ODE parameter prediction
We will perform ODE parameter prediction for forecasting of the number of cases. We have two ways for prediction.
Time-series prediction withOUT indicators
Time-series prediction with indicators
The second one uses indicators, including OxCGRT indicators, the number of vaccinations.
Note:
The target (Y) of prediction is ODE parameter values, not the number of cases. ODE parameter values are more useful because ODE parameter values have physical meanings, including (non-dimensional) effective contact rate, and they are always in the range of (0, 1).
[1]:
from datetime import timedelta
import covsirphy as cs
from matplotlib import pyplot as plt
import numpy as np
cs.__version__
[1]:
'3.1.1'
The target of prediction is estimated ODE parameter values. At we will prepare them as explained with tutorials. For example, we can use class method ODEScenario.auto_build(), specifying location name. “Baseline” scenario will be created automatically with downloaded datasets.
[2]:
snr = cs.ODEScenario.auto_build(geo="Japan", model=cs.SIRFModel)
# Show summary
snr.summary()
2024-04-27 at 02:53:03 | INFO |
<SIR-F Model: parameter estimation>
2024-04-27 at 02:53:03 | INFO | Running optimization with 4 CPUs...
2024-04-27 at 02:55:26 | INFO | Completed optimization. Total: 2 min 22 sec
[2]:
| Start | End | Rt | theta | kappa | rho | sigma | alpha1 [-] | 1/alpha2 [day] | 1/beta [day] | 1/gamma [day] | ODE | tau | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Scenario | Phase | |||||||||||||
| Baseline | 0th | 2020-02-23 | 2020-08-10 | 1.36 | 0.031993 | 0.000035 | 0.002057 | 0.001428 | 0.032 | 475 | 8 | 12 | SIR-F Model | 24 |
| 1st | 2020-08-11 | 2020-11-16 | 0.82 | 0.001366 | 0.00001 | 0.000798 | 0.000956 | 0.001 | 1619 | 21 | 17 | SIR-F Model | 24 | |
| 2nd | 2020-11-17 | 2020-12-24 | 1.52 | 0.002056 | 0.000007 | 0.001354 | 0.00088 | 0.002 | 2318 | 12 | 19 | SIR-F Model | 24 | |
| 3rd | 2020-12-25 | 2021-01-16 | 1.66 | 0.00129 | 0.000012 | 0.001149 | 0.000678 | 0.001 | 1367 | 15 | 25 | SIR-F Model | 24 | |
| 4th | 2021-01-17 | 2021-02-10 | 0.68 | 0.000456 | 0.00002 | 0.000693 | 0.000998 | 0.0 | 843 | 24 | 17 | SIR-F Model | 24 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | |
| 63rd | 2023-03-12 | 2023-03-27 | 1.65 | 0.00004 | 0.000004 | 0.001108 | 0.000667 | 0.0 | 4719 | 15 | 25 | SIR-F Model | 24 | |
| 64th | 2023-03-28 | 2023-04-07 | 0.84 | 0.0039 | 0.000004 | 0.001872 | 0.002216 | 0.004 | 4402 | 9 | 8 | SIR-F Model | 24 | |
| 65th | 2023-04-08 | 2023-04-18 | 1.45 | 0.000495 | 0.000003 | 0.001446 | 0.000993 | 0.0 | 5904 | 12 | 17 | SIR-F Model | 24 | |
| 66th | 2023-04-19 | 2023-04-27 | 1.75 | 0.000059 | 0.000005 | 0.001553 | 0.000881 | 0.0 | 3265 | 11 | 19 | SIR-F Model | 24 | |
| 67th | 2023-04-28 | 2023-05-08 | 1.58 | 0.00202 | 0.000003 | 0.001392 | 0.000879 | 0.002 | 6044 | 12 | 19 | SIR-F Model | 24 |
68 rows × 13 columns
For demonstration, we will get the start date of future phases.
[3]:
future_start_date = snr.simulate(display=False).index.max() + timedelta(days=1)
future_start_date
[3]:
Timestamp('2023-05-09 00:00:00')
1. Time-series prediction withOUT indicators
This scenario “Predicted” does not use indicators, using AutoTS package: a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale.
At first, create “Predicted” scenario, copying estimated ODE parameter values of “Baseline” scenario.
[4]:
snr.build_with_template(name="Predicted", template="Baseline");
Then, run ODEScenario().predict(days, name, **kwargs). We can apply keyword arguments of autots.AutoTS() except for forecast_length (always the same as days).
[5]:
snr.predict(days=30, name="Predicted");
Using 1 cpus for n_jobs.
Data frequency is: D, used frequency is: D
Model Number: 1 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "fake_date", "transformations": {"0": "DifferencedTransformer", "1": "SinTrend"}, "transformation_params": {"0": {}, "1": {}}}
Model Number: 2 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 3 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SeasonalDifference", "1": "Round", "2": "Detrend"}, "transformation_params": {"0": {"lag_1": 7, "method": "Mean"}, "1": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": false}, "2": {"model": "GLS"}}}
Model Number: 4 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "RollingMeanTransformer", "1": "DifferencedTransformer", "2": "Detrend", "3": "Slice"}, "transformation_params": {"0": {"fixed": true, "window": 3}, "1": {}, "2": {"model": "Linear"}, "3": {"method": 100}}}
Model Number: 5 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "median", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "RobustScaler", "3": "Round", "4": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}, "3": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": true}, "4": {}}}
Model Number: 6 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "bkfilter", "1": "SinTrend", "2": "Detrend", "3": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {}, "2": {"model": "Linear"}, "3": {}}}
Model Number: 7 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "PositiveShift", "1": "SinTrend", "2": "bkfilter"}, "transformation_params": {"0": {}, "1": {}, "2": {}}}
Model Number: 8 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "SinTrend"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}}}
Model Number: 9 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 10 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 2, "lag_2": 7} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SinTrend", "1": "Round", "2": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {"model": "middle", "decimals": 2, "on_transform": false, "on_inverse": true}, "2": {}}}
Model Number: 11 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 2, "lag_2": 1} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SeasonalDifference", "1": "QuantileTransformer", "2": "Detrend"}, "transformation_params": {"0": {"lag_1": 12, "method": "Median"}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"model": "GLS"}}}
Model Number: 12 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 7, "lag_2": 2} and transformations {"fillna": "mean", "transformations": {"0": "QuantileTransformer", "1": "ClipOutliers"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"method": "clip", "std_threshold": 2, "fillna": null}}}
Model Number: 13 with model ConstantNaive in generation 0 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "PowerTransformer", "1": "QuantileTransformer", "2": "SeasonalDifference"}, "transformation_params": {"0": {}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"lag_1": 7, "method": "LastValue"}}}
Model Number: 14 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 28} and transformations {"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 15 with model SectionalMotif in generation 0 of 1 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "sokalmichener", "include_differenced": true, "k": 20, "stride_size": 1, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 16 with model SectionalMotif in generation 0 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": false, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 17 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 30} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 18 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "weighted_mean", "distance_metric": "mae", "k": 10, "datepart_method": "common_fourier"} and transformations {"fillna": "nearest", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 19 with model ConstantNaive in generation 0 of 1 with params {"constant": 1} and transformations {"fillna": "zero", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": 30, "transform_dict": null}, "2": {"method": "zscore", "method_params": {"distribution": "chi2", "alpha": 0.1}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": "quadratic", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}, "isolated_only": false}}}
Model Number: 20 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "KNNImputer", "transformations": {"0": "bkfilter", "1": "Discretize", "2": "QuantileTransformer", "3": "LevelShiftTransformer"}, "transformation_params": {"0": {}, "1": {"discretization": "center", "n_bins": 5}, "2": {"output_distribution": "uniform", "n_quantiles": 1000}, "3": {"window_size": 90, "alpha": 3.0, "grouping_forward_limit": 2, "max_level_shifts": 30, "alignment": "average"}}}
Model Number: 21 with model AverageValueNaive in generation 0 of 1 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "time", "transformations": {"0": "RollingMean100thN", "1": "AlignLastValue", "2": "RobustScaler", "3": "Round", "4": "MaxAbsScaler"}, "transformation_params": {"0": {}, "1": {"rows": 7, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {"decimals": -1, "on_transform": true, "on_inverse": false}, "4": {}}}
Model Number: 22 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "RollingMeanTransformer", "1": "AlignLastValue", "2": "Round", "3": "PctChangeTransformer", "4": "MaxAbsScaler"}, "transformation_params": {"0": {"fixed": false, "window": 7, "macro_micro": false, "center": true}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"decimals": 1, "on_transform": true, "on_inverse": false}, "3": {}, "4": {}}}
Model Number: 23 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 1} and transformations {"fillna": "quadratic", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "DifferencedTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": 365, "transform_dict": null}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 24 with model SeasonalityMotif in generation 0 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "mqae", "k": 1, "datepart_method": "simple", "independent": true} and transformations {"fillna": "ffill", "transformations": {"0": "RollingMean100thN", "1": "AlignLastValue", "2": "PositiveShift", "3": "AlignLastValue", "4": "StandardScaler", "5": "RobustScaler"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": false}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {}, "5": {}}}
Model Number: 25 with model SectionalMotif in generation 0 of 1 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": "AlignLastValue", "1": "CenterSplit", "2": "MinMaxScaler", "3": "bkfilter", "4": "StandardScaler", "5": "DifferencedTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"fillna": "linear", "center": "zero"}, "2": {}, "3": {}, "4": {}, "5": {}}}
Model Number: 26 with model SectionalMotif in generation 0 of 1 with params {"window": 15, "point_method": "midhinge", "distance_metric": "sqeuclidean", "include_differenced": true, "k": 3, "stride_size": 2, "regression_type": null} and transformations {"fillna": "KNNImputer", "transformations": {"0": "RobustScaler"}, "transformation_params": {"0": {}}}
Model Number: 27 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
Model Number: 28 with model AverageValueNaive in generation 0 of 1 with params {"method": "Median", "window": null} and transformations {"fillna": "zero", "transformations": {"0": "MaxAbsScaler"}, "transformation_params": {"0": {}}}
Model Number: 29 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "k": 10, "datepart_method": "expanded", "independent": false} and transformations {"fillna": "quadratic", "transformations": {"0": "AnomalyRemoval", "1": "StandardScaler", "2": "CumSumTransformer", "3": "AlignLastValue", "4": "AnomalyRemoval", "5": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "minmax", "method_params": {"alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "1": {}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {"method": "mad", "method_params": {"distribution": "chi2", "alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "5": {}}}
Model Number: 30 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "akima", "transformations": {"0": "AlignLastValue", "1": "KalmanSmoothing", "2": "ClipOutliers", "3": "CumSumTransformer", "4": "bkfilter"}, "transformation_params": {"0": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"model_name": "MA", "state_transition": [[1, 0], [1, 0]], "process_noise": [[0.2, 0.0], [0.0, 0]], "observation_model": [[1, 0.1]], "observation_noise": 1.0, "em_iter": null}, "2": {"method": "clip", "std_threshold": 2, "fillna": null}, "3": {}, "4": {}}}
Model Number: 31 with model AverageValueNaive in generation 0 of 1 with params {"method": "Weighted_Mean", "window": null} and transformations {"fillna": "cubic", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}}}
Model Number: 32 with model AverageValueNaive in generation 0 of 1 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer", "1": "PositiveShift", "2": "SeasonalDifference", "3": "Slice", "4": "AnomalyRemoval", "5": "AnomalyRemoval"}, "transformation_params": {"0": {}, "1": {}, "2": {"lag_1": 12, "method": 20}, "3": {"method": 0.5}, "4": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": {"fillna": "ffill", "transformations": {"0": "MinMaxScaler", "1": "RobustScaler"}, "transformation_params": {"0": {}, "1": {}}}, "isolated_only": false}, "5": {"method": "IQR", "method_params": {"iqr_threshold": 2.0, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}}}
Model Number: 33 with model AverageValueNaive in generation 0 of 1 with params {"method": "Median", "window": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "LevelShiftTransformer", "1": "CumSumTransformer"}, "transformation_params": {"0": {"window_size": 90, "alpha": 3.0, "grouping_forward_limit": 3, "max_level_shifts": 10, "alignment": "rolling_diff"}, "1": {}}}
Model Number: 34 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "fake_date", "transformations": {"0": "StandardScaler", "1": "StandardScaler", "2": "ClipOutliers"}, "transformation_params": {"0": {}, "1": {}, "2": {"method": "clip", "std_threshold": 3, "fillna": null}}}
Model Number: 35 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 2, "lag_2": 1} and transformations {"fillna": "SeasonalityMotifImputerLinMix", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 36 with model ConstantNaive in generation 0 of 1 with params {"constant": 1} and transformations {"fillna": "ffill", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 37 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "AlignLastValue", "1": "AlignLastValue", "2": "RollingMean100thN", "3": "MaxAbsScaler"}, "transformation_params": {"0": {"rows": 1, "lag": 28, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"rows": 1, "lag": 2, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {}}}
Model Number: 38 with model SeasonalityMotif in generation 0 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "chebyshev", "k": 3, "datepart_method": "recurring", "independent": true} and transformations {"fillna": "cubic", "transformations": {"0": "SeasonalDifference"}, "transformation_params": {"0": {"lag_1": 7, "method": "Mean"}}}
Model Number: 39 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "ClipOutliers", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"method": "clip", "std_threshold": 2, "fillna": null}, "3": {"rows": 1, "lag": 28, "method": "additive", "strength": 0.9, "first_value_only": true}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5401, in inverse_transform
df = self._inverse_one(df, i, trans_method=trans_method, bounds=bounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5356, in _inverse_one
df = self.transformers[i].inverse_transform(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2794, in inverse_transform
df.iloc[0:1] + adjustment,
~~~~~~~~~~~~~^~~~~~~~~~~~
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
return method(self, other)
^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 186, in __add__
return self._arith_method(other, operator.add)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7913, in _arith_method
new_data = self._dispatch_frame_op(other, op, axis=axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7945, in _dispatch_frame_op
bm = self._mgr.apply(array_op, right=right)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 361, in apply
applied = b.apply(f, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 393, in apply
result = func(self.values, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 283, in arithmetic_op
res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 218, in _na_arithmetic_op
result = func(left, right)
^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 242, in evaluate
return _evaluate(op, op_str, a, b) # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 73, in _evaluate_standard
return op(a, b)
^^^^^^^^
TypeError: unsupported operand type(s) for +: 'float' and 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 884, in predict
self.transformer_object.inverse_transform(df_forecast.forecast)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5406, in inverse_transform
raise Exception(err_str) from e
Exception: Transformer AlignLastValue failed on inverse from params rolling_mean_24 {'0': {'method': 'clip', 'std_threshold': 3, 'fillna': None}, '1': {'model': 'GLS', 'phi': 1, 'window': None, 'transform_dict': None}, '2': {'method': 'clip', 'std_threshold': 2, 'fillna': None}, '3': {'rows': 1, 'lag': 28, 'method': 'additive', 'strength': 0.9, 'first_value_only': True}}
in model 39 in generation 0: ConstantNaive
Model Number: 40 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AnomalyRemoval", "1": "ScipyFilter"}, "transformation_params": {"0": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "linear", "transform_dict": null, "isolated_only": true}, "1": {"method": "butter", "method_args": {"N": 5, "window_size": 59, "btype": "lowpass", "analog": false, "output": "sos"}}}}
Model Number: 41 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean", "window": 24} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 42 with model ConstantNaive in generation 0 of 1 with params {"constant": -1} and transformations {"fillna": "nearest", "transformations": {"0": "SeasonalDifference", "1": "SeasonalDifference", "2": "RobustScaler", "3": "CenterSplit"}, "transformation_params": {"0": {"lag_1": 12, "method": "LastValue"}, "1": {"lag_1": 7, "method": "LastValue"}, "2": {}, "3": {"fillna": "akima", "center": "zero"}}}
Model Number: 43 with model SectionalMotif in generation 0 of 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "mahalanobis", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}}}
Model Number: 44 with model SeasonalNaive in generation 0 of 1 with params {"method": "mean", "lag_1": 2, "lag_2": 60} and transformations {"fillna": "ffill", "transformations": {"0": "bkfilter", "1": "AlignLastValue", "2": "Log", "3": "StandardScaler", "4": "AlignLastValue", "5": "EWMAFilter"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {}, "4": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "5": {"span": 7}}}
Model Number: 45 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "rolling_mean", "transformations": {"0": "bkfilter", "1": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 954, in predict
raise ValueError(
ValueError: Model returned NaN due to a preprocessing transformer {'fillna': 'rolling_mean', 'transformations': {'0': 'bkfilter', '1': 'AlignLastValue'}, 'transformation_params': {'0': {}, '1': {'rows': 1, 'lag': 1, 'method': 'multiplicative', 'strength': 1.0, 'first_value_only': False}}}. fail_on_forecast_nan=True
in model 45 in generation 0: ConstantNaive
Model Number: 46 with model SectionalMotif in generation 0 of 1 with params {"window": 7, "point_method": "weighted_mean", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 2, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "FFTFilter", "1": "RobustScaler", "2": "RegressionFilter"}, "transformation_params": {"0": {"cutoff": 0.1, "reverse": false}, "1": {}, "2": {"sigma": 3, "rolling_window": 90, "run_order": "trend_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}}}
Model Number: 47 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "MaxAbsScaler", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": {"fillna": null, "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "uniform", "alpha": 0.05}}}}}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 48 with model SeasonalNaive in generation 0 of 1 with params {"method": "mean", "lag_1": 364, "lag_2": null} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer", "1": "RobustScaler", "2": "bkfilter", "3": "HPFilter", "4": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {}, "2": {}, "3": {"part": "trend", "lamb": 1600}, "4": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 49 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "BKBandpassFilter", "1": "StandardScaler", "2": "ScipyFilter", "3": "RobustScaler", "4": "RegressionFilter"}, "transformation_params": {"0": {"low": 8, "high": 364, "K": 1, "lanczos_factor": false, "return_diff": false}, "1": {}, "2": {"method": "savgol_filter", "method_args": {"window_length": 31, "polyorder": 2, "deriv": 0, "mode": "nearest"}}, "3": {}, "4": {"sigma": 1, "rolling_window": 90, "run_order": "season_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "simple", "polynomial_degree": null, "transform_dict": {"fillna": null, "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 2}}}, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5292, in _fit
df = self._fit_one(df, i)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5273, in _fit_one
df = pd.DataFrame(df, index=self.df_index, columns=self.df_colnames)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 827, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 336, in ndarray_to_mgr
_check_values_indices_shape_match(values, index, columns)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 420, in _check_values_indices_shape_match
raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
ValueError: Shape of passed values is (1139, 4), indices imply (1141, 4)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1446, in model_forecast
model = model.fit(df_train_low, future_regressor_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 847, in fit
df_train_transformed = self.transformer_object._fit(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5297, in _fit
raise Exception(err_str) from e
Exception: Transformer StandardScaler failed on fit from params fake_date {'0': {'low': 8, 'high': 364, 'K': 1, 'lanczos_factor': False, 'return_diff': False}, '1': {}, '2': {'method': 'savgol_filter', 'method_args': {'window_length': 31, 'polyorder': 2, 'deriv': 0, 'mode': 'nearest'}}, '3': {}, '4': {'sigma': 1, 'rolling_window': 90, 'run_order': 'season_first', 'regression_params': {'regression_model': {'model': 'ElasticNet', 'model_params': {}}, 'datepart_method': 'simple', 'polynomial_degree': None, 'transform_dict': {'fillna': None, 'transformations': {'0': 'EWMAFilter'}, 'transformation_params': {'0': {'span': 2}}}, 'holiday_countries_used': False}, 'holiday_params': None, 'trend_method': 'rolling_mean'}}
in model 49 in generation 0: GLS
Model Number: 50 with model SeasonalNaive in generation 0 of 1 with params {"method": "median", "lag_1": 58, "lag_2": 24} and transformations {"fillna": "rolling_mean", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 51 with model SeasonalityMotif in generation 0 of 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "chebyshev", "k": 3, "datepart_method": "simple_2", "independent": false} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "CenterSplit", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"fillna": "mean", "center": "zero"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 52 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "zero", "transformations": {"0": "ClipOutliers", "1": "FFTDecomposition", "2": "Round", "3": "SeasonalDifference", "4": "AlignLastValue", "5": "MinMaxScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"n_harmonics": null, "detrend": "linear"}, "2": {"decimals": 1, "on_transform": true, "on_inverse": true}, "3": {"lag_1": 12, "method": "Mean"}, "4": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": true}, "5": {}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5401, in inverse_transform
df = self._inverse_one(df, i, trans_method=trans_method, bounds=bounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5356, in _inverse_one
df = self.transformers[i].inverse_transform(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2794, in inverse_transform
df.iloc[0:1] + adjustment,
~~~~~~~~~~~~~^~~~~~~~~~~~
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
