]
Traceback (most recent call last):
File "model.py", line 47, in <module>
model_train(df, encoders, args, model)
File "../automodel/automl_train/pipeline.py", line 408, in model_train
for train_indices, val_indices in split.split(np.zeros(y.shape[0]), y):
File "/usr/local/lib/python3.7/site-packages/sklearn/model_selection/_split.py", line 1315, in split
for train, test in self._iter_indices(X, y, groups):
File "/usr/local/lib/python3.7/site-packages/sklearn/model_selection/_split.py", line 1695, in _iter_indices
raise ValueError("The least populated class in y has only 1"
ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2.
Traceback (most recent call last): | 0/20 [00:00<?, ?epoch/s]
File "/usr/local/bin/automl_gs", line 10, in <module>
sys.exit(cmd())
File "/usr/local/lib/python3.7/site-packages/automl_gs/automl_gs.py", line 175, in cmd
tpu_address=args.tpu_address)
File "/usr/local/lib/python3.7/site-packages/automl_gs/automl_gs.py", line 87, in automl_grid_search
"metadata", "results.csv"))
File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 702, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 429, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 895, in __init__
self._make_engine(self.engine)
File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 1122, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py", line 1853, in __init__
self._reader = parsers.TextReader(src, **kwds)
File "pandas/_libs/parsers.pyx", line 387, in pandas._libs.parsers.TextReader.__cinit__
File "pandas/_libs/parsers.pyx", line 705, in pandas._libs.parsers.TextReader._setup_parser_source
FileNotFoundError: [Errno 2] File b'automl_train/metadata/results.csv' does not exist: b'automl_train/metadata/results.csv'
The text was updated successfully, but these errors were encountered:
From the provided dataset, the issue here is that it's trying to do a classification problem instead of a regression problem. I don't believe that error is otherwise wrong.
I removed problem_type as a parameter since I thought the heuristic was fine for that; I was apparently wrong. I may need to refine it.
In the meantime, if square is a float (has a decimal), it should work.
Although I have not gone through with code, but I think it can be a problem of stratification also. I have seen this kind of issue when I use stratify inside train_test_split.
I'll check and update.
I tried a simple dataset to play around with this, and I am running into
I believe this is because scikit wants there to be multiple iterations of the variable you're trying to predict. Might want to add it to the docs~
Input:
square.txt
Full log if needed:
The text was updated successfully, but these errors were encountered: