fillna

path: orion.primitives.timeseries_preprocessing.fillna

description: this primitive is an iterative imputation transformer for filling missing values using pandas.fillna.

see json.

argument	type	description
parameters
X	numpy.ndarray	n-dimensional sequence of values
hyperparameters
value	int, dict, pandas.Series, or pandas.DataFrame	Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list
method	str, or list	String or list of strings describing whether to use forward or backward fill. pad / ffill: propagate last valid observation forward to next valid. backfill / bfill: use next valid observation to fill gap. Otherwise use None to fill with desired value. Possible values include ['backfill', 'bfill', 'pad', 'ffill', None]
axis	int, or str	Axis along which to fill missing value. Possible values include 0 or “index”, 1 or “columns”
limit	int	If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
downcast	dict	A dict of item->dtype of what to downcast if possible, or the string “infer” which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible)
output
X	numpy.ndarray	Array of input sequence with imputed values

In [1]: import numpy as np

In [2]: from mlstars import load_primitive

In [3]: X = np.array([1] * 4 + [np.nan]).reshape(-1, 1)

In [4]: primitive = load_primitive('orion.primitives.timeseries_preprocessing.fillna',
   ...:     arguments={"X": X, "value": 0})
   ...: 

In [5]: primitive.fit()

In [6]: primitive.produce(X=X)
Out[6]: 
array([[1.],
       [1.],
       [1.],
       [1.],
       [0.]])

rolling window sequence SimpleImputer