return method(self, other)
^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 186, in __add__
return self._arith_method(other, operator.add)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7913, in _arith_method
new_data = self._dispatch_frame_op(other, op, axis=axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7945, in _dispatch_frame_op
bm = self._mgr.apply(array_op, right=right)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 361, in apply
applied = b.apply(f, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 393, in apply
result = func(self.values, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 283, in arithmetic_op
res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 218, in _na_arithmetic_op
result = func(left, right)
^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 242, in evaluate
return _evaluate(op, op_str, a, b) # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 73, in _evaluate_standard
return op(a, b)
^^^^^^^^
TypeError: unsupported operand type(s) for +: 'float' and 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 884, in predict
self.transformer_object.inverse_transform(df_forecast.forecast)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5406, in inverse_transform
raise Exception(err_str) from e
Exception: Transformer AlignLastValue failed on inverse from params zero {'0': {'method': 'clip', 'std_threshold': 4, 'fillna': None}, '1': {'n_harmonics': None, 'detrend': 'linear'}, '2': {'decimals': 1, 'on_transform': True, 'on_inverse': True}, '3': {'lag_1': 12, 'method': 'Mean'}, '4': {'rows': 1, 'lag': 7, 'method': 'additive', 'strength': 1.0, 'first_value_only': True}, '5': {}}
in model 52 in generation 0: LastValueNaive
Model Number: 53 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
New Generation: 1 of 1
Model Number: 54 with model SeasonalityMotif in generation 1 of 1 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "canberra", "k": 3, "datepart_method": "recurring", "independent": true} and transformations {"fillna": "quadratic", "transformations": {"0": "AnomalyRemoval", "1": "StandardScaler", "2": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "minmax", "method_params": {"alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "1": {}, "2": {"method": "mad", "method_params": {"distribution": "chi2", "alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}}}
Model Number: 55 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "Discretize", "1": "SinTrend"}, "transformation_params": {"0": {"discretization": "sklearn-uniform", "n_bins": 5}, "1": {}}}
Model Number: 56 with model SectionalMotif in generation 1 of 1 with params {"window": 50, "point_method": "midhinge", "distance_metric": "mahalanobis", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "linear", "transformations": {"0": "Round"}, "transformation_params": {"0": {"decimals": -1, "on_transform": false, "on_inverse": true}}}
Model Number: 57 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "quadratic", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 58 with model AverageValueNaive in generation 1 of 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
Model Number: 59 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ReplaceConstant", "1": "ClipOutliers"}, "transformation_params": {"0": {"constant": 0, "reintroduction_model": {"model": "SGD", "model_params": {}, "datepart_method": "simple_2"}, "fillna": "linear"}, "1": {"method": "clip", "std_threshold": 3, "fillna": null}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5292, in _fit
df = self._fit_one(df, i)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5270, in _fit_one
df = self.transformers[i].fit_transform(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4289, in fit_transform
return self._fit(df)
^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4236, in _fit
self.model.fit(X, y)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 535, in fit
super().fit(X, Y, sample_weight=sample_weight, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 270, in fit
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 67, in __call__
return super().__call__(iterable_with_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1863, in __call__
return output if self.return_generator else list(output)
^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
res = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 129, in __call__
return self.function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 61, in _fit_estimator
estimator.fit(X, y, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 917, in fit
return self._fit(
^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 704, in _fit
self._partial_fit(
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 658, in _partial_fit
raise ValueError(
ValueError: The number of classes has to be greater than one; got 1 class
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1446, in model_forecast
model = model.fit(df_train_low, future_regressor_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 847, in fit
df_train_transformed = self.transformer_object._fit(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5297, in _fit
raise Exception(err_str) from e
Exception: Transformer ReplaceConstant failed on fit from params mean {'0': {'constant': 0, 'reintroduction_model': {'model': 'SGD', 'model_params': {}, 'datepart_method': 'simple_2'}, 'fillna': 'linear'}, '1': {'method': 'clip', 'std_threshold': 3, 'fillna': None}}
in model 59 in generation 1: LastValueNaive
Model Number: 60 with model AverageValueNaive in generation 1 of 1 with params {"method": "Weighted_Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 7}}}
Model Number: 61 with model SeasonalityMotif in generation 1 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "chebyshev", "k": 10, "datepart_method": "simple_2"} and transformations {"fillna": "mean", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 62 with model AverageValueNaive in generation 1 of 1 with params {"method": "Midhinge", "window": null} and transformations {"fillna": "cubic", "transformations": {"0": "MaxAbsScaler"}, "transformation_params": {"0": {}}}
Model Number: 63 with model SectionalMotif in generation 1 of 1 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "StandardScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {}}}
Model Number: 64 with model SectionalMotif in generation 1 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": false, "k": 5, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
Model Number: 65 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "zero", "transformations": {"0": "Discretize", "1": "bkfilter", "2": "AlignLastValue"}, "transformation_params": {"0": {"discretization": "sklearn-uniform", "n_bins": 5}, "1": {}, "2": {"rows": 1, "lag": 2, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 66 with model SeasonalNaive in generation 1 of 1 with params {"method": "mean", "lag_1": 96, "lag_2": 60} and transformations {"fillna": "fake_date", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 67 with model AverageValueNaive in generation 1 of 1 with params {"method": "Median", "window": null} and transformations {"fillna": "ffill", "transformations": {"0": "HistoricValues", "1": "CumSumTransformer", "2": "FFTFilter"}, "transformation_params": {"0": {"window": null}, "1": {}, "2": {"cutoff": 0.05, "reverse": false}}}
Model Number: 68 with model GLS in generation 1 of 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers", "1": "HPFilter", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"part": "trend", "lamb": 1600}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": true}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5401, in inverse_transform
df = self._inverse_one(df, i, trans_method=trans_method, bounds=bounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5356, in _inverse_one
df = self.transformers[i].inverse_transform(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2794, in inverse_transform
df.iloc[0:1] + adjustment,
~~~~~~~~~~~~~^~~~~~~~~~~~
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
return method(self, other)
^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 186, in __add__
return self._arith_method(other, operator.add)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7913, in _arith_method
new_data = self._dispatch_frame_op(other, op, axis=axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7945, in _dispatch_frame_op
bm = self._mgr.apply(array_op, right=right)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 361, in apply
applied = b.apply(f, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 393, in apply
result = func(self.values, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 283, in arithmetic_op
res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 218, in _na_arithmetic_op
result = func(left, right)
^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 242, in evaluate
return _evaluate(op, op_str, a, b) # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 73, in _evaluate_standard
return op(a, b)
^^^^^^^^
TypeError: unsupported operand type(s) for +: 'float' and 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 884, in predict
self.transformer_object.inverse_transform(df_forecast.forecast)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5406, in inverse_transform
raise Exception(err_str) from e
Exception: Transformer AlignLastValue failed on inverse from params rolling_mean {'0': {'method': 'clip', 'std_threshold': 3, 'fillna': None}, '1': {'part': 'trend', 'lamb': 1600}, '2': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': False}, '3': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': False}, '4': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 0.9, 'first_value_only': True}}
in model 68 in generation 1: GLS
Model Number: 69 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "time", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
Model Number: 70 with model SectionalMotif in generation 1 of 1 with params {"window": 7, "point_method": "weighted_mean", "distance_metric": "canberra", "include_differenced": true, "k": 15, "stride_size": 1, "regression_type": "User"} and transformations {"fillna": "zero", "transformations": {"0": "AlignLastValue", "1": "RobustScaler", "2": "MinMaxScaler", "3": "bkfilter", "4": "StandardScaler"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {}, "2": {}, "3": {}, "4": {}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1446, in model_forecast
model = model.fit(df_train_low, future_regressor_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 855, in fit
self.model = self.model.fit(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/models/basics.py", line 1828, in fit
raise ValueError(
ValueError: regression_type=='User' but no future_regressor supplied
in model 70 in generation 1: SectionalMotif
Model Number: 71 with model SectionalMotif in generation 1 of 1 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": "StandardScaler", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 72 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "zero", "transformations": {"0": "ReplaceConstant", "1": "MaxAbsScaler", "2": "LocalLinearTrend"}, "transformation_params": {"0": {"constant": 0, "reintroduction_model": {"model": "SGD", "model_params": {}, "datepart_method": "simple_2"}, "fillna": "linear"}, "1": {}, "2": {"rolling_window": 0.1, "n_tails": 30, "n_future": 0.2, "method": "median", "macro_micro": true}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5292, in _fit
df = self._fit_one(df, i)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5270, in _fit_one
df = self.transformers[i].fit_transform(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4289, in fit_transform
return self._fit(df)
^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4236, in _fit
self.model.fit(X, y)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 535, in fit
super().fit(X, Y, sample_weight=sample_weight, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 270, in fit
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 67, in __call__
return super().__call__(iterable_with_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1863, in __call__
return output if self.return_generator else list(output)
^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
res = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 129, in __call__
return self.function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 61, in _fit_estimator
estimator.fit(X, y, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 917, in fit
return self._fit(
^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 704, in _fit
self._partial_fit(
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 658, in _partial_fit
raise ValueError(
ValueError: The number of classes has to be greater than one; got 1 class
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1446, in model_forecast
model = model.fit(df_train_low, future_regressor_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 847, in fit
df_train_transformed = self.transformer_object._fit(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5297, in _fit
raise Exception(err_str) from e
Exception: Transformer ReplaceConstant failed on fit from params zero {'0': {'constant': 0, 'reintroduction_model': {'model': 'SGD', 'model_params': {}, 'datepart_method': 'simple_2'}, 'fillna': 'linear'}, '1': {}, '2': {'rolling_window': 0.1, 'n_tails': 30, 'n_future': 0.2, 'method': 'median', 'macro_micro': True}}
in model 72 in generation 1: LastValueNaive
Model Number: 73 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "cubic", "transformations": {"0": "ClipOutliers", "1": "EWMAFilter"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"span": 4}}}
Model Number: 74 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 1} and transformations {"fillna": "mean", "transformations": {"0": "QuantileTransformer", "1": "ClipOutliers"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"method": "clip", "std_threshold": 2, "fillna": null}}}
Model Number: 75 with model SeasonalityMotif in generation 1 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "mqae", "k": 5, "datepart_method": "simple", "independent": false} and transformations {"fillna": "fake_date", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
Model Number: 76 with model AverageValueNaive in generation 1 of 1 with params {"method": "Median", "window": 7} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 77 with model AverageValueNaive in generation 1 of 1 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "zero", "transformations": {"0": "RobustScaler", "1": "PositiveShift", "2": "SeasonalDifference", "3": "Slice"}, "transformation_params": {"0": {}, "1": {}, "2": {"lag_1": 12, "method": 20}, "3": {"method": 0.5}}}
Model Number: 78 with model SectionalMotif in generation 1 of 1 with params {"window": 15, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": false, "k": 3, "stride_size": 2, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "MaxAbsScaler", "1": "RobustScaler", "2": "QuantileTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {}, "2": {"output_distribution": "normal", "n_quantiles": 1000}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.2, "first_value_only": false}}}
Model Number: 79 with model SeasonalityMotif in generation 1 of 1 with params {"window": 5, "point_method": "weighted_mean", "distance_metric": "mae", "k": 10, "datepart_method": "common_fourier"} and transformations {"fillna": "median", "transformations": {"0": "AnomalyRemoval", "1": "StandardScaler", "2": "ScipyFilter", "3": "MinMaxScaler"}, "transformation_params": {"0": {"method": "minmax", "method_params": {"alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "1": {}, "2": {"method": "savgol_filter", "method_args": {"window_length": 31, "polyorder": 2, "deriv": 0, "mode": "nearest"}}, "3": {}}}
Model Number: 80 with model AverageValueNaive in generation 1 of 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "KNNImputer", "transformations": {"0": "bkfilter", "1": "PositiveShift", "2": "SeasonalDifference", "3": "Slice", "4": "AnomalyRemoval"}, "transformation_params": {"0": {}, "1": {}, "2": {"lag_1": 12, "method": 20}, "3": {"method": 0.5}, "4": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": {"fillna": "ffill", "transformations": {"0": "MinMaxScaler", "1": "RobustScaler"}, "transformation_params": {"0": {}, "1": {}}}, "isolated_only": false}}}
Model Number: 81 with model SeasonalityMotif in generation 1 of 1 with params {"window": 5, "point_method": "weighted_mean", "distance_metric": "mae", "k": 10, "datepart_method": "common_fourier"} and transformations {"fillna": "median", "transformations": {"0": "AnomalyRemoval", "1": "ScipyFilter"}, "transformation_params": {"0": {"method": "IQR", "method_params": {"iqr_threshold": 2.0, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "1": {"method": "savgol_filter", "method_args": {"window_length": 31, "polyorder": 2, "deriv": 0, "mode": "nearest"}}}}
Model Number: 82 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 58, "lag_2": 24} and transformations {"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "QuantileTransformer", "2": "Detrend", "3": "FFTDecomposition", "4": "CenterSplit"}, "transformation_params": {"0": {"lag_1": 12, "method": "Median"}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"model": "GLS"}, "3": {"n_harmonics": "mid10", "detrend": "quadratic"}, "4": {"fillna": "ffill", "center": "zero"}}}
Model Number: 83 with model SectionalMotif in generation 1 of 1 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 2, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "BKBandpassFilter", "1": "RobustScaler", "2": "RegressionFilter"}, "transformation_params": {"0": {"low": 8, "high": 32, "K": 6, "lanczos_factor": false, "return_diff": true}, "1": {}, "2": {"sigma": 3, "rolling_window": 90, "run_order": "trend_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}}}
Model Number: 84 with model AverageValueNaive in generation 1 of 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
Model Number: 85 with model AverageValueNaive in generation 1 of 1 with params {"method": "Median", "window": null} and transformations {"fillna": "ffill", "transformations": {"0": "MaxAbsScaler", "1": "PositiveShift", "2": "SeasonalDifference", "3": "Slice", "4": "AnomalyRemoval", "5": "AnomalyRemoval"}, "transformation_params": {"0": {}, "1": {}, "2": {"lag_1": 12, "method": 20}, "3": {"method": 0.5}, "4": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": {"fillna": "ffill", "transformations": {"0": "MinMaxScaler", "1": "RobustScaler"}, "transformation_params": {"0": {}, "1": {}}}, "isolated_only": false}, "5": {"method": "IQR", "method_params": {"iqr_threshold": 2.0, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}}}
Model Number: 86 with model SectionalMotif in generation 1 of 1 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "SeasonalDifference", "2": "StandardScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"lag_1": 35, "method": "Median"}, "2": {}}}
Model Number: 87 with model SeasonalityMotif in generation 1 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "k": 10, "datepart_method": "expanded"} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "CenterSplit"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"fillna": "mean", "center": "zero"}}}
Model Number: 88 with model SeasonalNaive in generation 1 of 1 with params {"method": "median", "lag_1": 58, "lag_2": 364} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
TotalRuntime missing in 2!
Validation Round: 1
Validation train index is DatetimeIndex(['2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',
'2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',
'2020-03-02', '2020-03-03',
...
'2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03',
'2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07',
'2023-03-08', '2023-03-09'],
dtype='datetime64[ns]', name='Date', length=1111, freq=None)
Model Number: 1 of 15 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
📈 1 - LastValueNaive with avg smape 51.85:
Model Number: 2 of 15 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "time", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
2 - LastValueNaive with avg smape 51.85:
Model Number: 3 of 15 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
📈 3 - LastValueNaive with avg smape 51.7:
Model Number: 4 of 15 with model AverageValueNaive for Validation 1 with params {"method": "Median", "window": 7} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
4 - AverageValueNaive with avg smape 51.85:
Model Number: 5 of 15 with model SectionalMotif for Validation 1 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": "AlignLastValue", "1": "CenterSplit", "2": "MinMaxScaler", "3": "bkfilter", "4": "StandardScaler", "5": "DifferencedTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"fillna": "linear", "center": "zero"}, "2": {}, "3": {}, "4": {}, "5": {}}}
5 - SectionalMotif with avg smape 73.34:
Model Number: 6 of 15 with model SeasonalityMotif for Validation 1 with params {"window": 5, "point_method": "weighted_mean", "distance_metric": "mae", "k": 10, "datepart_method": "common_fourier"} and transformations {"fillna": "nearest", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
📈 6 - SeasonalityMotif with avg smape 50.58:
Model Number: 7 of 15 with model ConstantNaive for Validation 1 with params {"constant": 0} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
7 - ConstantNaive with avg smape 51.85:
Model Number: 8 of 15 with model AverageValueNaive for Validation 1 with params {"method": "Mean", "window": 24} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
8 - AverageValueNaive with avg smape 51.85:
Model Number: 9 of 15 with model SectionalMotif for Validation 1 with params {"window": 7, "point_method": "weighted_mean", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 2, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "FFTFilter", "1": "RobustScaler", "2": "RegressionFilter"}, "transformation_params": {"0": {"cutoff": 0.1, "reverse": false}, "1": {}, "2": {"sigma": 3, "rolling_window": 90, "run_order": "trend_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}}}
9 - SectionalMotif with avg smape 64.8:
Model Number: 10 of 15 with model SectionalMotif for Validation 1 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "StandardScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {}}}
10 - SectionalMotif with avg smape 81.74:
Model Number: 11 of 15 with model AverageValueNaive for Validation 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
11 - AverageValueNaive with avg smape 51.89:
Model Number: 12 of 15 with model SeasonalityMotif for Validation 1 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "canberra", "k": 3, "datepart_method": "recurring"} and transformations {"fillna": "quadratic", "transformations": {"0": "AnomalyRemoval", "1": "StandardScaler", "2": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "minmax", "method_params": {"alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "1": {}, "2": {"method": "mad", "method_params": {"distribution": "chi2", "alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}}}
12 - SeasonalityMotif with avg smape 81.89:
Model Number: 13 of 15 with model SeasonalityMotif for Validation 1 with params {"window": 10, "point_method": "mean", "distance_metric": "mqae", "k": 5, "datepart_method": "simple"} and transformations {"fillna": "fake_date", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
13 - SeasonalityMotif with avg smape 65.64:
Model Number: 14 of 15 with model GLS for Validation 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "MaxAbsScaler", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": {"fillna": null, "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "uniform", "alpha": 0.05}}}}}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
14 - GLS with avg smape 53.23:
Model Number: 15 of 15 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "Discretize", "1": "SinTrend"}, "transformation_params": {"0": {"discretization": "sklearn-uniform", "n_bins": 5}, "1": {}}}
15 - LastValueNaive with avg smape 64.67:
Validation Round: 2
Validation train index is DatetimeIndex(['2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',
'2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',
'2020-03-02', '2020-03-03',
...
