FourierFeaturizer and DateFeaturizer pipeline: "transform() got multiple values for argument 'y' result"

[wouterbles]

FourierFeaturizer and DateFeaturizer pipeline: "transform() got multiple values for argument 'y' result"

[msat59]

Thank you @aaronreidsmith .

The bug is fixed and my code runs smoothly.

[benman1]

I've come across the same problem. But I found that if I downgrade to scikit-learn 1.1.3 (from 1.2.0) the problem goes away.

[msat59]

I have the same issue. I created a new environment and updated all libraries, so I am not sure which one causes this.

Here is the code to replicate the issue:

from pmdarima import pipeline
from pmdarima import arima
from pmdarima import preprocessing as ppc

max_p=5
max_q=5
ff_m=12
ff_k=4
n_jobs=4
trend = 'c'

train = np.array([ 53.49732848,  55.67194689,  58.38817983,  60.15814887,
        60.78495554,  60.92771421,  61.30123253,  62.37336819,
        64.31094699,  66.95783357,  69.91670478,  72.54269204,
        73.76937463,  72.23523373,  67.0330952 ,  58.5990769 ,
        49.0156232 ,  41.45220597,  38.91781587,  43.00469642,
        53.33866198,  67.77987974,  83.06470788,  95.81475843,
       103.74286951, 106.54764586, 105.90211977, 104.27736115,
       103.47655163, 104.35387802, 107.59139252, 113.98787597,
       123.92153058, 136.93004483, 151.58183531, 165.31885435,
       175.02600621, 178.97489817, 178.31169515, 175.85739037,
       173.50084961, 171.48318201, 169.87503182, 169.55511273,
       171.45912256, 175.46712864])

pipe = pipeline.Pipeline([
            ("fourier", ppc.FourierFeaturizer(m=ff_m, k=ff_k)),
            ("arima", arima.AutoARIMA(stepwise=False, trace=5, error_action="ignore",
                                      seasonal=False,  # because we use Fourier
                                      trend=trend, n_jobs=n_jobs,
                                      max_p=max_p, max_q=max_q,
                                      suppress_warnings=True))
        ])

pipe.fit(train)

pip.predict(2)

Output:

TypeError: transform() got multiple values for argument 'y' 

[aaronreidsmith]

I think this should be fixed by #532. We'll get a fixed version released so y'all aren't stuck pinning scikit to <1.2.0

[msat59]

I think it's because of scikit-learn new feature:

Major Feature The set_output API has been adopted by all transformers. Meta-estimators that contain transformers such as pipeline.Pipeline or compose.ColumnTransformer also define a set_output.

[aaronreidsmith]

I just deployed version 2.0.3 (to PyPI; conda builds are maintained separately). Give that a shot and let me know if it works