micahjsmith / dengue_prediction Goto Github PK
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License: MIT License
Predicting dengue fever spread
License: MIT License
The following code does work:
transformer = [
LagImputer(groupby_kwargs={'level': 'city'}),
Imputer(),
StandardScaler(),
SimpleFunctionTransformer(
lambda df: np.mean(df, axis=1)
),
]
but this doesn't:
transformer = [
LagImputer(groupby_kwargs={'level': 'city'}),
Imputer(),
SimpleFunctionTransformer(
lambda df: np.mean(df, axis=1)
),
StandardScaler()
]
and it gives this stack trace:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-103-10686064a0e4> in <module>()
----> 1 mapper.fit(X_df, y_df)
~/dengue_prediction/dengue_prediction_env/lib/python3.6/site-packages/sklearn_pandas/dataframe_mapper.py in fit(self, X, y)
212 with add_column_names_to_exception(columns):
213 Xt = self._get_col_subset(X, columns, input_df)
--> 214 _call_fit(transformers.fit, Xt, y)
215
216 # handle features not explicitly selected
~/dengue_prediction/dengue_prediction_env/lib/python3.6/site-packages/sklearn_pandas/pipeline.py in _call_fit(fit_method, X, y, **kwargs)
22 """
23 try:
---> 24 return fit_method(X, y, **kwargs)
25 except TypeError:
26 # fit takes only one argument
~/dengue_prediction/dengue_prediction_env/lib/python3.6/site-packages/fhub_core/feature.py in wrapped(X, y, **kwargs)
46 "Converting using approach '{}'".format(convert.__name__))
47 if y is not None:
---> 48 return func(convert(X), y=convert(y), **kwargs)
49 else:
50 return func(convert(X), **kwargs)
~/dengue_prediction/dengue_prediction_env/lib/python3.6/site-packages/sklearn_pandas/pipeline.py in fit(self, X, y, **fit_params)
74
75 def fit(self, X, y=None, **fit_params):
---> 76 Xt, fit_params = self._pre_transform(X, y, **fit_params)
77 _call_fit(self.steps[-1][-1].fit, Xt, y, **fit_params)
78 return self
~/dengue_prediction/dengue_prediction_env/lib/python3.6/site-packages/sklearn_pandas/pipeline.py in _pre_transform(self, X, y, **fit_params)
67 if hasattr(transform, "fit_transform"):
68 Xt = _call_fit(transform.fit_transform,
---> 69 Xt, y, **fit_params_steps[name])
70 else:
71 Xt = _call_fit(transform.fit,
~/dengue_prediction/dengue_prediction_env/lib/python3.6/site-packages/sklearn_pandas/pipeline.py in _call_fit(fit_method, X, y, **kwargs)
22 """
23 try:
---> 24 return fit_method(X, y, **kwargs)
25 except TypeError:
26 # fit takes only one argument
~/dengue_prediction/dengue_prediction_env/lib/python3.6/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
518 else:
519 # fit method of arity 2 (supervised transformation)
--> 520 return self.fit(X, y, **fit_params).transform(X)
521
522
~/dengue_prediction/dengue_prediction_env/lib/python3.6/site-packages/fhub_transformers/base.py in transform(self, X, **transform_kwargs)
36 def transform(self, X, **transform_kwargs):
37 if self.groupby_kwargs:
---> 38 call = X.sort_index().groupby(**self.groupby_kwargs).apply
39 else:
40 call = X.sort_index().pipe
AttributeError: ['ndvi_se', 'ndvi_sw', 'ndvi_ne', 'ndvi_nw']: 'numpy.ndarray' object has no attribute 'sort_index'
from fhub_core import Feature
from fhub_transformers.missing import LagImputer
from sklearn.preprocessing import Imputer, StandardScaler, MinMaxScaler
from fhub_transformers import NamedFramer, SimpleFunctionTransformer
feature = Feature(input=input, transformer=transformer)
input = 'precipitation_amt_mm'
transformer = [
LagImputer(groupby_kwargs={'level': 'city'}),
Imputer(),
SimpleFunctionTransformer(
lambda ser: ser if ser > 0 else ser
),
]
mapper = feature.as_dataframe_mapper()
mapper.fit(X_df, y_df)
mapper.transform(X_df)
gives error:
AttributeError Traceback (most recent call last)
<ipython-input-83-0de258be511f> in <module>()
----> 1 mapper.transform(X_df)
~/miniconda3/envs/dengue_preiction/lib/python3.6/site-packages/sklearn_pandas/dataframe_mapper.py in transform(self, X)
277 if transformers is not None:
278 with add_column_names_to_exception(columns):
--> 279 Xt = transformers.transform(Xt)
280 extracted.append(_handle_feature(Xt))
281
~/miniconda3/envs/dengue_preiction/lib/python3.6/site-packages/fhub_core/feature.py in wrapped(X, y, **kwargs)
48 return func(convert(X), y=convert(y), **kwargs)
49 else:
---> 50 return func(convert(X), **kwargs)
51 except catch as e:
52 formatted_exc = indent(traceback.format_exc(), n=8)
~/miniconda3/envs/dengue_preiction/lib/python3.6/site-packages/fhub_core/feature.py in transform(self, X, **transform_kwargs)
22 def transform(self, X, **transform_kwargs):
23 _transform = make_robust_to_tabular_types(super().transform)
---> 24 return _transform(X, **transform_kwargs)
25
26
~/miniconda3/envs/dengue_preiction/lib/python3.6/site-packages/fhub_core/feature.py in wrapped(X, y, **kwargs)
48 return func(convert(X), y=convert(y), **kwargs)
49 else:
---> 50 return func(convert(X), **kwargs)
51 except catch as e:
52 formatted_exc = indent(traceback.format_exc(), n=8)
~/miniconda3/envs/dengue_preiction/lib/python3.6/site-packages/sklearn/pipeline.py in _transform(self, X)
424 for name, transform in self.steps:
425 if transform is not None:
--> 426 Xt = transform.transform(Xt)
427 return Xt
428
~/miniconda3/envs/dengue_preiction/lib/python3.6/site-packages/fhub_transformers/base.py in transform(self, X, **transform_kwargs)
36 def transform(self, X, **transform_kwargs):
37 if self.groupby_kwargs:
---> 38 call = X.sort_index().groupby(**self.groupby_kwargs).apply
39 else:
40 call = X.sort_index().pipe
AttributeError: precipitation_amt_mm: 'numpy.ndarray' object has no attribute 'sort_index'
Not sure if this is just me using this incorrectly.
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