'2023-01-29', '2023-01-30', '2023-01-31', '2023-02-01',
'2023-02-02', '2023-02-03', '2023-02-04', '2023-02-05',
'2023-02-06', '2023-02-07'],
dtype='datetime64[ns]', name='Date', length=1081, freq=None)
Model Number: 1 of 15 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
📈 1 - LastValueNaive with avg smape 50.25:
Model Number: 2 of 15 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "time", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
2 - LastValueNaive with avg smape 50.25:
Model Number: 3 of 15 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
📈 3 - LastValueNaive with avg smape 49.96:
Model Number: 4 of 15 with model AverageValueNaive for Validation 2 with params {"method": "Median", "window": 7} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
4 - AverageValueNaive with avg smape 50.25:
Model Number: 5 of 15 with model SectionalMotif for Validation 2 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": "AlignLastValue", "1": "CenterSplit", "2": "MinMaxScaler", "3": "bkfilter", "4": "StandardScaler", "5": "DifferencedTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"fillna": "linear", "center": "zero"}, "2": {}, "3": {}, "4": {}, "5": {}}}
5 - SectionalMotif with avg smape 53.37:
Model Number: 6 of 15 with model SeasonalityMotif for Validation 2 with params {"window": 5, "point_method": "weighted_mean", "distance_metric": "mae", "k": 10, "datepart_method": "common_fourier"} and transformations {"fillna": "nearest", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
6 - SeasonalityMotif with avg smape 62.55:
Model Number: 7 of 15 with model ConstantNaive for Validation 2 with params {"constant": 0} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
7 - ConstantNaive with avg smape 50.25:
Model Number: 8 of 15 with model AverageValueNaive for Validation 2 with params {"method": "Mean", "window": 24} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
8 - AverageValueNaive with avg smape 50.25:
Model Number: 9 of 15 with model SectionalMotif for Validation 2 with params {"window": 7, "point_method": "weighted_mean", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 2, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "FFTFilter", "1": "RobustScaler", "2": "RegressionFilter"}, "transformation_params": {"0": {"cutoff": 0.1, "reverse": false}, "1": {}, "2": {"sigma": 3, "rolling_window": 90, "run_order": "trend_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}}}
9 - SectionalMotif with avg smape 80.87:
Model Number: 10 of 15 with model SectionalMotif for Validation 2 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "StandardScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {}}}
10 - SectionalMotif with avg smape 70.76:
Model Number: 11 of 15 with model AverageValueNaive for Validation 2 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
11 - AverageValueNaive with avg smape 50.94:
Model Number: 12 of 15 with model SeasonalityMotif for Validation 2 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "canberra", "k": 3, "datepart_method": "recurring"} and transformations {"fillna": "quadratic", "transformations": {"0": "AnomalyRemoval", "1": "StandardScaler", "2": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "minmax", "method_params": {"alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "1": {}, "2": {"method": "mad", "method_params": {"distribution": "chi2", "alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}}}
12 - SeasonalityMotif with avg smape 53.79:
Model Number: 13 of 15 with model SeasonalityMotif for Validation 2 with params {"window": 10, "point_method": "mean", "distance_metric": "mqae", "k": 5, "datepart_method": "simple"} and transformations {"fillna": "fake_date", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
13 - SeasonalityMotif with avg smape 59.26:
Model Number: 14 of 15 with model GLS for Validation 2 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "MaxAbsScaler", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": {"fillna": null, "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "uniform", "alpha": 0.05}}}}}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
14 - GLS with avg smape 50.83:
Model Number: 15 of 15 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "Discretize", "1": "SinTrend"}, "transformation_params": {"0": {"discretization": "sklearn-uniform", "n_bins": 5}, "1": {}}}
📈 15 - LastValueNaive with avg smape 36.09:
TotalRuntime missing in 3!
Validation Round: 1
Validation train index is DatetimeIndex(['2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',
'2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',
'2020-03-02', '2020-03-03',
...
'2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03',
'2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07',
'2023-03-08', '2023-03-09'],
dtype='datetime64[ns]', name='Date', length=1111, freq=None)
TotalRuntime missing in 0!
Validation Round: 2
Validation train index is DatetimeIndex(['2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',
'2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',
'2020-03-02', '2020-03-03',
...
'2023-01-29', '2023-01-30', '2023-01-31', '2023-02-01',
'2023-02-02', '2023-02-03', '2023-02-04', '2023-02-05',
'2023-02-06', '2023-02-07'],
dtype='datetime64[ns]', name='Date', length=1081, freq=None)
TotalRuntime missing in 0!
Model Number: 1 with model Ensemble in generation 0 of Horizontal Ensembles with params {"model_name": "Horizontal", "model_count": 4, "model_metric": "Score", "models": {"7a14af550afa2194472cfc2e4e1440fb": {"Model": "LastValueNaive", "ModelParameters": "{}", "TransformationParameters": "{\"fillna\": \"mean\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"QuantileTransformer\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 1, \"fillna\": null}, \"1\": {\"output_distribution\": \"uniform\", \"n_quantiles\": 1000}}}"}, "da9cb9cb573cb2a2db24742c0af6a220": {"Model": "SectionalMotif", "ModelParameters": "{\"window\": 50, \"point_method\": \"weighted_mean\", \"distance_metric\": \"braycurtis\", \"include_differenced\": true, \"k\": 5, \"stride_size\": 10, \"regression_type\": null}", "TransformationParameters": "{\"fillna\": \"zero\", \"transformations\": {\"0\": \"AlignLastValue\", \"1\": \"CenterSplit\", \"2\": \"MinMaxScaler\", \"3\": \"bkfilter\", \"4\": \"StandardScaler\", \"5\": \"DifferencedTransformer\"}, \"transformation_params\": {\"0\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false}, \"1\": {\"fillna\": \"linear\", \"center\": \"zero\"}, \"2\": {}, \"3\": {}, \"4\": {}, \"5\": {}}}"}, "6686fac09a576355300eb3aa05c72841": {"Model": "GLS", "ModelParameters": "{}", "TransformationParameters": "{\"fillna\": \"rolling_mean\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"Detrend\", \"2\": \"MaxAbsScaler\", \"3\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 3, \"fillna\": null}, \"1\": {\"model\": \"GLS\", \"phi\": 1, \"window\": null, \"transform_dict\": {\"fillna\": null, \"transformations\": {\"0\": \"AnomalyRemoval\"}, \"transformation_params\": {\"0\": {\"method\": \"zscore\", \"transform_dict\": {\"transformations\": {\"0\": \"DatepartRegression\"}, \"transformation_params\": {\"0\": {\"datepart_method\": \"simple_3\", \"regression_model\": {\"model\": \"ElasticNet\", \"model_params\": {}}}}}, \"method_params\": {\"distribution\": \"uniform\", \"alpha\": 0.05}}}}}, \"2\": {}, \"3\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false}}}"}, "d761ba4cd045f2beba0050a1aa9006ee": {"Model": "LastValueNaive", "ModelParameters": "{}", "TransformationParameters": "{\"fillna\": \"fake_date\", \"transformations\": {\"0\": \"Discretize\", \"1\": \"SinTrend\"}, \"transformation_params\": {\"0\": {\"discretization\": \"sklearn-uniform\", \"n_bins\": 5}, \"1\": {}}}"}}, "series": {"theta": "d761ba4cd045f2beba0050a1aa9006ee", "kappa": "da9cb9cb573cb2a2db24742c0af6a220", "rho": "7a14af550afa2194472cfc2e4e1440fb", "sigma": "6686fac09a576355300eb3aa05c72841"}} and transformations {}
Ensemble Horizontal component 1 of 4 LastValueNaive started
Ensemble Horizontal component 2 of 4 SectionalMotif started
Ensemble Horizontal component 3 of 4 GLS started
Ensemble Horizontal component 4 of 4 LastValueNaive started
Ensemble Horizontal component 1 of 4 LastValueNaive started
Ensemble Horizontal component 2 of 4 SectionalMotif started
Ensemble Horizontal component 3 of 4 GLS started
Ensemble Horizontal component 4 of 4 LastValueNaive started
2024-04-27 at 02:55:34 | ERROR | validation of end failed
2024-04-27 at 02:55:34 | ERROR | validation of end failed
2024-04-27 at 02:55:34 | ERROR | validation of end failed
Check the predicted ODE parameter values.
[6]:
df = snr.append().summary()
df.loc[df["Start"] >= future_start_date]
2024-04-27 at 02:55:34 | ERROR | validation of end failed
2024-04-27 at 02:55:34 | ERROR | validation of end failed
[6]:
| Start | End | Rt | theta | kappa | rho | sigma | alpha1 [-] | 1/alpha2 [day] | 1/beta [day] | 1/gamma [day] | ODE | tau | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Scenario | Phase | |||||||||||||
| Predicted | 68th | 2023-05-09 | 2023-05-12 | 1.58 | 0.002127 | 0.000003 | 0.001392 | 0.000879 | 0.002 | 5977 | 12 | 19 | SIR-F Model | 24 |
| 69th | 2023-05-13 | 2023-06-04 | 1.57 | 0.004922 | 0.000005 | 0.001392 | 0.00088 | 0.005 | 3537 | 12 | 19 | SIR-F Model | 24 | |
| 70th | 2023-06-05 | 2023-06-07 | 1.56 | 0.00524 | 0.000005 | 0.001392 | 0.000881 | 0.005 | 3353 | 12 | 19 | SIR-F Model | 24 |
Check the dynamics.
[7]:
snr.simulate(name="Predicted");
2. Time-series prediction with indicators
using `future_regressor functionality of AutoTS <https://winedarksea.github.io/AutoTS/build/html/source/tutorial.html#adding-regressors-and-other-information>`__, we will predict ODE parameter values with indicators. We can download/create time-series data of indicators using DataEngineer class as explained in Tutorial: Data preparation and Tutorial: Data
engineering.
[8]:
data_eng = cs.DataEngineer()
data_eng.download(databases=["japan", "covid19dh", "owid"]).clean().transform()
subset_df, *_ = data_eng.subset(geo="Japan")
indicator_df = subset_df.drop(["Population", "Susceptible", "Confirmed", "Infected", "Fatal", "Recovered"], axis=1)
indicator_df
[8]:
| Cancel_events | Contact_tracing | Gatherings_restrictions | Information_campaigns | Internal_movement_restrictions | International_movement_restrictions | School_closing | Stay_home_restrictions | Stringency_index | Testing_policy | ... | Transport_closing | Vaccinated_full | Vaccinated_once | Vaccinations | Vaccinations_boosters | Workplace_closing | Country_0 | Country_Japan | Product_0 | Product_Moderna, Novavax, Oxford/AstraZeneca, Pfizer/BioNTech | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||||||||||||||
| 2020-02-05 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 0 | 1 | 0 |
| 2020-02-06 | 0.0 | 2.0 | 0.0 | 2.0 | 0.0 | 3.0 | 0.0 | 0.0 | 19.44 | 1.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 1 | 1 | 0 |
| 2020-02-07 | 0.0 | 2.0 | 0.0 | 2.0 | 0.0 | 3.0 | 0.0 | 0.0 | 19.44 | 1.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 1 | 1 | 0 |
| 2020-02-08 | 0.0 | 2.0 | 0.0 | 2.0 | 0.0 | 3.0 | 0.0 | 0.0 | 19.44 | 1.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 1 | 1 | 0 |
| 2020-02-09 | 0.0 | 2.0 | 0.0 | 2.0 | 0.0 | 3.0 | 0.0 | 0.0 | 19.44 | 1.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 1 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2023-05-04 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 33.33 | 1.0 | ... | 0.0 | 63889986991.0 | 67171434888.0 | 181897083005.0 | 50835661126.0 | 1.0 | 0 | 1 | 0 | 1 |
| 2023-05-05 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 33.33 | 1.0 | ... | 0.0 | 63993367323.0 | 67276140121.0 | 182280782416.0 | 51011274972.0 | 1.0 | 0 | 1 | 0 | 1 |
| 2023-05-06 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 33.33 | 1.0 | ... | 0.0 | 64096748099.0 | 67380845596.0 | 182664482513.0 | 51186888818.0 | 1.0 | 0 | 1 | 0 | 1 |
| 2023-05-07 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 33.33 | 1.0 | ... | 0.0 | 64200128969.0 | 67485551123.0 | 183048182756.0 | 51362502664.0 | 1.0 | 0 | 1 | 0 | 1 |
| 2023-05-08 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 33.33 | 1.0 | ... | 0.0 | 64303509961.0 | 67590256732.0 | 183431912857.0 | 51538146164.0 | 1.0 | 0 | 1 | 0 | 1 |
1189 rows × 21 columns
2.1 Principal Component Analysis
To remove multicollinearity of indicators, we use pca package: a python package to perform Principal Component Analysis and to create insightful plots via our MLEngineer(seed=0).pca(X, n_components). Standardization (Z-score normalization) and Principal Component Analysis (PCA) will be performed.
[9]:
ml = cs.MLEngineer()
pca_dict = ml.pca(X=indicator_df, n_components=0.95)
pca_df = pca_dict["PC"].copy()
pca_df.tail()
[9]:
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | PC10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-05-04 | 5.673492 | 2.243826 | -0.555292 | -0.134307 | 0.888876 | -0.020971 | 0.364962 | 0.630145 | 0.039768 | -0.324168 |
| 2023-05-05 | 5.683267 | 2.247735 | -0.557146 | -0.134422 | 0.891652 | -0.019268 | 0.363681 | 0.632074 | 0.041499 | -0.325286 |
| 2023-05-06 | 5.693041 | 2.251644 | -0.558999 | -0.134538 | 0.894427 | -0.017564 | 0.362401 | 0.634004 | 0.043231 | -0.326404 |
| 2023-05-07 | 5.702815 | 2.255553 | -0.560853 | -0.134654 | 0.897203 | -0.015860 | 0.361120 | 0.635933 | 0.044962 | -0.327522 |
| 2023-05-08 | 5.712591 | 2.259462 | -0.562707 | -0.134770 | 0.899979 | -0.014157 | 0.359839 | 0.637862 | 0.046694 | -0.328641 |
The output of MLEngineer().pca() is the model of pca package, we can show some figures easily as follows.
Explained variance:
[10]:
pca_dict["model"].plot()
plt.close()
Top features:
[11]:
df = pca_dict["topfeat"].copy()
df["PC"] = df["PC"].str.extract(r"(\d+)").astype(np.int64)
df = df.sort_values(["PC", "type"]).reset_index(drop=True)
def highlight(d):
styles = d.copy()
styles.loc[:, :] = ""
styles.loc[d["type"] == "best", :] = "background-color: yellow"
return styles
df.style.apply(highlight, axis=None)
[11]:
| PC | feature | loading | type | |
|---|---|---|---|---|
| 0 | 1 | Vaccinated_once | 0.347669 | best |
| 1 | 1 | Contact_tracing | -0.294071 | weak |
| 2 | 1 | Information_campaigns | -0.281398 | weak |
| 3 | 1 | Vaccinated_full | 0.346382 | weak |
| 4 | 1 | Vaccinations | 0.343559 | weak |
| 5 | 1 | Vaccinations_boosters | 0.308128 | weak |
| 6 | 2 | Stringency_index | -0.438532 | best |
| 7 | 2 | International_movement_restrictions | -0.313789 | weak |
| 8 | 2 | Product_0 | 0.318462 | weak |
| 9 | 2 | Product_Moderna, Novavax, Oxford/AstraZeneca, Pfizer/BioNTech | -0.318462 | weak |
| 10 | 3 | Country_0 | 0.677881 | best |
| 11 | 3 | Country_Japan | -0.677881 | weak |
| 12 | 4 | School_closing | 0.558851 | best |
| 13 | 5 | Transport_closing | 0.554676 | best |
| 14 | 6 | Workplace_closing | -0.650936 | best |
| 15 | 7 | Tests | -0.570970 | best |
| 16 | 8 | Cancel_events | -0.513032 | best |
| 17 | 9 | Testing_policy | 0.664159 | best |
| 18 | 10 | Gatherings_restrictions | 0.525608 | best |
| 19 | 10 | Internal_movement_restrictions | 0.331885 | weak |
| 20 | 10 | Stay_home_restrictions | -0.421741 | weak |
2-2. Future values of indicators
Before prediction of ODE parameter values, we need to prepare future values of (PCA-performed) indicators. We can add future values to the pandas.DataFrame manually or forecast them with MLEngineer(seed=0).predict(Y, days=<int>, X=None) as follows.
[12]:
future_df = ml.forecast(Y=pca_df, days=30, X=None)
future_df.tail()
Using 1 cpus for n_jobs.
Data frequency is: D, used frequency is: D
Model Number: 1 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "fake_date", "transformations": {"0": "DifferencedTransformer", "1": "SinTrend"}, "transformation_params": {"0": {}, "1": {}}}
Model Number: 2 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 3 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SeasonalDifference", "1": "Round", "2": "Detrend"}, "transformation_params": {"0": {"lag_1": 7, "method": "Mean"}, "1": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": false}, "2": {"model": "GLS"}}}
Model Number: 4 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "RollingMeanTransformer", "1": "DifferencedTransformer", "2": "Detrend", "3": "Slice"}, "transformation_params": {"0": {"fixed": true, "window": 3}, "1": {}, "2": {"model": "Linear"}, "3": {"method": 100}}}
Model Number: 5 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "median", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "RobustScaler", "3": "Round", "4": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}, "3": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": true}, "4": {}}}
Model Number: 6 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "bkfilter", "1": "SinTrend", "2": "Detrend", "3": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {}, "2": {"model": "Linear"}, "3": {}}}
Model Number: 7 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "PositiveShift", "1": "SinTrend", "2": "bkfilter"}, "transformation_params": {"0": {}, "1": {}, "2": {}}}
Model Number: 8 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "SinTrend"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}}}
Model Number: 9 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 10 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 2, "lag_2": 7} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SinTrend", "1": "Round", "2": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {"model": "middle", "decimals": 2, "on_transform": false, "on_inverse": true}, "2": {}}}
Model Number: 11 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 2, "lag_2": 1} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SeasonalDifference", "1": "QuantileTransformer", "2": "Detrend"}, "transformation_params": {"0": {"lag_1": 12, "method": "Median"}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"model": "GLS"}}}
Model Number: 12 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 7, "lag_2": 2} and transformations {"fillna": "mean", "transformations": {"0": "QuantileTransformer", "1": "ClipOutliers"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"method": "clip", "std_threshold": 2, "fillna": null}}}
Model Number: 13 with model ConstantNaive in generation 0 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "PowerTransformer", "1": "QuantileTransformer", "2": "SeasonalDifference"}, "transformation_params": {"0": {}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"lag_1": 7, "method": "LastValue"}}}
Model Number: 14 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 28} and transformations {"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 15 with model SectionalMotif in generation 0 of 1 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "sokalmichener", "include_differenced": true, "k": 20, "stride_size": 1, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 16 with model SectionalMotif in generation 0 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": false, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 17 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 30} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 18 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "weighted_mean", "distance_metric": "mae", "k": 10, "datepart_method": "common_fourier"} and transformations {"fillna": "nearest", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 19 with model ConstantNaive in generation 0 of 1 with params {"constant": 1} and transformations {"fillna": "zero", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": 30, "transform_dict": null}, "2": {"method": "zscore", "method_params": {"distribution": "chi2", "alpha": 0.1}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": "quadratic", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}, "isolated_only": false}}}
Model Number: 20 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "KNNImputer", "transformations": {"0": "bkfilter", "1": "Discretize", "2": "QuantileTransformer", "3": "LevelShiftTransformer"}, "transformation_params": {"0": {}, "1": {"discretization": "center", "n_bins": 5}, "2": {"output_distribution": "uniform", "n_quantiles": 1000}, "3": {"window_size": 90, "alpha": 3.0, "grouping_forward_limit": 2, "max_level_shifts": 30, "alignment": "average"}}}
Model Number: 21 with model AverageValueNaive in generation 0 of 1 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "time", "transformations": {"0": "RollingMean100thN", "1": "AlignLastValue", "2": "RobustScaler", "3": "Round", "4": "MaxAbsScaler"}, "transformation_params": {"0": {}, "1": {"rows": 7, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {"decimals": -1, "on_transform": true, "on_inverse": false}, "4": {}}}
Model Number: 22 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "RollingMeanTransformer", "1": "AlignLastValue", "2": "Round", "3": "PctChangeTransformer", "4": "MaxAbsScaler"}, "transformation_params": {"0": {"fixed": false, "window": 7, "macro_micro": false, "center": true}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"decimals": 1, "on_transform": true, "on_inverse": false}, "3": {}, "4": {}}}
Model Number: 23 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 1} and transformations {"fillna": "quadratic", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "DifferencedTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": 365, "transform_dict": null}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 24 with model SeasonalityMotif in generation 0 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "mqae", "k": 1, "datepart_method": "simple", "independent": true} and transformations {"fillna": "ffill", "transformations": {"0": "RollingMean100thN", "1": "AlignLastValue", "2": "PositiveShift", "3": "AlignLastValue", "4": "StandardScaler", "5": "RobustScaler"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": false}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {}, "5": {}}}
Model Number: 25 with model SectionalMotif in generation 0 of 1 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": "AlignLastValue", "1": "CenterSplit", "2": "MinMaxScaler", "3": "bkfilter", "4": "StandardScaler", "5": "DifferencedTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"fillna": "linear", "center": "zero"}, "2": {}, "3": {}, "4": {}, "5": {}}}
Model Number: 26 with model SectionalMotif in generation 0 of 1 with params {"window": 15, "point_method": "midhinge", "distance_metric": "sqeuclidean", "include_differenced": true, "k": 3, "stride_size": 2, "regression_type": null} and transformations {"fillna": "KNNImputer", "transformations": {"0": "RobustScaler"}, "transformation_params": {"0": {}}}
Model Number: 27 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
Model Number: 28 with model AverageValueNaive in generation 0 of 1 with params {"method": "Median", "window": null} and transformations {"fillna": "zero", "transformations": {"0": "MaxAbsScaler"}, "transformation_params": {"0": {}}}
Model Number: 29 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "k": 10, "datepart_method": "expanded", "independent": false} and transformations {"fillna": "quadratic", "transformations": {"0": "AnomalyRemoval", "1": "StandardScaler", "2": "CumSumTransformer", "3": "AlignLastValue", "4": "AnomalyRemoval", "5": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "minmax", "method_params": {"alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "1": {}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {"method": "mad", "method_params": {"distribution": "chi2", "alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "5": {}}}
Model Number: 30 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "akima", "transformations": {"0": "AlignLastValue", "1": "KalmanSmoothing", "2": "ClipOutliers", "3": "CumSumTransformer", "4": "bkfilter"}, "transformation_params": {"0": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"model_name": "MA", "state_transition": [[1, 0], [1, 0]], "process_noise": [[0.2, 0.0], [0.0, 0]], "observation_model": [[1, 0.1]], "observation_noise": 1.0, "em_iter": null}, "2": {"method": "clip", "std_threshold": 2, "fillna": null}, "3": {}, "4": {}}}
Model Number: 31 with model AverageValueNaive in generation 0 of 1 with params {"method": "Weighted_Mean", "window": null} and transformations {"fillna": "cubic", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}}}
Model Number: 32 with model AverageValueNaive in generation 0 of 1 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer", "1": "PositiveShift", "2": "SeasonalDifference", "3": "Slice", "4": "AnomalyRemoval", "5": "AnomalyRemoval"}, "transformation_params": {"0": {}, "1": {}, "2": {"lag_1": 12, "method": 20}, "3": {"method": 0.5}, "4": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": {"fillna": "ffill", "transformations": {"0": "MinMaxScaler", "1": "RobustScaler"}, "transformation_params": {"0": {}, "1": {}}}, "isolated_only": false}, "5": {"method": "IQR", "method_params": {"iqr_threshold": 2.0, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}}}
Model Number: 33 with model AverageValueNaive in generation 0 of 1 with params {"method": "Median", "window": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "LevelShiftTransformer", "1": "CumSumTransformer"}, "transformation_params": {"0": {"window_size": 90, "alpha": 3.0, "grouping_forward_limit": 3, "max_level_shifts": 10, "alignment": "rolling_diff"}, "1": {}}}
Model Number: 34 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "fake_date", "transformations": {"0": "StandardScaler", "1": "StandardScaler", "2": "ClipOutliers"}, "transformation_params": {"0": {}, "1": {}, "2": {"method": "clip", "std_threshold": 3, "fillna": null}}}
Model Number: 35 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 2, "lag_2": 1} and transformations {"fillna": "SeasonalityMotifImputerLinMix", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 36 with model ConstantNaive in generation 0 of 1 with params {"constant": 1} and transformations {"fillna": "ffill", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 37 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "AlignLastValue", "1": "AlignLastValue", "2": "RollingMean100thN", "3": "MaxAbsScaler"}, "transformation_params": {"0": {"rows": 1, "lag": 28, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"rows": 1, "lag": 2, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {}}}
Model Number: 38 with model SeasonalityMotif in generation 0 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "chebyshev", "k": 3, "datepart_method": "recurring", "independent": true} and transformations {"fillna": "cubic", "transformations": {"0": "SeasonalDifference"}, "transformation_params": {"0": {"lag_1": 7, "method": "Mean"}}}
Model Number: 39 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "ClipOutliers", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"method": "clip", "std_threshold": 2, "fillna": null}, "3": {"rows": 1, "lag": 28, "method": "additive", "strength": 0.9, "first_value_only": true}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5401, in inverse_transform
df = self._inverse_one(df, i, trans_method=trans_method, bounds=bounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5356, in _inverse_one
df = self.transformers[i].inverse_transform(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2794, in inverse_transform
df.iloc[0:1] + adjustment,
~~~~~~~~~~~~~^~~~~~~~~~~~
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
return method(self, other)
^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 186, in __add__
return self._arith_method(other, operator.add)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7913, in _arith_method
new_data = self._dispatch_frame_op(other, op, axis=axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7945, in _dispatch_frame_op
bm = self._mgr.apply(array_op, right=right)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 361, in apply
applied = b.apply(f, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 393, in apply
result = func(self.values, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 283, in arithmetic_op
res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 218, in _na_arithmetic_op
result = func(left, right)
^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 242, in evaluate
return _evaluate(op, op_str, a, b) # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 73, in _evaluate_standard
return op(a, b)
^^^^^^^^
TypeError: unsupported operand type(s) for +: 'float' and 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 884, in predict
self.transformer_object.inverse_transform(df_forecast.forecast)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5406, in inverse_transform
raise Exception(err_str) from e
Exception: Transformer AlignLastValue failed on inverse from params rolling_mean_24 {'0': {'method': 'clip', 'std_threshold': 3, 'fillna': None}, '1': {'model': 'GLS', 'phi': 1, 'window': None, 'transform_dict': None}, '2': {'method': 'clip', 'std_threshold': 2, 'fillna': None}, '3': {'rows': 1, 'lag': 28, 'method': 'additive', 'strength': 0.9, 'first_value_only': True}}
in model 39 in generation 0: ConstantNaive
Model Number: 40 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AnomalyRemoval", "1": "ScipyFilter"}, "transformation_params": {"0": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "linear", "transform_dict": null, "isolated_only": true}, "1": {"method": "butter", "method_args": {"N": 5, "window_size": 59, "btype": "lowpass", "analog": false, "output": "sos"}}}}
Model Number: 41 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean", "window": 24} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 42 with model ConstantNaive in generation 0 of 1 with params {"constant": -1} and transformations {"fillna": "nearest", "transformations": {"0": "SeasonalDifference", "1": "SeasonalDifference", "2": "RobustScaler", "3": "CenterSplit"}, "transformation_params": {"0": {"lag_1": 12, "method": "LastValue"}, "1": {"lag_1": 7, "method": "LastValue"}, "2": {}, "3": {"fillna": "akima", "center": "zero"}}}
Model Number: 43 with model SectionalMotif in generation 0 of 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "mahalanobis", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}}}
Model Number: 44 with model SeasonalNaive in generation 0 of 1 with params {"method": "mean", "lag_1": 2, "lag_2": 60} and transformations {"fillna": "ffill", "transformations": {"0": "bkfilter", "1": "AlignLastValue", "2": "Log", "3": "StandardScaler", "4": "AlignLastValue", "5": "EWMAFilter"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {}, "4": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "5": {"span": 7}}}
Model Number: 45 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "rolling_mean", "transformations": {"0": "bkfilter", "1": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 954, in predict
raise ValueError(
ValueError: Model returned NaN due to a preprocessing transformer {'fillna': 'rolling_mean', 'transformations': {'0': 'bkfilter', '1': 'AlignLastValue'}, 'transformation_params': {'0': {}, '1': {'rows': 1, 'lag': 1, 'method': 'multiplicative', 'strength': 1.0, 'first_value_only': False}}}. fail_on_forecast_nan=True
in model 45 in generation 0: ConstantNaive
Model Number: 46 with model SectionalMotif in generation 0 of 1 with params {"window": 7, "point_method": "weighted_mean", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 2, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "FFTFilter", "1": "RobustScaler", "2": "RegressionFilter"}, "transformation_params": {"0": {"cutoff": 0.1, "reverse": false}, "1": {}, "2": {"sigma": 3, "rolling_window": 90, "run_order": "trend_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}}}
Model Number: 47 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "MaxAbsScaler", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": {"fillna": null, "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "uniform", "alpha": 0.05}}}}}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 48 with model SeasonalNaive in generation 0 of 1 with params {"method": "mean", "lag_1": 364, "lag_2": null} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer", "1": "RobustScaler", "2": "bkfilter", "3": "HPFilter", "4": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {}, "2": {}, "3": {"part": "trend", "lamb": 1600}, "4": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 49 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "BKBandpassFilter", "1": "StandardScaler", "2": "ScipyFilter", "3": "RobustScaler", "4": "RegressionFilter"}, "transformation_params": {"0": {"low": 8, "high": 364, "K": 1, "lanczos_factor": false, "return_diff": false}, "1": {}, "2": {"method": "savgol_filter", "method_args": {"window_length": 31, "polyorder": 2, "deriv": 0, "mode": "nearest"}}, "3": {}, "4": {"sigma": 1, "rolling_window": 90, "run_order": "season_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "simple", "polynomial_degree": null, "transform_dict": {"fillna": null, "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 2}}}, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5292, in _fit
df = self._fit_one(df, i)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5273, in _fit_one
df = pd.DataFrame(df, index=self.df_index, columns=self.df_colnames)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 827, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 336, in ndarray_to_mgr
_check_values_indices_shape_match(values, index, columns)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 420, in _check_values_indices_shape_match
raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
ValueError: Shape of passed values is (1157, 10), indices imply (1159, 10)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1446, in model_forecast
model = model.fit(df_train_low, future_regressor_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 847, in fit
df_train_transformed = self.transformer_object._fit(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5297, in _fit
raise Exception(err_str) from e
Exception: Transformer StandardScaler failed on fit from params fake_date {'0': {'low': 8, 'high': 364, 'K': 1, 'lanczos_factor': False, 'return_diff': False}, '1': {}, '2': {'method': 'savgol_filter', 'method_args': {'window_length': 31, 'polyorder': 2, 'deriv': 0, 'mode': 'nearest'}}, '3': {}, '4': {'sigma': 1, 'rolling_window': 90, 'run_order': 'season_first', 'regression_params': {'regression_model': {'model': 'ElasticNet', 'model_params': {}}, 'datepart_method': 'simple', 'polynomial_degree': None, 'transform_dict': {'fillna': None, 'transformations': {'0': 'EWMAFilter'}, 'transformation_params': {'0': {'span': 2}}}, 'holiday_countries_used': False}, 'holiday_params': None, 'trend_method': 'rolling_mean'}}
in model 49 in generation 0: GLS
Model Number: 50 with model SeasonalNaive in generation 0 of 1 with params {"method": "median", "lag_1": 58, "lag_2": 24} and transformations {"fillna": "rolling_mean", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 51 with model SeasonalityMotif in generation 0 of 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "chebyshev", "k": 3, "datepart_method": "simple_2", "independent": false} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "CenterSplit", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"fillna": "mean", "center": "zero"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 52 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "zero", "transformations": {"0": "ClipOutliers", "1": "FFTDecomposition", "2": "Round", "3": "SeasonalDifference", "4": "AlignLastValue", "5": "MinMaxScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"n_harmonics": null, "detrend": "linear"}, "2": {"decimals": 1, "on_transform": true, "on_inverse": true}, "3": {"lag_1": 12, "method": "Mean"}, "4": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": true}, "5": {}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5401, in inverse_transform
df = self._inverse_one(df, i, trans_method=trans_method, bounds=bounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5356, in _inverse_one
df = self.transformers[i].inverse_transform(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2794, in inverse_transform
df.iloc[0:1] + adjustment,
~~~~~~~~~~~~~^~~~~~~~~~~~
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
return method(self, other)
^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 186, in __add__
return self._arith_method(other, operator.add)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7913, in _arith_method
new_data = self._dispatch_frame_op(other, op, axis=axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7945, in _dispatch_frame_op
bm = self._mgr.apply(array_op, right=right)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 361, in apply
applied = b.apply(f, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 393, in apply
result = func(self.values, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 283, in arithmetic_op
res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 218, in _na_arithmetic_op
result = func(left, right)
^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 242, in evaluate
return _evaluate(op, op_str, a, b) # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 73, in _evaluate_standard
return op(a, b)
^^^^^^^^
TypeError: unsupported operand type(s) for +: 'float' and 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 884, in predict
self.transformer_object.inverse_transform(df_forecast.forecast)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5406, in inverse_transform
raise Exception(err_str) from e
Exception: Transformer AlignLastValue failed on inverse from params zero {'0': {'method': 'clip', 'std_threshold': 4, 'fillna': None}, '1': {'n_harmonics': None, 'detrend': 'linear'}, '2': {'decimals': 1, 'on_transform': True, 'on_inverse': True}, '3': {'lag_1': 12, 'method': 'Mean'}, '4': {'rows': 1, 'lag': 7, 'method': 'additive', 'strength': 1.0, 'first_value_only': True}, '5': {}}
in model 52 in generation 0: LastValueNaive
Model Number: 53 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
New Generation: 1 of 1
Model Number: 54 with model ConstantNaive in generation 1 of 1 with params {"constant": 0.1} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 55 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 7, "lag_2": 28} and transformations {"fillna": "SeasonalityMotifImputerLinMix", "transformations": {"0": "CenterSplit"}, "transformation_params": {"0": {"fillna": "mean", "center": "zero"}}}
Model Number: 56 with model SectionalMotif in generation 1 of 1 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "euclidean", "include_differenced": true, "k": 1, "stride_size": 10, "regression_type": null} and transformations {"fillna": "KNNImputer", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 57 with model GLS in generation 1 of 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "LevelShiftTransformer", "1": "Round", "2": "SeasonalDifference", "3": "AlignLastValue", "4": "SinTrend", "5": "AlignLastValue"}, "transformation_params": {"0": {"window_size": 90, "alpha": 3.5, "grouping_forward_limit": 4, "max_level_shifts": 10, "alignment": "last_value"}, "1": {"decimals": 0, "on_transform": false, "on_inverse": true}, "2": {"lag_1": 12, "method": 20}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {}, "5": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 58 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 35, "lag_2": 7} and transformations {"fillna": "SeasonalityMotifImputerLinMix", "transformations": {"0": "Detrend", "1": "AlignLastValue"}, "transformation_params": {"0": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 59 with model SeasonalityMotif in generation 1 of 1 with params {"window": 15, "point_method": "mean", "distance_metric": "chebyshev", "k": 3, "datepart_method": "simple_2", "independent": false} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "CumSumTransformer", "3": "AlignLastValue", "4": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {}}}
Model Number: 60 with model SeasonalityMotif in generation 1 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "k": 10, "datepart_method": "simple_2"} and transformations {"fillna": "quadratic", "transformations": {"0": "AnomalyRemoval", "1": "StandardScaler", "2": "CumSumTransformer", "3": "AlignLastValue", "4": "AnomalyRemoval", "5": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "minmax", "method_params": {"alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "1": {}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {"method": "mad", "method_params": {"distribution": "chi2", "alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "5": {}}}
Model Number: 61 with model AverageValueNaive in generation 1 of 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "DifferencedTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 62 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 35, "lag_2": 7} and transformations {"fillna": "zero", "transformations": {"0": "bkfilter", "1": "AlignLastValue", "2": "LevelShiftTransformer", "3": "Detrend"}, "transformation_params": {"0": {}, "1": {"rows": 7, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"window_size": 7, "alpha": 3.5, "grouping_forward_limit": 3, "max_level_shifts": 5, "alignment": "rolling_diff"}, "3": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}}}
Model Number: 63 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "RegressionFilter", "1": "CenterSplit"}, "transformation_params": {"0": {"sigma": 1, "rolling_window": 90, "run_order": "trend_first", "regression_params": {"regression_model": {"model": "DecisionTree", "model_params": {"max_depth": 3, "min_samples_split": 2}}, "datepart_method": "simple_binarized", "polynomial_degree": null, "transform_dict": {"fillna": null, "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 2}}}, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}, "1": {"fillna": "mean", "center": "zero"}}}
Model Number: 64 with model SeasonalityMotif in generation 1 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "mqae", "k": 1, "datepart_method": "simple", "independent": true} and transformations {"fillna": "ffill", "transformations": {"0": "RollingMean100thN", "1": "AlignLastValue", "2": "PositiveShift", "3": "AlignLastValue", "4": "DifferencedTransformer"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": false}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {}}}
Model Number: 65 with model AverageValueNaive in generation 1 of 1 with params {"method": "Mean", "window": 24} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "HistoricValues", "2": "StandardScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"window": 730}, "2": {}}}
Model Number: 66 with model SectionalMotif in generation 1 of 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 67 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": null} and transformations {"fillna": "ffill", "transformations": {"0": "Detrend", "1": "CenterSplit", "2": "AlignLastValue"}, "transformation_params": {"0": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "1": {"fillna": "mean", "center": "zero"}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 68 with model SeasonalityMotif in generation 1 of 1 with params {"window": 15, "point_method": "mean", "distance_metric": "minkowski", "k": 3, "datepart_method": "recurring", "independent": true} and transformations {"fillna": "ffill", "transformations": {"0": "AlignLastValue", "1": "CenterSplit"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "1": {"fillna": "mean", "center": "zero"}}}
Model Number: 69 with model GLS in generation 1 of 1 with params {} and transformations {"fillna": "pchip", "transformations": {"0": "LevelShiftTransformer", "1": "Detrend", "2": "SeasonalDifference", "3": "AlignLastValue", "4": "SinTrend", "5": "AlignLastValue"}, "transformation_params": {"0": {"window_size": 90, "alpha": 3.5, "grouping_forward_limit": 4, "max_level_shifts": 10, "alignment": "last_value"}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": {"fillna": null, "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "uniform", "alpha": 0.05}}}}}, "2": {"lag_1": 12, "method": 20}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {}, "5": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 70 with model ConstantNaive in generation 1 of 1 with params {"constant": 1} and transformations {"fillna": "SeasonalityMotifImputerLinMix", "transformations": {"0": "RobustScaler", "1": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"rows": 2, "lag": 28, "method": "additive", "strength": 0.7, "first_value_only": false}}}
Model Number: 71 with model SeasonalityMotif in generation 1 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "mqae", "k": 1, "datepart_method": "simple", "independent": false} and transformations {"fillna": "ffill", "transformations": {"0": "ReplaceConstant", "1": "DatepartRegression", "2": "CenterSplit"}, "transformation_params": {"0": {"constant": 0, "reintroduction_model": {"model": "SGD", "model_params": {}, "datepart_method": "simple_2"}, "fillna": "linear"}, "1": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": {"fillna": null, "transformations": {"0": "ScipyFilter"}, "transformation_params": {"0": {"method": "savgol_filter", "method_args": {"window_length": 31, "polyorder": 3, "deriv": 0, "mode": "interp"}}}}, "holiday_countries_used": true}, "2": {"fillna": "mean", "center": "zero"}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5292, in _fit
df = self._fit_one(df, i)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5270, in _fit_one
df = self.transformers[i].fit_transform(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4289, in fit_transform
return self._fit(df)
^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4236, in _fit
self.model.fit(X, y)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 535, in fit
super().fit(X, Y, sample_weight=sample_weight, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 270, in fit
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 67, in __call__
return super().__call__(iterable_with_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1863, in __call__
return output if self.return_generator else list(output)
^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
res = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 129, in __call__
return self.function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 61, in _fit_estimator
estimator.fit(X, y, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 917, in fit
return self._fit(
^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 704, in _fit
self._partial_fit(
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 658, in _partial_fit
raise ValueError(
ValueError: The number of classes has to be greater than one; got 1 class
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1446, in model_forecast
model = model.fit(df_train_low, future_regressor_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 847, in fit
df_train_transformed = self.transformer_object._fit(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5297, in _fit
raise Exception(err_str) from e
Exception: Transformer ReplaceConstant failed on fit from params ffill {'0': {'constant': 0, 'reintroduction_model': {'model': 'SGD', 'model_params': {}, 'datepart_method': 'simple_2'}, 'fillna': 'linear'}, '1': {'regression_model': {'model': 'ElasticNet', 'model_params': {}}, 'datepart_method': 'expanded', 'polynomial_degree': None, 'transform_dict': {'fillna': None, 'transformations': {'0': 'ScipyFilter'}, 'transformation_params': {'0': {'method': 'savgol_filter', 'method_args': {'window_length': 31, 'polyorder': 3, 'deriv': 0, 'mode': 'interp'}}}}, 'holiday_countries_used': True}, '2': {'fillna': 'mean', 'center': 'zero'}}
in model 71 in generation 1: SeasonalityMotif
Model Number: 72 with model GLS in generation 1 of 1 with params {} and transformations {"fillna": "akima", "transformations": {"0": "RegressionFilter", "1": "CumSumTransformer", "2": "bkfilter"}, "transformation_params": {"0": {"sigma": 1, "rolling_window": 90, "run_order": "season_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "simple_binarized", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": true}, "holiday_params": null, "trend_method": "rolling_mean"}, "1": {}, "2": {}}}
Model Number: 73 with model SeasonalNaive in generation 1 of 1 with params {"method": "median", "lag_1": 364, "lag_2": 12} and transformations {"fillna": "fake_date", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue", "2": "DifferencedTransformer", "3": "IntermittentOccurrence"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": true}, "2": {}, "3": {"center": "mean"}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5401, in inverse_transform
df = self._inverse_one(df, i, trans_method=trans_method, bounds=bounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5356, in _inverse_one
df = self.transformers[i].inverse_transform(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2794, in inverse_transform
df.iloc[0:1] + adjustment,
~~~~~~~~~~~~~^~~~~~~~~~~~
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
return method(self, other)
^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 186, in __add__
return self._arith_method(other, operator.add)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7913, in _arith_method
new_data = self._dispatch_frame_op(other, op, axis=axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7945, in _dispatch_frame_op
bm = self._mgr.apply(array_op, right=right)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 361, in apply
applied = b.apply(f, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 393, in apply
result = func(self.values, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 283, in arithmetic_op
res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 218, in _na_arithmetic_op
result = func(left, right)
^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 242, in evaluate
return _evaluate(op, op_str, a, b) # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 73, in _evaluate_standard
return op(a, b)
^^^^^^^^
TypeError: unsupported operand type(s) for +: 'float' and 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 884, in predict
self.transformer_object.inverse_transform(df_forecast.forecast)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5406, in inverse_transform
raise Exception(err_str) from e
Exception: Transformer AlignLastValue failed on inverse from params fake_date {'0': {'method': 'clip', 'std_threshold': 3.5, 'fillna': None}, '1': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': True}, '2': {}, '3': {'center': 'mean'}}
in model 73 in generation 1: SeasonalNaive
Model Number: 74 with model ConstantNaive in generation 1 of 1 with params {"constant": 1} and transformations {"fillna": "ffill", "transformations": {"0": "bkfilter", "1": "Detrend", "2": "CenterSplit", "3": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"fillna": "mean", "center": "zero"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 75 with model ConstantNaive in generation 1 of 1 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "SeasonalDifference", "2": "RobustScaler", "3": "CenterSplit"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"lag_1": 7, "method": "LastValue"}, "2": {}, "3": {"fillna": "akima", "center": "zero"}}}
Model Number: 76 with model SectionalMotif in generation 1 of 1 with params {"window": 7, "point_method": "weighted_mean", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "ffill_mean_biased", "transformations": {"0": "RobustScaler"}, "transformation_params": {"0": {}}}
Model Number: 77 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "AlignLastValue", "1": "bkfilter", "2": "CenterSplit", "3": "AlignLastValue"}, "transformation_params": {"0": {"rows": 7, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {}, "2": {"fillna": "mean", "center": "zero"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 78 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "DiffSmoother", "2": "StandardScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"method": null, "method_params": null, "transform_dict": null, "reverse_alignment": true, "isolated_only": false, "fillna": 3.0}, "2": {}}}
Model Number: 79 with model SectionalMotif in generation 1 of 1 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "mahalanobis", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "pchip", "transformations": {"0": "ClipOutliers", "1": "FFTFilter", "2": "SeasonalDifference", "3": "EWMAFilter"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"cutoff": 0.005, "reverse": false}, "2": {"lag_1": 12, "method": 20}, "3": {"span": 10}}}
Model Number: 80 with model ConstantNaive in generation 1 of 1 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "HPFilter", "2": "StandardScaler", "3": "RollingMean100thN"}, "transformation_params": {"0": {"lag_1": 12, "method": "LastValue"}, "1": {"part": "trend", "lamb": 104976000000}, "2": {}, "3": {}}}
Model Number: 81 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "time", "transformations": {"0": "SinTrend", "1": "AlignLastValue", "2": "Detrend", "3": "LevelShiftTransformer", "4": "StandardScaler"}, "transformation_params": {"0": {}, "1": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"model": "Linear"}, "3": {"window_size": 364, "alpha": 3.5, "grouping_forward_limit": 2, "max_level_shifts": 10, "alignment": "average"}, "4": {}}}
Model Number: 82 with model SeasonalNaive in generation 1 of 1 with params {"method": "median", "lag_1": 35, "lag_2": 2} and transformations {"fillna": "KNNImputer", "transformations": {"0": "RobustScaler", "1": "AlignLastValue", "2": "FFTFilter", "3": "Detrend", "4": "bkfilter"}, "transformation_params": {"0": {}, "1": {"rows": 7, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"cutoff": 0.4, "reverse": false}, "3": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "4": {}}}
Model Number: 83 with model SeasonalNaive in generation 1 of 1 with params {"method": "median", "lag_1": 2, "lag_2": 48} and transformations {"fillna": "rolling_mean", "transformations": {"0": "AlignLastValue", "1": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"rows": 7, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 84 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "bkfilter", "1": "Detrend", "2": "STLFilter", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"decomp_type": "STL", "part": "trend", "seasonal": 7}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": true}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5401, in inverse_transform
df = self._inverse_one(df, i, trans_method=trans_method, bounds=bounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5356, in _inverse_one
df = self.transformers[i].inverse_transform(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2794, in inverse_transform
df.iloc[0:1] + adjustment,
~~~~~~~~~~~~~^~~~~~~~~~~~
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
return method(self, other)
^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 186, in __add__
return self._arith_method(other, operator.add)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7913, in _arith_method
new_data = self._dispatch_frame_op(other, op, axis=axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7945, in _dispatch_frame_op
bm = self._mgr.apply(array_op, right=right)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 361, in apply
applied = b.apply(f, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 393, in apply
result = func(self.values, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 283, in arithmetic_op
res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 218, in _na_arithmetic_op
result = func(left, right)
^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 242, in evaluate
return _evaluate(op, op_str, a, b) # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 73, in _evaluate_standard
return op(a, b)
^^^^^^^^
TypeError: unsupported operand type(s) for +: 'float' and 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 884, in predict
self.transformer_object.inverse_transform(df_forecast.forecast)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5406, in inverse_transform
raise Exception(err_str) from e
Exception: Transformer AlignLastValue failed on inverse from params ffill {'0': {}, '1': {'model': 'GLS', 'phi': 1, 'window': None, 'transform_dict': None}, '2': {'decomp_type': 'STL', 'part': 'trend', 'seasonal': 7}, '3': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': False}, '4': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 0.5, 'first_value_only': True}}
in model 84 in generation 1: LastValueNaive
Model Number: 85 with model SeasonalNaive in generation 1 of 1 with params {"method": "median", "lag_1": 2, "lag_2": 1} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {}}}
Model Number: 86 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AnomalyRemoval", "1": "LevelShiftTransformer"}, "transformation_params": {"0": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "linear", "transform_dict": null, "isolated_only": true}, "1": {"window_size": 90, "alpha": 2.5, "grouping_forward_limit": 4, "max_level_shifts": 30, "alignment": "average"}}}
Model Number: 87 with model SectionalMotif in generation 1 of 1 with params {"window": 15, "point_method": "weighted_mean", "distance_metric": "sqeuclidean", "include_differenced": true, "k": 5, "stride_size": 2, "regression_type": null} and transformations {"fillna": "KNNImputer", "transformations": {"0": "SeasonalDifference", "1": "AlignLastValue"}, "transformation_params": {"0": {"lag_1": 7, "method": "Median"}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 88 with model SeasonalityMotif in generation 1 of 1 with params {"window": 50, "point_method": "mean", "distance_metric": "canberra", "k": 10, "datepart_method": "recurring", "independent": false} and transformations {"fillna": "median", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 0.7, "first_value_only": false}}}
TotalRuntime missing in 2!
Validation Round: 1
Validation train index is DatetimeIndex(['2020-02-05', '2020-02-06', '2020-02-07', '2020-02-08',
'2020-02-09', '2020-02-10', '2020-02-11', '2020-02-12',
'2020-02-13', '2020-02-14',
...
'2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03',
'2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07',
'2023-03-08', '2023-03-09'],
dtype='datetime64[ns]', length=1129, freq=None)
Model Number: 1 of 17 with model AverageValueNaive for Validation 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "DifferencedTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
📈 1 - AverageValueNaive with avg smape 100.0:
Model Number: 2 of 17 with model SeasonalityMotif for Validation 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "chebyshev", "k": 3, "datepart_method": "simple_2"} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "CenterSplit", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"fillna": "mean", "center": "zero"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
2 - SeasonalityMotif with avg smape 100.15:
Model Number: 3 of 17 with model SectionalMotif for Validation 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
3 - SectionalMotif with avg smape 100.15:
Model Number: 4 of 17 with model GLS for Validation 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "RollingMeanTransformer", "1": "DifferencedTransformer", "2": "Detrend", "3": "Slice"}, "transformation_params": {"0": {"fixed": true, "window": 3}, "1": {}, "2": {"model": "Linear"}, "3": {"method": 100}}}
4 - GLS with avg smape 104.14:
Model Number: 5 of 17 with model SectionalMotif for Validation 1 with params {"window": 15, "point_method": "weighted_mean", "distance_metric": "sqeuclidean", "include_differenced": true, "k": 5, "stride_size": 2, "regression_type": null} and transformations {"fillna": "KNNImputer", "transformations": {"0": "SeasonalDifference", "1": "AlignLastValue"}, "transformation_params": {"0": {"lag_1": 7, "method": "Median"}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
5 - SectionalMotif with avg smape 101.08:
Model Number: 6 of 17 with model AverageValueNaive for Validation 1 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer", "1": "PositiveShift", "2": "SeasonalDifference", "3": "Slice", "4": "AnomalyRemoval", "5": "AnomalyRemoval"}, "transformation_params": {"0": {}, "1": {}, "2": {"lag_1": 12, "method": 20}, "3": {"method": 0.5}, "4": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": {"fillna": "ffill", "transformations": {"0": "MinMaxScaler", "1": "RobustScaler"}, "transformation_params": {"0": {}, "1": {}}}, "isolated_only": false}, "5": {"method": "IQR", "method_params": {"iqr_threshold": 2.0, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}}}
6 - AverageValueNaive with avg smape 102.19:
Model Number: 7 of 17 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
7 - LastValueNaive with avg smape 102.16:
Model Number: 8 of 17 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AnomalyRemoval", "1": "LevelShiftTransformer"}, "transformation_params": {"0": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "linear", "transform_dict": null, "isolated_only": true}, "1": {"window_size": 90, "alpha": 2.5, "grouping_forward_limit": 4, "max_level_shifts": 30, "alignment": "average"}}}
8 - LastValueNaive with avg smape 102.16:
Model Number: 9 of 17 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "RegressionFilter", "1": "CenterSplit"}, "transformation_params": {"0": {"sigma": 1, "rolling_window": 90, "run_order": "trend_first", "regression_params": {"regression_model": {"model": "DecisionTree", "model_params": {"max_depth": 3, "min_samples_split": 2}}, "datepart_method": "simple_binarized", "polynomial_degree": null, "transform_dict": {"fillna": null, "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 2}}}, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}, "1": {"fillna": "mean", "center": "zero"}}}
9 - LastValueNaive with avg smape 102.16:
Model Number: 10 of 17 with model AverageValueNaive for Validation 1 with params {"method": "Mean", "window": 24} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
10 - AverageValueNaive with avg smape 102.16:
Model Number: 11 of 17 with model ConstantNaive for Validation 1 with params {"constant": 0} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
11 - ConstantNaive with avg smape 102.16:
Model Number: 12 of 17 with model ConstantNaive for Validation 1 with params {"constant": 0.1} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
12 - ConstantNaive with avg smape 102.16:
Model Number: 13 of 17 with model ConstantNaive for Validation 1 with params {"constant": 1} and transformations {"fillna": "ffill", "transformations": {"0": "bkfilter", "1": "Detrend", "2": "CenterSplit", "3": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"fillna": "mean", "center": "zero"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
13 - ConstantNaive with avg smape 102.31:
Model Number: 14 of 17 with model SectionalMotif for Validation 1 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": "AlignLastValue", "1": "CenterSplit", "2": "MinMaxScaler", "3": "bkfilter", "4": "StandardScaler", "5": "DifferencedTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"fillna": "linear", "center": "zero"}, "2": {}, "3": {}, "4": {}, "5": {}}}
14 - SectionalMotif with avg smape 103.79:
Model Number: 15 of 17 with model SectionalMotif for Validation 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": false, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
15 - SectionalMotif with avg smape 104.92:
Model Number: 16 of 17 with model SectionalMotif for Validation 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "mahalanobis", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}}}
16 - SectionalMotif with avg smape 104.91:
Model Number: 17 of 17 with model SeasonalNaive for Validation 1 with params {"method": "mean", "lag_1": 364, "lag_2": null} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer", "1": "RobustScaler", "2": "bkfilter", "3": "HPFilter", "4": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {}, "2": {}, "3": {"part": "trend", "lamb": 1600}, "4": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
17 - SeasonalNaive with avg smape 103.17:
Validation Round: 2
Validation train index is DatetimeIndex(['2020-02-05', '2020-02-06', '2020-02-07', '2020-02-08',
'2020-02-09', '2020-02-10', '2020-02-11', '2020-02-12',
'2020-02-13', '2020-02-14',
...
'2023-01-29', '2023-01-30', '2023-01-31', '2023-02-01',
'2023-02-02', '2023-02-03', '2023-02-04', '2023-02-05',
'2023-02-06', '2023-02-07'],
dtype='datetime64[ns]', length=1099, freq=None)
Model Number: 1 of 17 with model AverageValueNaive for Validation 2 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "DifferencedTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
📈 1 - AverageValueNaive with avg smape 100.01:
Model Number: 2 of 17 with model SeasonalityMotif for Validation 2 with params {"window": 10, "point_method": "midhinge", "distance_metric": "chebyshev", "k": 3, "datepart_method": "simple_2"} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "CenterSplit", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"fillna": "mean", "center": "zero"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
2 - SeasonalityMotif with avg smape 100.18:
Model Number: 3 of 17 with model SectionalMotif for Validation 2 with params {"window": 10, "point_method": "midhinge", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
3 - SectionalMotif with avg smape 100.19:
Model Number: 4 of 17 with model GLS for Validation 2 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "RollingMeanTransformer", "1": "DifferencedTransformer", "2": "Detrend", "3": "Slice"}, "transformation_params": {"0": {"fixed": true, "window": 3}, "1": {}, "2": {"model": "Linear"}, "3": {"method": 100}}}
4 - GLS with avg smape 104.37:
Model Number: 5 of 17 with model SectionalMotif for Validation 2 with params {"window": 15, "point_method": "weighted_mean", "distance_metric": "sqeuclidean", "include_differenced": true, "k": 5, "stride_size": 2, "regression_type": null} and transformations {"fillna": "KNNImputer", "transformations": {"0": "SeasonalDifference", "1": "AlignLastValue"}, "transformation_params": {"0": {"lag_1": 7, "method": "Median"}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
5 - SectionalMotif with avg smape 102.0:
Model Number: 6 of 17 with model AverageValueNaive for Validation 2 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer", "1": "PositiveShift", "2": "SeasonalDifference", "3": "Slice", "4": "AnomalyRemoval", "5": "AnomalyRemoval"}, "transformation_params": {"0": {}, "1": {}, "2": {"lag_1": 12, "method": 20}, "3": {"method": 0.5}, "4": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": {"fillna": "ffill", "transformations": {"0": "MinMaxScaler", "1": "RobustScaler"}, "transformation_params": {"0": {}, "1": {}}}, "isolated_only": false}, "5": {"method": "IQR", "method_params": {"iqr_threshold": 2.0, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}}}
6 - AverageValueNaive with avg smape 102.19:
Model Number: 7 of 17 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
7 - LastValueNaive with avg smape 102.28:
Model Number: 8 of 17 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AnomalyRemoval", "1": "LevelShiftTransformer"}, "transformation_params": {"0": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "linear", "transform_dict": null, "isolated_only": true}, "1": {"window_size": 90, "alpha": 2.5, "grouping_forward_limit": 4, "max_level_shifts": 30, "alignment": "average"}}}
8 - LastValueNaive with avg smape 102.28:
Model Number: 9 of 17 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "RegressionFilter", "1": "CenterSplit"}, "transformation_params": {"0": {"sigma": 1, "rolling_window": 90, "run_order": "trend_first", "regression_params": {"regression_model": {"model": "DecisionTree", "model_params": {"max_depth": 3, "min_samples_split": 2}}, "datepart_method": "simple_binarized", "polynomial_degree": null, "transform_dict": {"fillna": null, "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 2}}}, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}, "1": {"fillna": "mean", "center": "zero"}}}
9 - LastValueNaive with avg smape 102.28:
Model Number: 10 of 17 with model AverageValueNaive for Validation 2 with params {"method": "Mean", "window": 24} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
10 - AverageValueNaive with avg smape 102.28:
Model Number: 11 of 17 with model ConstantNaive for Validation 2 with params {"constant": 0} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
11 - ConstantNaive with avg smape 102.28:
Model Number: 12 of 17 with model ConstantNaive for Validation 2 with params {"constant": 0.1} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
12 - ConstantNaive with avg smape 102.28:
Model Number: 13 of 17 with model ConstantNaive for Validation 2 with params {"constant": 1} and transformations {"fillna": "ffill", "transformations": {"0": "bkfilter", "1": "Detrend", "2": "CenterSplit", "3": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"fillna": "mean", "center": "zero"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
13 - ConstantNaive with avg smape 102.43:
Model Number: 14 of 17 with model SectionalMotif for Validation 2 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": "AlignLastValue", "1": "CenterSplit", "2": "MinMaxScaler", "3": "bkfilter", "4": "StandardScaler", "5": "DifferencedTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"fillna": "linear", "center": "zero"}, "2": {}, "3": {}, "4": {}, "5": {}}}
14 - SectionalMotif with avg smape 104.7:
Model Number: 15 of 17 with model SectionalMotif for Validation 2 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": false, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
15 - SectionalMotif with avg smape 105.18:
Model Number: 16 of 17 with model SectionalMotif for Validation 2 with params {"window": 10, "point_method": "midhinge", "distance_metric": "mahalanobis", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}}}
16 - SectionalMotif with avg smape 105.19:
Model Number: 17 of 17 with model SeasonalNaive for Validation 2 with params {"method": "mean", "lag_1": 364, "lag_2": null} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer", "1": "RobustScaler", "2": "bkfilter", "3": "HPFilter", "4": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {}, "2": {}, "3": {"part": "trend", "lamb": 1600}, "4": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
17 - SeasonalNaive with avg smape 101.82:
TotalRuntime missing in 3!
Validation Round: 1
Validation train index is DatetimeIndex(['2020-02-05', '2020-02-06', '2020-02-07', '2020-02-08',
'2020-02-09', '2020-02-10', '2020-02-11', '2020-02-12',
'2020-02-13', '2020-02-14',
...
'2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03',
'2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07',
'2023-03-08', '2023-03-09'],
dtype='datetime64[ns]', length=1129, freq=None)
TotalRuntime missing in 0!
Validation Round: 2
Validation train index is DatetimeIndex(['2020-02-05', '2020-02-06', '2020-02-07', '2020-02-08',
'2020-02-09', '2020-02-10', '2020-02-11', '2020-02-12',
'2020-02-13', '2020-02-14',
...
'2023-01-29', '2023-01-30', '2023-01-31', '2023-02-01',
'2023-02-02', '2023-02-03', '2023-02-04', '2023-02-05',
'2023-02-06', '2023-02-07'],
dtype='datetime64[ns]', length=1099, freq=None)
TotalRuntime missing in 0!
Model Number: 1 with model Ensemble in generation 0 of Horizontal Ensembles with params {"model_name": "Horizontal", "model_count": 3, "model_metric": "Score", "models": {"18ddd05077c55f7aa0b592ffbd520c76": {"Model": "SectionalMotif", "ModelParameters": "{\"window\": 10, \"point_method\": \"midhinge\", \"distance_metric\": \"mahalanobis\", \"include_differenced\": true, \"k\": 10, \"stride_size\": 1, \"regression_type\": null}", "TransformationParameters": "{\"fillna\": \"rolling_mean\", \"transformations\": {\"0\": \"ClipOutliers\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 4, \"fillna\": null}}}"}, "44d4635234302947c25391ec3d85c980": {"Model": "SeasonalityMotif", "ModelParameters": "{\"window\": 10, \"point_method\": \"midhinge\", \"distance_metric\": \"chebyshev\", \"k\": 3, \"datepart_method\": \"simple_2\"}", "TransformationParameters": "{\"fillna\": \"ffill\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"Detrend\", \"2\": \"CenterSplit\", \"3\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 3.5, \"fillna\": null}, \"1\": {\"model\": \"GLS\", \"phi\": 1, \"window\": null, \"transform_dict\": null}, \"2\": {\"fillna\": \"mean\", \"center\": \"zero\"}, \"3\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false}}}"}, "7559d0e789b43678f6a083f9dd0e8dec": {"Model": "AverageValueNaive", "ModelParameters": "{\"method\": \"Mean\", \"window\": null}", "TransformationParameters": "{\"fillna\": \"mean\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"Detrend\", \"2\": \"DifferencedTransformer\", \"3\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 3.5, \"fillna\": null}, \"1\": {\"model\": \"GLS\", \"phi\": 1, \"window\": null, \"transform_dict\": null}, \"2\": {}, \"3\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false}}}"}}, "series": {"PC1": "7559d0e789b43678f6a083f9dd0e8dec", "PC2": "7559d0e789b43678f6a083f9dd0e8dec", "PC3": "18ddd05077c55f7aa0b592ffbd520c76", "PC4": "18ddd05077c55f7aa0b592ffbd520c76", "PC5": "7559d0e789b43678f6a083f9dd0e8dec", "PC6": "18ddd05077c55f7aa0b592ffbd520c76", "PC7": "7559d0e789b43678f6a083f9dd0e8dec", "PC8": "7559d0e789b43678f6a083f9dd0e8dec", "PC9": "44d4635234302947c25391ec3d85c980", "PC10": "18ddd05077c55f7aa0b592ffbd520c76"}} and transformations {}
Ensemble Horizontal component 1 of 3 SectionalMotif started
Ensemble Horizontal component 2 of 3 SeasonalityMotif started
Ensemble Horizontal component 3 of 3 AverageValueNaive started
Ensemble Horizontal component 1 of 3 SectionalMotif started
Ensemble Horizontal component 2 of 3 SeasonalityMotif started
Ensemble Horizontal component 3 of 3 AverageValueNaive started
[12]:
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | PC10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Date | ||||||||||
| 2023-06-03 | 5.966747 | 2.361111 | 0.0 | 0.0 | 0.972152 | 0.0 | 0.326543 | 0.688027 | 0.089966 | 0.0 |
| 2023-06-04 | 5.976522 | 2.365021 | 0.0 | 0.0 | 0.974928 | 0.0 | 0.325262 | 0.689956 | 0.091697 | 0.0 |
| 2023-06-05 | 5.986297 | 2.368930 | 0.0 | 0.0 | 0.977704 | 0.0 | 0.323982 | 0.691886 | 0.093429 | 0.0 |
| 2023-06-06 | 5.996072 | 2.372840 | 0.0 | 0.0 | 0.980480 | 0.0 | 0.322701 | 0.693815 | 0.095160 | 0.0 |
| 2023-06-07 | 6.005848 | 2.376750 | 0.0 | 0.0 | 0.983256 | 0.0 | 0.321420 | 0.695745 | 0.096892 | 0.0 |
2-3. Prediction of ODE parameter values
Now we have Y (estimated ODE parameter values) and X (estimated/forecasted indicator values without multicollinearity), we can predict ODE parameter values of future phases using ODEScenario().predict(days=<int>, name=<str>, seed=0, X=<pandas.DataFrame>). The new scenario is named “Predicted_with_X” here.
[13]:
snr.build_with_template(name="Predicted_with_X", template="Baseline")
snr.predict(days=30, name="Predicted_with_X", seed=0, X=future_df);
Using 1 cpus for n_jobs.
Data frequency is: D, used frequency is: D
Model Number: 1 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "fake_date", "transformations": {"0": "DifferencedTransformer", "1": "SinTrend"}, "transformation_params": {"0": {}, "1": {}}}
Model Number: 2 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 3 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean"} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SeasonalDifference", "1": "Round", "2": "Detrend"}, "transformation_params": {"0": {"lag_1": 7, "method": "Mean"}, "1": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": false}, "2": {"model": "GLS"}}}
Model Number: 4 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "RollingMeanTransformer", "1": "DifferencedTransformer", "2": "Detrend", "3": "Slice"}, "transformation_params": {"0": {"fixed": true, "window": 3}, "1": {}, "2": {"model": "Linear"}, "3": {"method": 100}}}
Model Number: 5 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "median", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "RobustScaler", "3": "Round", "4": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}, "3": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": true}, "4": {}}}
Model Number: 6 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "bkfilter", "1": "SinTrend", "2": "Detrend", "3": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {}, "2": {"model": "Linear"}, "3": {}}}
Model Number: 7 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "PositiveShift", "1": "SinTrend", "2": "bkfilter"}, "transformation_params": {"0": {}, "1": {}, "2": {}}}
Model Number: 8 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "SinTrend"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}}}
Model Number: 9 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
Model Number: 10 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 2, "lag_2": 7} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SinTrend", "1": "Round", "2": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {"model": "middle", "decimals": 2, "on_transform": false, "on_inverse": true}, "2": {}}}
Model Number: 11 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 2, "lag_2": 1} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "SeasonalDifference", "1": "QuantileTransformer", "2": "Detrend"}, "transformation_params": {"0": {"lag_1": 12, "method": "Median"}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"model": "GLS"}}}
Model Number: 12 with model SeasonalNaive in generation 0 of 1 with params {"method": "LastValue", "lag_1": 7, "lag_2": 2} and transformations {"fillna": "mean", "transformations": {"0": "QuantileTransformer", "1": "ClipOutliers"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"method": "clip", "std_threshold": 2, "fillna": null}}}
Model Number: 13 with model ConstantNaive in generation 0 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "PowerTransformer", "1": "QuantileTransformer", "2": "SeasonalDifference"}, "transformation_params": {"0": {}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"lag_1": 7, "method": "LastValue"}}}
Model Number: 14 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 28} and transformations {"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 15 with model SectionalMotif in generation 0 of 1 with params {"window": 10, "point_method": "weighted_mean", "distance_metric": "sokalmichener", "include_differenced": true, "k": 20, "stride_size": 1, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 16 with model SectionalMotif in generation 0 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": false, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 17 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 30} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 18 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "weighted_mean", "distance_metric": "mae", "k": 10, "datepart_method": "common_fourier"} and transformations {"fillna": "nearest", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 19 with model ConstantNaive in generation 0 of 1 with params {"constant": 1} and transformations {"fillna": "zero", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": 30, "transform_dict": null}, "2": {"method": "zscore", "method_params": {"distribution": "chi2", "alpha": 0.1}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": "quadratic", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}, "isolated_only": false}}}
Model Number: 20 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "KNNImputer", "transformations": {"0": "bkfilter", "1": "Discretize", "2": "QuantileTransformer", "3": "LevelShiftTransformer"}, "transformation_params": {"0": {}, "1": {"discretization": "center", "n_bins": 5}, "2": {"output_distribution": "uniform", "n_quantiles": 1000}, "3": {"window_size": 90, "alpha": 3.0, "grouping_forward_limit": 2, "max_level_shifts": 30, "alignment": "average"}}}
Model Number: 21 with model AverageValueNaive in generation 0 of 1 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "time", "transformations": {"0": "RollingMean100thN", "1": "AlignLastValue", "2": "RobustScaler", "3": "Round", "4": "MaxAbsScaler"}, "transformation_params": {"0": {}, "1": {"rows": 7, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {"decimals": -1, "on_transform": true, "on_inverse": false}, "4": {}}}
Model Number: 22 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "RollingMeanTransformer", "1": "AlignLastValue", "2": "Round", "3": "PctChangeTransformer", "4": "MaxAbsScaler"}, "transformation_params": {"0": {"fixed": false, "window": 7, "macro_micro": false, "center": true}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"decimals": 1, "on_transform": true, "on_inverse": false}, "3": {}, "4": {}}}
Model Number: 23 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 1} and transformations {"fillna": "quadratic", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "DifferencedTransformer", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": 365, "transform_dict": null}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 24 with model SeasonalityMotif in generation 0 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "mqae", "k": 1, "datepart_method": "simple", "independent": true} and transformations {"fillna": "ffill", "transformations": {"0": "RollingMean100thN", "1": "AlignLastValue", "2": "PositiveShift", "3": "AlignLastValue", "4": "StandardScaler", "5": "RobustScaler"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": false}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {}, "5": {}}}
Model Number: 25 with model SectionalMotif in generation 0 of 1 with params {"window": 50, "point_method": "weighted_mean", "distance_metric": "braycurtis", "include_differenced": true, "k": 5, "stride_size": 10, "regression_type": null} and transformations {"fillna": "zero", "transformations": {"0": "AlignLastValue", "1": "CenterSplit", "2": "MinMaxScaler", "3": "bkfilter", "4": "StandardScaler", "5": "DifferencedTransformer"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"fillna": "linear", "center": "zero"}, "2": {}, "3": {}, "4": {}, "5": {}}}
Model Number: 26 with model SectionalMotif in generation 0 of 1 with params {"window": 15, "point_method": "midhinge", "distance_metric": "sqeuclidean", "include_differenced": true, "k": 3, "stride_size": 2, "regression_type": null} and transformations {"fillna": "KNNImputer", "transformations": {"0": "RobustScaler"}, "transformation_params": {"0": {}}}
Model Number: 27 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
Model Number: 28 with model AverageValueNaive in generation 0 of 1 with params {"method": "Median", "window": null} and transformations {"fillna": "zero", "transformations": {"0": "MaxAbsScaler"}, "transformation_params": {"0": {}}}
Model Number: 29 with model SeasonalityMotif in generation 0 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "k": 10, "datepart_method": "expanded", "independent": false} and transformations {"fillna": "quadratic", "transformations": {"0": "AnomalyRemoval", "1": "StandardScaler", "2": "CumSumTransformer", "3": "AlignLastValue", "4": "AnomalyRemoval", "5": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "minmax", "method_params": {"alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "1": {}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "4": {"method": "mad", "method_params": {"distribution": "chi2", "alpha": 0.05}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}, "5": {}}}
Model Number: 30 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "akima", "transformations": {"0": "AlignLastValue", "1": "KalmanSmoothing", "2": "ClipOutliers", "3": "CumSumTransformer", "4": "bkfilter"}, "transformation_params": {"0": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"model_name": "MA", "state_transition": [[1, 0], [1, 0]], "process_noise": [[0.2, 0.0], [0.0, 0]], "observation_model": [[1, 0.1]], "observation_noise": 1.0, "em_iter": null}, "2": {"method": "clip", "std_threshold": 2, "fillna": null}, "3": {}, "4": {}}}
Model Number: 31 with model AverageValueNaive in generation 0 of 1 with params {"method": "Weighted_Mean", "window": null} and transformations {"fillna": "cubic", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}}}
Model Number: 32 with model AverageValueNaive in generation 0 of 1 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer", "1": "PositiveShift", "2": "SeasonalDifference", "3": "Slice", "4": "AnomalyRemoval", "5": "AnomalyRemoval"}, "transformation_params": {"0": {}, "1": {}, "2": {"lag_1": 12, "method": 20}, "3": {"method": 0.5}, "4": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": {"fillna": "ffill", "transformations": {"0": "MinMaxScaler", "1": "RobustScaler"}, "transformation_params": {"0": {}, "1": {}}}, "isolated_only": false}, "5": {"method": "IQR", "method_params": {"iqr_threshold": 2.0, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill", "transform_dict": null, "isolated_only": false}}}
Model Number: 33 with model AverageValueNaive in generation 0 of 1 with params {"method": "Median", "window": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "LevelShiftTransformer", "1": "CumSumTransformer"}, "transformation_params": {"0": {"window_size": 90, "alpha": 3.0, "grouping_forward_limit": 3, "max_level_shifts": 10, "alignment": "rolling_diff"}, "1": {}}}
Model Number: 34 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "fake_date", "transformations": {"0": "StandardScaler", "1": "StandardScaler", "2": "ClipOutliers"}, "transformation_params": {"0": {}, "1": {}, "2": {"method": "clip", "std_threshold": 3, "fillna": null}}}
Model Number: 35 with model SeasonalNaive in generation 0 of 1 with params {"method": "lastvalue", "lag_1": 2, "lag_2": 1} and transformations {"fillna": "SeasonalityMotifImputerLinMix", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 36 with model ConstantNaive in generation 0 of 1 with params {"constant": 1} and transformations {"fillna": "ffill", "transformations": {"0": null}, "transformation_params": {"0": {}}}
Model Number: 37 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "AlignLastValue", "1": "AlignLastValue", "2": "RollingMean100thN", "3": "MaxAbsScaler"}, "transformation_params": {"0": {"rows": 1, "lag": 28, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"rows": 1, "lag": 2, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {}}}
Model Number: 38 with model SeasonalityMotif in generation 0 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "chebyshev", "k": 3, "datepart_method": "recurring", "independent": true} and transformations {"fillna": "cubic", "transformations": {"0": "SeasonalDifference"}, "transformation_params": {"0": {"lag_1": 7, "method": "Mean"}}}
Model Number: 39 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "ClipOutliers", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"method": "clip", "std_threshold": 2, "fillna": null}, "3": {"rows": 1, "lag": 28, "method": "additive", "strength": 0.9, "first_value_only": true}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5401, in inverse_transform
df = self._inverse_one(df, i, trans_method=trans_method, bounds=bounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5356, in _inverse_one
df = self.transformers[i].inverse_transform(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2794, in inverse_transform
df.iloc[0:1] + adjustment,
~~~~~~~~~~~~~^~~~~~~~~~~~
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
return method(self, other)
^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 186, in __add__
return self._arith_method(other, operator.add)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7913, in _arith_method
new_data = self._dispatch_frame_op(other, op, axis=axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7945, in _dispatch_frame_op
bm = self._mgr.apply(array_op, right=right)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 361, in apply
applied = b.apply(f, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 393, in apply
result = func(self.values, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 283, in arithmetic_op
res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 218, in _na_arithmetic_op
result = func(left, right)
^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 242, in evaluate
return _evaluate(op, op_str, a, b) # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 73, in _evaluate_standard
return op(a, b)
^^^^^^^^
TypeError: unsupported operand type(s) for +: 'float' and 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 884, in predict
self.transformer_object.inverse_transform(df_forecast.forecast)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5406, in inverse_transform
raise Exception(err_str) from e
Exception: Transformer AlignLastValue failed on inverse from params rolling_mean_24 {'0': {'method': 'clip', 'std_threshold': 3, 'fillna': None}, '1': {'model': 'GLS', 'phi': 1, 'window': None, 'transform_dict': None}, '2': {'method': 'clip', 'std_threshold': 2, 'fillna': None}, '3': {'rows': 1, 'lag': 28, 'method': 'additive', 'strength': 0.9, 'first_value_only': True}}
in model 39 in generation 0: ConstantNaive
Model Number: 40 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "AnomalyRemoval", "1": "ScipyFilter"}, "transformation_params": {"0": {"method": "IQR", "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "linear", "transform_dict": null, "isolated_only": true}, "1": {"method": "butter", "method_args": {"N": 5, "window_size": 59, "btype": "lowpass", "analog": false, "output": "sos"}}}}
Model Number: 41 with model AverageValueNaive in generation 0 of 1 with params {"method": "Mean", "window": 24} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 42 with model ConstantNaive in generation 0 of 1 with params {"constant": -1} and transformations {"fillna": "nearest", "transformations": {"0": "SeasonalDifference", "1": "SeasonalDifference", "2": "RobustScaler", "3": "CenterSplit"}, "transformation_params": {"0": {"lag_1": 12, "method": "LastValue"}, "1": {"lag_1": 7, "method": "LastValue"}, "2": {}, "3": {"fillna": "akima", "center": "zero"}}}
Model Number: 43 with model SectionalMotif in generation 0 of 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "mahalanobis", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}}}
Model Number: 44 with model SeasonalNaive in generation 0 of 1 with params {"method": "mean", "lag_1": 2, "lag_2": 60} and transformations {"fillna": "ffill", "transformations": {"0": "bkfilter", "1": "AlignLastValue", "2": "Log", "3": "StandardScaler", "4": "AlignLastValue", "5": "EWMAFilter"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {}, "4": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "5": {"span": 7}}}
Model Number: 45 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "rolling_mean", "transformations": {"0": "bkfilter", "1": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 954, in predict
raise ValueError(
ValueError: Model returned NaN due to a preprocessing transformer {'fillna': 'rolling_mean', 'transformations': {'0': 'bkfilter', '1': 'AlignLastValue'}, 'transformation_params': {'0': {}, '1': {'rows': 1, 'lag': 1, 'method': 'multiplicative', 'strength': 1.0, 'first_value_only': False}}}. fail_on_forecast_nan=True
in model 45 in generation 0: ConstantNaive
Model Number: 46 with model SectionalMotif in generation 0 of 1 with params {"window": 7, "point_method": "weighted_mean", "distance_metric": "nan_euclidean", "include_differenced": true, "k": 10, "stride_size": 2, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "FFTFilter", "1": "RobustScaler", "2": "RegressionFilter"}, "transformation_params": {"0": {"cutoff": 0.1, "reverse": false}, "1": {}, "2": {"sigma": 3, "rolling_window": 90, "run_order": "trend_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}}}
Model Number: 47 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "MaxAbsScaler", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": {"fillna": null, "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "uniform", "alpha": 0.05}}}}}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 48 with model SeasonalNaive in generation 0 of 1 with params {"method": "mean", "lag_1": 364, "lag_2": null} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer", "1": "RobustScaler", "2": "bkfilter", "3": "HPFilter", "4": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {}, "2": {}, "3": {"part": "trend", "lamb": 1600}, "4": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 49 with model GLS in generation 0 of 1 with params {} and transformations {"fillna": "fake_date", "transformations": {"0": "BKBandpassFilter", "1": "StandardScaler", "2": "ScipyFilter", "3": "RobustScaler", "4": "RegressionFilter"}, "transformation_params": {"0": {"low": 8, "high": 364, "K": 1, "lanczos_factor": false, "return_diff": false}, "1": {}, "2": {"method": "savgol_filter", "method_args": {"window_length": 31, "polyorder": 2, "deriv": 0, "mode": "nearest"}}, "3": {}, "4": {"sigma": 1, "rolling_window": 90, "run_order": "season_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "simple", "polynomial_degree": null, "transform_dict": {"fillna": null, "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 2}}}, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5292, in _fit
df = self._fit_one(df, i)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5273, in _fit_one
df = pd.DataFrame(df, index=self.df_index, columns=self.df_colnames)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 827, in __init__
mgr = ndarray_to_mgr(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 336, in ndarray_to_mgr
_check_values_indices_shape_match(values, index, columns)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 420, in _check_values_indices_shape_match
raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
ValueError: Shape of passed values is (1169, 4), indices imply (1171, 4)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1446, in model_forecast
model = model.fit(df_train_low, future_regressor_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 847, in fit
df_train_transformed = self.transformer_object._fit(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5297, in _fit
raise Exception(err_str) from e
Exception: Transformer StandardScaler failed on fit from params fake_date {'0': {'low': 8, 'high': 364, 'K': 1, 'lanczos_factor': False, 'return_diff': False}, '1': {}, '2': {'method': 'savgol_filter', 'method_args': {'window_length': 31, 'polyorder': 2, 'deriv': 0, 'mode': 'nearest'}}, '3': {}, '4': {'sigma': 1, 'rolling_window': 90, 'run_order': 'season_first', 'regression_params': {'regression_model': {'model': 'ElasticNet', 'model_params': {}}, 'datepart_method': 'simple', 'polynomial_degree': None, 'transform_dict': {'fillna': None, 'transformations': {'0': 'EWMAFilter'}, 'transformation_params': {'0': {'span': 2}}}, 'holiday_countries_used': False}, 'holiday_params': None, 'trend_method': 'rolling_mean'}}
in model 49 in generation 0: GLS
Model Number: 50 with model SeasonalNaive in generation 0 of 1 with params {"method": "median", "lag_1": 58, "lag_2": 24} and transformations {"fillna": "rolling_mean", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 51 with model SeasonalityMotif in generation 0 of 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "chebyshev", "k": 3, "datepart_method": "simple_2", "independent": false} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "CenterSplit", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"fillna": "mean", "center": "zero"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 52 with model LastValueNaive in generation 0 of 1 with params {} and transformations {"fillna": "zero", "transformations": {"0": "ClipOutliers", "1": "FFTDecomposition", "2": "Round", "3": "SeasonalDifference", "4": "AlignLastValue", "5": "MinMaxScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"n_harmonics": null, "detrend": "linear"}, "2": {"decimals": 1, "on_transform": true, "on_inverse": true}, "3": {"lag_1": 12, "method": "Mean"}, "4": {"rows": 1, "lag": 7, "method": "additive", "strength": 1.0, "first_value_only": true}, "5": {}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5401, in inverse_transform
df = self._inverse_one(df, i, trans_method=trans_method, bounds=bounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5356, in _inverse_one
df = self.transformers[i].inverse_transform(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 2794, in inverse_transform
df.iloc[0:1] + adjustment,
~~~~~~~~~~~~~^~~~~~~~~~~~
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 76, in new_method
return method(self, other)
^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/arraylike.py", line 186, in __add__
return self._arith_method(other, operator.add)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7913, in _arith_method
new_data = self._dispatch_frame_op(other, op, axis=axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7945, in _dispatch_frame_op
bm = self._mgr.apply(array_op, right=right)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py", line 361, in apply
applied = b.apply(f, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/internals/blocks.py", line 393, in apply
result = func(self.values, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 283, in arithmetic_op
res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/ops/array_ops.py", line 218, in _na_arithmetic_op
result = func(left, right)
^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 242, in evaluate
return _evaluate(op, op_str, a, b) # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/pandas/core/computation/expressions.py", line 73, in _evaluate_standard
return op(a, b)
^^^^^^^^
TypeError: unsupported operand type(s) for +: 'float' and 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 884, in predict
self.transformer_object.inverse_transform(df_forecast.forecast)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5406, in inverse_transform
raise Exception(err_str) from e
Exception: Transformer AlignLastValue failed on inverse from params zero {'0': {'method': 'clip', 'std_threshold': 4, 'fillna': None}, '1': {'n_harmonics': None, 'detrend': 'linear'}, '2': {'decimals': 1, 'on_transform': True, 'on_inverse': True}, '3': {'lag_1': 12, 'method': 'Mean'}, '4': {'rows': 1, 'lag': 7, 'method': 'additive', 'strength': 1.0, 'first_value_only': True}, '5': {}}
in model 52 in generation 0: LastValueNaive
Model Number: 53 with model ConstantNaive in generation 0 of 1 with params {"constant": 0} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
New Generation: 1 of 1
Model Number: 54 with model ConstantNaive in generation 1 of 1 with params {"constant": 1} and transformations {"fillna": "mean", "transformations": {"0": "ReplaceConstant", "1": "ClipOutliers"}, "transformation_params": {"0": {"constant": 0, "reintroduction_model": {"model": "SGD", "model_params": {}, "datepart_method": "simple_2"}, "fillna": "linear"}, "1": {"method": "clip", "std_threshold": 3, "fillna": null}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5292, in _fit
df = self._fit_one(df, i)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5270, in _fit_one
df = self.transformers[i].fit_transform(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4289, in fit_transform
return self._fit(df)
^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4236, in _fit
self.model.fit(X, y)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 535, in fit
super().fit(X, Y, sample_weight=sample_weight, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 270, in fit
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 67, in __call__
return super().__call__(iterable_with_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1863, in __call__
return output if self.return_generator else list(output)
^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
res = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 129, in __call__
return self.function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 61, in _fit_estimator
estimator.fit(X, y, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 917, in fit
return self._fit(
^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 704, in _fit
self._partial_fit(
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 658, in _partial_fit
raise ValueError(
ValueError: The number of classes has to be greater than one; got 1 class
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1446, in model_forecast
model = model.fit(df_train_low, future_regressor_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 847, in fit
df_train_transformed = self.transformer_object._fit(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5297, in _fit
raise Exception(err_str) from e
Exception: Transformer ReplaceConstant failed on fit from params mean {'0': {'constant': 0, 'reintroduction_model': {'model': 'SGD', 'model_params': {}, 'datepart_method': 'simple_2'}, 'fillna': 'linear'}, '1': {'method': 'clip', 'std_threshold': 3, 'fillna': None}}
in model 54 in generation 1: ConstantNaive
Model Number: 55 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 1} and transformations {"fillna": "time", "transformations": {"0": "AlignLastValue", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 56 with model ConstantNaive in generation 1 of 1 with params {"constant": -1} and transformations {"fillna": "nearest", "transformations": {"0": "Discretize", "1": "IntermittentOccurrence", "2": "AnomalyRemoval", "3": "CenterSplit", "4": "MaxAbsScaler", "5": "RobustScaler"}, "transformation_params": {"0": {"discretization": "lower", "n_bins": 50}, "1": {"center": "mean"}, "2": {"method": "IsolationForest", "method_params": {"contamination": "auto", "n_estimators": 20, "max_features": 1.0, "bootstrap": false}, "fillna": "ffill", "transform_dict": {"fillna": "median", "transformations": {"0": "RegressionFilter"}, "transformation_params": {"0": {"sigma": 1, "rolling_window": 90, "run_order": "season_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": true}, "holiday_params": null, "trend_method": "rolling_mean"}}}, "isolated_only": false}, "3": {"fillna": "akima", "center": "zero"}, "4": {}, "5": {}}}
Model Number: 57 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 60} and transformations {"fillna": "median", "transformations": {"0": "CumSumTransformer", "1": "KalmanSmoothing"}, "transformation_params": {"0": {}, "1": {"model_name": "local linear hidden state with seasonal 7", "state_transition": [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]], "process_noise": [[0.0016, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1e-06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]], "observation_model": [[1, 1, 0, 0, 0, 0, 0, 0]], "observation_noise": 0.1, "em_iter": 10}}}
Model Number: 58 with model GLS in generation 1 of 1 with params {} and transformations {"fillna": "time", "transformations": {"0": "AlignLastValue", "1": "CumSumTransformer", "2": "bkfilter", "3": "bkfilter"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.2, "first_value_only": false}, "1": {}, "2": {}, "3": {}}}
Model Number: 59 with model AverageValueNaive in generation 1 of 1 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "zero", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
Model Number: 60 with model GLS in generation 1 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "SeasonalDifference", "2": "AlignLastValue", "3": "Round"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"lag_1": 24, "method": "LastValue"}, "2": {"rows": 7, "lag": 1, "method": "additive", "strength": 0.5, "first_value_only": false}, "3": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": true}}}
Model Number: 61 with model SectionalMotif in generation 1 of 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "sokalmichener", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean_24", "transformations": {"0": "FFTDecomposition", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"n_harmonics": 20, "detrend": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
Model Number: 62 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "linear", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 7, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 63 with model AverageValueNaive in generation 1 of 1 with params {"method": "Median", "window": 36} and transformations {"fillna": "time", "transformations": {"0": "RollingMean100thN", "1": "AlignLastValue"}, "transformation_params": {"0": {}, "1": {"rows": 7, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 64 with model GLS in generation 1 of 1 with params {} and transformations {"fillna": "median", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue", "2": "RollingMeanTransformer", "3": "ReplaceConstant"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"fixed": true, "window": 180, "macro_micro": true, "center": false}, "3": {"constant": 0, "reintroduction_model": {"model": "KNN", "model_params": {"n_neighbors": 5, "weights": "distance", "p": 2, "leaf_size": 30}, "datepart_method": "common_fourier"}, "fillna": "linear"}}}
Model Number: 65 with model GLS in generation 1 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer", "1": "bkfilter"}, "transformation_params": {"0": {}, "1": {}}}
Model Number: 66 with model SeasonalityMotif in generation 1 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "chebyshev", "k": 3, "datepart_method": "simple"} and transformations {"fillna": "cubic", "transformations": {"0": "SeasonalDifference", "1": "Detrend", "2": "Slice"}, "transformation_params": {"0": {"lag_1": 7, "method": "Mean"}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"method": 0.3}}}
Model Number: 67 with model ConstantNaive in generation 1 of 1 with params {"constant": 0} and transformations {"fillna": "zero", "transformations": {"0": "DifferencedTransformer", "1": "Detrend", "2": "AnomalyRemoval", "3": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {"model": "GLS", "phi": 1, "window": 30, "transform_dict": null}, "2": {"method": "zscore", "method_params": {"distribution": "chi2", "alpha": 0.1}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": "quadratic", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}, "isolated_only": false}, "3": {}}}
Model Number: 68 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 28} and transformations {"fillna": "linear", "transformations": {"0": "AlignLastValue", "1": "HistoricValues", "2": "PositiveShift"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"window": 10}, "2": {}}}
Model Number: 69 with model ConstantNaive in generation 1 of 1 with params {"constant": 0} and transformations {"fillna": "zero", "transformations": {"0": "ReplaceConstant", "1": "MaxAbsScaler", "2": "LocalLinearTrend"}, "transformation_params": {"0": {"constant": 0, "reintroduction_model": {"model": "SGD", "model_params": {}, "datepart_method": "simple_2"}, "fillna": "linear"}, "1": {}, "2": {"rolling_window": 0.1, "n_tails": 30, "n_future": 0.2, "method": "median", "macro_micro": true}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5292, in _fit
df = self._fit_one(df, i)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5270, in _fit_one
df = self.transformers[i].fit_transform(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4289, in fit_transform
return self._fit(df)
^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4236, in _fit
self.model.fit(X, y)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 535, in fit
super().fit(X, Y, sample_weight=sample_weight, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 270, in fit
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 67, in __call__
return super().__call__(iterable_with_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1863, in __call__
return output if self.return_generator else list(output)
^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
res = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 129, in __call__
return self.function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 61, in _fit_estimator
estimator.fit(X, y, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 917, in fit
return self._fit(
^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 704, in _fit
self._partial_fit(
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 658, in _partial_fit
raise ValueError(
ValueError: The number of classes has to be greater than one; got 1 class
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1446, in model_forecast
model = model.fit(df_train_low, future_regressor_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 847, in fit
df_train_transformed = self.transformer_object._fit(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5297, in _fit
raise Exception(err_str) from e
Exception: Transformer ReplaceConstant failed on fit from params zero {'0': {'constant': 0, 'reintroduction_model': {'model': 'SGD', 'model_params': {}, 'datepart_method': 'simple_2'}, 'fillna': 'linear'}, '1': {}, '2': {'rolling_window': 0.1, 'n_tails': 30, 'n_future': 0.2, 'method': 'median', 'macro_micro': True}}
in model 69 in generation 1: ConstantNaive
Model Number: 70 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "bkfilter", "1": "Detrend"}, "transformation_params": {"0": {}, "1": {"model": "Linear"}}}
Model Number: 71 with model SeasonalityMotif in generation 1 of 1 with params {"window": 5, "point_method": "midhinge", "distance_metric": "chebyshev", "k": 3, "datepart_method": "expanded"} and transformations {"fillna": "ffill", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "CenterSplit", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": null}, "2": {"fillna": "mean", "center": "zero"}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
Model Number: 72 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 2, "lag_2": 28} and transformations {"fillna": "mean", "transformations": {"0": "QuantileTransformer", "1": "ClipOutliers"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"method": "clip", "std_threshold": 2, "fillna": null}}}
Model Number: 73 with model AverageValueNaive in generation 1 of 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "time", "transformations": {"0": "KalmanSmoothing", "1": "RobustScaler", "2": "Round"}, "transformation_params": {"0": {"model_name": "ucm_deterministictrend_seasonal7", "state_transition": [[1, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, -1, -1, -1, -1, -1, -1]], "process_noise": [[0.001, 0, 0, 0, 0, 0, 0, 0], [0, 0.001, 0, 0, 0, 0, 0, 0], [0, 0, 0.001, 0, 0, 0, 0, 0], [0, 0, 0, 0.001, 0, 0, 0, 0], [0, 0, 0, 0, 0.001, 0, 0, 0], [0, 0, 0, 0, 0, 0.001, 0, 0], [0, 0, 0, 0, 0, 0, 0.001, 0], [0, 0, 0, 0, 0, 0, 0, 0]], "observation_model": [[1, 0, 1, 1, 1, 1, 1, 1]], "observation_noise": 0.03, "em_iter": null}, "1": {}, "2": {"decimals": 0, "on_transform": true, "on_inverse": false}}}
Model Number: 74 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}}}
Model Number: 75 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 60} and transformations {"fillna": "fake_date", "transformations": {"0": "MaxAbsScaler", "1": "MaxAbsScaler", "2": "FFTDecomposition"}, "transformation_params": {"0": {}, "1": {}, "2": {"n_harmonics": 20, "detrend": "quadratic"}}}
Model Number: 76 with model LastValueNaive in generation 1 of 1 with params {} and transformations {"fillna": "ffill", "transformations": {"0": "bkfilter", "1": "SinTrend"}, "transformation_params": {"0": {}, "1": {}}}
Model Number: 77 with model SectionalMotif in generation 1 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "mahalanobis", "include_differenced": true, "k": 20, "stride_size": 1, "regression_type": null} and transformations {"fillna": "cubic", "transformations": {"0": "AlignLastValue", "1": "CenterSplit", "2": "MinMaxScaler", "3": "bkfilter", "4": "StandardScaler", "5": "Slice"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"fillna": "linear", "center": "zero"}, "2": {}, "3": {}, "4": {}, "5": {"method": 0.3}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 866, in predict
df_forecast = self.model.predict(
^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/models/basics.py", line 1904, in predict
cdist(
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/scipy/spatial/distance.py", line 2980, in cdist
return cdist_fn(XA, XB, out=out, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/scipy/spatial/distance.py", line 1626, in __call__
XA, XB, typ, kwargs = _validate_cdist_input(
^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/scipy/spatial/distance.py", line 204, in _validate_cdist_input
kwargs = _validate_kwargs((XA, XB), mA + mB, n, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/scipy/spatial/distance.py", line 247, in _validate_mahalanobis_kwargs
VI = np.linalg.inv(CV).T.copy()
^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/numpy/linalg/linalg.py", line 561, in inv
ainv = _umath_linalg.inv(a, signature=signature, extobj=extobj)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/numpy/linalg/linalg.py", line 112, in _raise_linalgerror_singular
raise LinAlgError("Singular matrix")
numpy.linalg.LinAlgError: Singular matrix
in model 77 in generation 1: SectionalMotif
Model Number: 78 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 24} and transformations {"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 79 with model AverageValueNaive in generation 1 of 1 with params {"method": "Mean", "window": 364} and transformations {"fillna": "piecewise_polynomial", "transformations": {"0": "AlignLastValue", "1": "AlignLastValue", "2": "RobustScaler", "3": "Round"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"rows": 7, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {"decimals": -1, "on_transform": true, "on_inverse": false}}}
Model Number: 80 with model AverageValueNaive in generation 1 of 1 with params {"method": "Median", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "SeasonalDifference", "1": "FFTDecomposition", "2": "CenterLastValue"}, "transformation_params": {"0": {"lag_1": 96, "method": 20}, "1": {"n_harmonics": 0.5, "detrend": null}, "2": {"rows": 1}}}
Model Number: 81 with model AverageValueNaive in generation 1 of 1 with params {"method": "Weighted_Mean", "window": null} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "FFTDecomposition", "2": "CenterLastValue", "3": "ReplaceConstant"}, "transformation_params": {"0": {"lag_1": 96, "method": 20}, "1": {"n_harmonics": 0.5, "detrend": null}, "2": {"rows": 1}, "3": {"constant": 0, "reintroduction_model": {"model": "KNN", "model_params": {"n_neighbors": 14, "weights": "distance", "p": 2, "leaf_size": 30}, "datepart_method": "common_fourier"}, "fillna": "akima"}}}
Model Number: 82 with model SectionalMotif in generation 1 of 1 with params {"window": 10, "point_method": "mean", "distance_metric": "correlation", "include_differenced": true, "k": 10, "stride_size": 1, "regression_type": null} and transformations {"fillna": "rolling_mean", "transformations": {"0": "FFTFilter", "1": "RobustScaler", "2": "RegressionFilter"}, "transformation_params": {"0": {"cutoff": 0.1, "reverse": false}, "1": {}, "2": {"sigma": 3, "rolling_window": 90, "run_order": "trend_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "expanded", "polynomial_degree": null, "transform_dict": null, "holiday_countries_used": false}, "holiday_params": null, "trend_method": "rolling_mean"}}}
Model Number: 83 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 30} and transformations {"fillna": "quadratic", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": 365, "transform_dict": null}, "2": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": false}}}
Model Number: 84 with model SeasonalityMotif in generation 1 of 1 with params {"window": 10, "point_method": "midhinge", "distance_metric": "chebyshev", "k": 3, "datepart_method": "expanded"} and transformations {"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "PositiveShift", "2": "AlignLastValue", "3": "ReplaceConstant"}, "transformation_params": {"0": {"lag_1": 7, "method": "Mean"}, "1": {}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": true}, "3": {"constant": 0, "reintroduction_model": {"model": "SGD", "model_params": {}, "datepart_method": "simple"}, "fillna": "pchip"}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5292, in _fit
df = self._fit_one(df, i)
^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5270, in _fit_one
df = self.transformers[i].fit_transform(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4289, in fit_transform
return self._fit(df)
^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 4236, in _fit
self.model.fit(X, y)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 535, in fit
super().fit(X, Y, sample_weight=sample_weight, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 270, in fit
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 67, in __call__
return super().__call__(iterable_with_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1863, in __call__
return output if self.return_generator else list(output)
^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
res = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py", line 129, in __call__
return self.function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/multioutput.py", line 61, in _fit_estimator
estimator.fit(X, y, **fit_params)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/base.py", line 1351, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 917, in fit
return self._fit(
^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 704, in _fit
self._partial_fit(
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 658, in _partial_fit
raise ValueError(
ValueError: The number of classes has to be greater than one; got 1 class
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1446, in model_forecast
model = model.fit(df_train_low, future_regressor_train)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 847, in fit
df_train_transformed = self.transformer_object._fit(df)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5297, in _fit
raise Exception(err_str) from e
Exception: Transformer ReplaceConstant failed on fit from params ffill {'0': {'lag_1': 7, 'method': 'Mean'}, '1': {}, '2': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': True}, '3': {'constant': 0, 'reintroduction_model': {'model': 'SGD', 'model_params': {}, 'datepart_method': 'simple'}, 'fillna': 'pchip'}}
in model 84 in generation 1: SeasonalityMotif
Model Number: 85 with model AverageValueNaive in generation 1 of 1 with params {"method": "Mean", "window": 24} and transformations {"fillna": "ffill", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
Model Number: 86 with model AverageValueNaive in generation 1 of 1 with params {"method": "trimmed_mean_20", "window": 4} and transformations {"fillna": "time", "transformations": {"0": "FFTFilter", "1": "AlignLastValue", "2": "AnomalyRemoval", "3": "Round", "4": "MaxAbsScaler"}, "transformation_params": {"0": {"cutoff": 0.2, "reverse": false}, "1": {"rows": 7, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {"method": "nonparametric", "method_params": {"p": null, "z_init": 2.0, "z_limit": 10, "z_step": 0.25, "inverse": false, "max_contamination": 0.25, "mean_weight": 25, "sd_weight": 200, "anomaly_count_weight": 1.0}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": null, "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 6}}}, "isolated_only": false}, "3": {"decimals": -1, "on_transform": true, "on_inverse": false}, "4": {}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 954, in predict
raise ValueError(
ValueError: Model returned NaN due to a preprocessing transformer {'fillna': 'time', 'transformations': {'0': 'FFTFilter', '1': 'AlignLastValue', '2': 'AnomalyRemoval', '3': 'Round', '4': 'MaxAbsScaler'}, 'transformation_params': {'0': {'cutoff': 0.2, 'reverse': False}, '1': {'rows': 7, 'lag': 7, 'method': 'multiplicative', 'strength': 1.0, 'first_value_only': False}, '2': {'method': 'nonparametric', 'method_params': {'p': None, 'z_init': 2.0, 'z_limit': 10, 'z_step': 0.25, 'inverse': False, 'max_contamination': 0.25, 'mean_weight': 25, 'sd_weight': 200, 'anomaly_count_weight': 1.0}, 'fillna': 'rolling_mean_24', 'transform_dict': {'fillna': None, 'transformations': {'0': 'ClipOutliers'}, 'transformation_params': {'0': {'method': 'clip', 'std_threshold': 6}}}, 'isolated_only': False}, '3': {'decimals': -1, 'on_transform': True, 'on_inverse': False}, '4': {}}}. fail_on_forecast_nan=True
in model 86 in generation 1: AverageValueNaive
Model Number: 87 with model SeasonalNaive in generation 1 of 1 with params {"method": "lastvalue", "lag_1": 7, "lag_2": 1} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
Model Number: 88 with model AverageValueNaive in generation 1 of 1 with params {"method": "Exp_Weighted_Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer", "1": "AlignLastValue", "2": "RobustScaler", "3": "Round"}, "transformation_params": {"0": {}, "1": {"rows": 7, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {"decimals": -1, "on_transform": true, "on_inverse": false}}}
Template Eval Error: Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5401, in inverse_transform
df = self._inverse_one(df, i, trans_method=trans_method, bounds=bounds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5364, in _inverse_one
df = self.transformers[i].inverse_transform(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 1483, in inverse_transform
raise ValueError("NaN in DifferencedTransformer.inverse_transform")
ValueError: NaN in DifferencedTransformer.inverse_transform
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1599, in TemplateWizard
df_forecast = model_forecast(
^^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 1447, in model_forecast
return model.predict(
^^^^^^^^^^^^^^
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/evaluator/auto_model.py", line 884, in predict
self.transformer_object.inverse_transform(df_forecast.forecast)
File "/home/runner/work/covid19-sir/covid19-sir/.venv/lib/python3.12/site-packages/autots/tools/transform.py", line 5406, in inverse_transform
raise Exception(err_str) from e
Exception: Transformer DifferencedTransformer failed on inverse from params mean {'0': {}, '1': {'rows': 7, 'lag': 7, 'method': 'multiplicative', 'strength': 1.0, 'first_value_only': False}, '2': {}, '3': {'decimals': -1, 'on_transform': True, 'on_inverse': False}}
in model 88 in generation 1: AverageValueNaive
TotalRuntime missing in 2!
Validation Round: 1
Validation train index is DatetimeIndex(['2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',
'2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',
'2020-03-02', '2020-03-03',
...
'2023-03-30', '2023-03-31', '2023-04-01', '2023-04-02',
'2023-04-03', '2023-04-04', '2023-04-05', '2023-04-06',
'2023-04-07', '2023-04-08'],
dtype='datetime64[ns]', name='Date', length=1141, freq=None)
Model Number: 1 of 15 with model ConstantNaive for Validation 1 with params {"constant": 0} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
📈 1 - ConstantNaive with avg smape 30.35:
Model Number: 2 of 15 with model AverageValueNaive for Validation 1 with params {"method": "Mean", "window": 364} and transformations {"fillna": "piecewise_polynomial", "transformations": {"0": "AlignLastValue", "1": "AlignLastValue", "2": "RobustScaler", "3": "Round"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"rows": 7, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {"decimals": -1, "on_transform": true, "on_inverse": false}}}
2 - AverageValueNaive with avg smape 30.35:
Model Number: 3 of 15 with model SeasonalNaive for Validation 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 28} and transformations {"fillna": "linear", "transformations": {"0": "AlignLastValue", "1": "HistoricValues", "2": "PositiveShift"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"window": 10}, "2": {}}}
3 - SeasonalNaive with avg smape 32.37:
Model Number: 4 of 15 with model ConstantNaive for Validation 1 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "PowerTransformer", "1": "QuantileTransformer", "2": "SeasonalDifference"}, "transformation_params": {"0": {}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"lag_1": 7, "method": "LastValue"}}}
4 - ConstantNaive with avg smape 63.44:
Model Number: 5 of 15 with model AverageValueNaive for Validation 1 with params {"method": "Mean", "window": 24} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
5 - AverageValueNaive with avg smape 30.35:
Model Number: 6 of 15 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
6 - LastValueNaive with avg smape 30.35:
Model Number: 7 of 15 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}}}
7 - LastValueNaive with avg smape 30.35:
Model Number: 8 of 15 with model LastValueNaive for Validation 1 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
8 - LastValueNaive with avg smape 30.35:
Model Number: 9 of 15 with model SeasonalNaive for Validation 1 with params {"method": "lastvalue", "lag_1": 2, "lag_2": 1} and transformations {"fillna": "SeasonalityMotifImputerLinMix", "transformations": {"0": null}, "transformation_params": {"0": {}}}
9 - SeasonalNaive with avg smape 39.89:
Model Number: 10 of 15 with model SeasonalNaive for Validation 1 with params {"method": "lastvalue", "lag_1": 7, "lag_2": 1} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
10 - SeasonalNaive with avg smape 63.44:
Model Number: 11 of 15 with model GLS for Validation 1 with params {} and transformations {"fillna": "median", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue", "2": "RollingMeanTransformer", "3": "ReplaceConstant"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"fixed": true, "window": 180, "macro_micro": true, "center": false}, "3": {"constant": 0, "reintroduction_model": {"model": "KNN", "model_params": {"n_neighbors": 5, "weights": "distance", "p": 2, "leaf_size": 30}, "datepart_method": "common_fourier"}, "fillna": "linear"}}}
📈 11 - GLS with avg smape 30.34:
Model Number: 12 of 15 with model GLS for Validation 1 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "MaxAbsScaler", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": {"fillna": null, "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "uniform", "alpha": 0.05}}}}}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
12 - GLS with avg smape 30.34:
Model Number: 13 of 15 with model ConstantNaive for Validation 1 with params {"constant": 0} and transformations {"fillna": "zero", "transformations": {"0": "DifferencedTransformer", "1": "Detrend", "2": "AnomalyRemoval", "3": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {"model": "GLS", "phi": 1, "window": 30, "transform_dict": null}, "2": {"method": "zscore", "method_params": {"distribution": "chi2", "alpha": 0.1}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": "quadratic", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}, "isolated_only": false}, "3": {}}}
📈 13 - ConstantNaive with avg smape 30.3:
Model Number: 14 of 15 with model AverageValueNaive for Validation 1 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
14 - AverageValueNaive with avg smape 34.43:
Model Number: 15 of 15 with model SeasonalNaive for Validation 1 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 30} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
15 - SeasonalNaive with avg smape 72.2:
Validation Round: 2
Validation train index is DatetimeIndex(['2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',
'2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',
'2020-03-02', '2020-03-03',
...
'2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03',
'2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07',
'2023-03-08', '2023-03-09'],
dtype='datetime64[ns]', name='Date', length=1111, freq=None)
Model Number: 1 of 15 with model ConstantNaive for Validation 2 with params {"constant": 0} and transformations {"fillna": "mean", "transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}
📈 1 - ConstantNaive with avg smape 51.85:
Model Number: 2 of 15 with model AverageValueNaive for Validation 2 with params {"method": "Mean", "window": 364} and transformations {"fillna": "piecewise_polynomial", "transformations": {"0": "AlignLastValue", "1": "AlignLastValue", "2": "RobustScaler", "3": "Round"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"rows": 7, "lag": 7, "method": "multiplicative", "strength": 1.0, "first_value_only": false}, "2": {}, "3": {"decimals": -1, "on_transform": true, "on_inverse": false}}}
2 - AverageValueNaive with avg smape 51.85:
Model Number: 3 of 15 with model SeasonalNaive for Validation 2 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 28} and transformations {"fillna": "linear", "transformations": {"0": "AlignLastValue", "1": "HistoricValues", "2": "PositiveShift"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "1": {"window": 10}, "2": {}}}
3 - SeasonalNaive with avg smape 63.16:
Model Number: 4 of 15 with model ConstantNaive for Validation 2 with params {"constant": 0} and transformations {"fillna": "ffill", "transformations": {"0": "PowerTransformer", "1": "QuantileTransformer", "2": "SeasonalDifference"}, "transformation_params": {"0": {}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"lag_1": 7, "method": "LastValue"}}}
4 - ConstantNaive with avg smape 51.85:
Model Number: 5 of 15 with model AverageValueNaive for Validation 2 with params {"method": "Mean", "window": 24} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Slice", "2": "AlignLastValue", "3": "AlignLastValue", "4": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"method": 0.2}, "2": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.9, "first_value_only": false}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}, "4": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
5 - AverageValueNaive with avg smape 51.85:
Model Number: 6 of 15 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}
6 - LastValueNaive with avg smape 51.85:
Model Number: 7 of 15 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "mean", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 0.7, "first_value_only": false}}}
7 - LastValueNaive with avg smape 51.85:
Model Number: 8 of 15 with model LastValueNaive for Validation 2 with params {} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}
📈 8 - LastValueNaive with avg smape 51.7:
Model Number: 9 of 15 with model SeasonalNaive for Validation 2 with params {"method": "lastvalue", "lag_1": 2, "lag_2": 1} and transformations {"fillna": "SeasonalityMotifImputerLinMix", "transformations": {"0": null}, "transformation_params": {"0": {}}}
9 - SeasonalNaive with avg smape 51.85:
Model Number: 10 of 15 with model SeasonalNaive for Validation 2 with params {"method": "lastvalue", "lag_1": 7, "lag_2": 1} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
10 - SeasonalNaive with avg smape 51.85:
Model Number: 11 of 15 with model GLS for Validation 2 with params {} and transformations {"fillna": "median", "transformations": {"0": "ClipOutliers", "1": "AlignLastValue", "2": "RollingMeanTransformer", "3": "ReplaceConstant"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"fixed": true, "window": 180, "macro_micro": true, "center": false}, "3": {"constant": 0, "reintroduction_model": {"model": "KNN", "model_params": {"n_neighbors": 5, "weights": "distance", "p": 2, "leaf_size": 30}, "datepart_method": "common_fourier"}, "fillna": "linear"}}}
11 - GLS with avg smape 52.5:
Model Number: 12 of 15 with model GLS for Validation 2 with params {} and transformations {"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "MaxAbsScaler", "3": "AlignLastValue"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS", "phi": 1, "window": null, "transform_dict": {"fillna": null, "transformations": {"0": "AnomalyRemoval"}, "transformation_params": {"0": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "uniform", "alpha": 0.05}}}}}, "2": {}, "3": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}
12 - GLS with avg smape 53.23:
Model Number: 13 of 15 with model ConstantNaive for Validation 2 with params {"constant": 0} and transformations {"fillna": "zero", "transformations": {"0": "DifferencedTransformer", "1": "Detrend", "2": "AnomalyRemoval", "3": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {"model": "GLS", "phi": 1, "window": 30, "transform_dict": null}, "2": {"method": "zscore", "method_params": {"distribution": "chi2", "alpha": 0.1}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": "quadratic", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 4, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}, "isolated_only": false}, "3": {}}}
13 - ConstantNaive with avg smape 58.33:
Model Number: 14 of 15 with model AverageValueNaive for Validation 2 with params {"method": "Mean", "window": null} and transformations {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}
14 - AverageValueNaive with avg smape 51.89:
Model Number: 15 of 15 with model SeasonalNaive for Validation 2 with params {"method": "lastvalue", "lag_1": 364, "lag_2": 30} and transformations {"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}
15 - SeasonalNaive with avg smape 51.85:
TotalRuntime missing in 3!
Validation Round: 1
Validation train index is DatetimeIndex(['2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',
'2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',
'2020-03-02', '2020-03-03',
...
'2023-03-30', '2023-03-31', '2023-04-01', '2023-04-02',
'2023-04-03', '2023-04-04', '2023-04-05', '2023-04-06',
'2023-04-07', '2023-04-08'],
dtype='datetime64[ns]', name='Date', length=1141, freq=None)
TotalRuntime missing in 0!
Validation Round: 2
Validation train index is DatetimeIndex(['2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',
'2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',
'2020-03-02', '2020-03-03',
...
'2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03',
'2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07',
'2023-03-08', '2023-03-09'],
dtype='datetime64[ns]', name='Date', length=1111, freq=None)
TotalRuntime missing in 0!
Model Number: 1 with model Ensemble in generation 0 of Horizontal Ensembles with params {"model_name": "Horizontal", "model_count": 3, "model_metric": "Score", "models": {"7a14af550afa2194472cfc2e4e1440fb": {"Model": "LastValueNaive", "ModelParameters": "{}", "TransformationParameters": "{\"fillna\": \"mean\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"QuantileTransformer\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 1, \"fillna\": null}, \"1\": {\"output_distribution\": \"uniform\", \"n_quantiles\": 1000}}}"}, "fc7912b3f7a4c6b33e04315841bea19d": {"Model": "AverageValueNaive", "ModelParameters": "{\"method\": \"Mean\", \"window\": 24}", "TransformationParameters": "{\"fillna\": \"mean\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"Slice\", \"2\": \"AlignLastValue\", \"3\": \"AlignLastValue\", \"4\": \"AlignLastValue\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 4, \"fillna\": null}, \"1\": {\"method\": 0.2}, \"2\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 0.9, \"first_value_only\": false}, \"3\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 0.7, \"first_value_only\": false}, \"4\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false}}}"}, "e25cd43451e41b017236ed8d591b32a5": {"Model": "GLS", "ModelParameters": "{}", "TransformationParameters": "{\"fillna\": \"median\", \"transformations\": {\"0\": \"ClipOutliers\", \"1\": \"AlignLastValue\", \"2\": \"RollingMeanTransformer\", \"3\": \"ReplaceConstant\"}, \"transformation_params\": {\"0\": {\"method\": \"clip\", \"std_threshold\": 3.5, \"fillna\": null}, \"1\": {\"rows\": 1, \"lag\": 1, \"method\": \"additive\", \"strength\": 1.0, \"first_value_only\": false}, \"2\": {\"fixed\": true, \"window\": 180, \"macro_micro\": true, \"center\": false}, \"3\": {\"constant\": 0, \"reintroduction_model\": {\"model\": \"KNN\", \"model_params\": {\"n_neighbors\": 5, \"weights\": \"distance\", \"p\": 2, \"leaf_size\": 30}, \"datepart_method\": \"common_fourier\"}, \"fillna\": \"linear\"}}}"}}, "series": {"theta": "fc7912b3f7a4c6b33e04315841bea19d", "kappa": "7a14af550afa2194472cfc2e4e1440fb", "rho": "fc7912b3f7a4c6b33e04315841bea19d", "sigma": "e25cd43451e41b017236ed8d591b32a5"}} and transformations {}
Ensemble Horizontal component 1 of 3 LastValueNaive started
Ensemble Horizontal component 2 of 3 AverageValueNaive started
Ensemble Horizontal component 3 of 3 GLS started
Ensemble Horizontal component 1 of 3 LastValueNaive started
Ensemble Horizontal component 2 of 3 AverageValueNaive started
Ensemble Horizontal component 3 of 3 GLS started
2024-04-27 at 02:56:05 | ERROR | validation of end failed
2024-04-27 at 02:56:05 | ERROR | validation of end failed
3. Compare scenarios
As explained with Tutorial: Scenario analysis, we can compare scenarios.
[14]:
# Adjust the last date, appending a phase
snr.append();
2024-04-27 at 02:56:05 | ERROR | validation of end failed
2024-04-27 at 02:56:05 | ERROR | validation of end failed
2024-04-27 at 02:56:05 | ERROR | validation of end failed
[15]:
# Compare reproduction number
ymin = snr.compare_param("Rt", date_range=(future_start_date, None), display=False).min().min()
snr.compare_param("Rt", date_range=(future_start_date, None), ylim=(ymin, None));
Note that minimum value of y in the figure was changed to focus on the differences of the scenarios.
[16]:
ymin_value = snr.compare_cases("Confirmed", date_range=(future_start_date, None), display=False).Predicted.min()
snr.compare_cases("Confirmed", date_range=(future_start_date, None), ylim=(ymin_value, None));
[17]:
# Show representative values
snr.describe()
[17]:
| max(Infected) | argmax(Infected) | Confirmed on 07Jul2023 | Infected on 07Jul2023 | Fatal on 07Jul2023 | |
|---|---|---|---|---|---|
| Baseline | 3659176.0 | 2022-08-15 | 34660345.0 | 285210.0 | 78941.0 |
| Predicted | 3659176.0 | 2022-08-15 | 34651079.0 | 278741.0 | 82885.0 |
| Predicted_with_X | 3659176.0 | 2022-08-15 | 34659939.0 | 284596.0 | 78939.0 |
Thank you!