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Avoid warning in Many-Models Notebook #1971

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Dec 10, 2022
Merged

Avoid warning in Many-Models Notebook #1971

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f456c0b
avoid warning
Dec 9, 2022
191eb16
update reason for dropping column
Dec 9, 2022
25a9103
update data_preprocessing_tabular script
Dec 9, 2022
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1 change: 0 additions & 1 deletion ...h-azureml/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb
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Expand Up @@ -368,7 +368,6 @@
"\n",
"forecasting_parameters = ForecastingParameters(\n",
" time_column_name=TIME_COLNAME,\n",
" drop_column_names=\"Revenue\",\n",
" forecast_horizon=6,\n",
" time_series_id_column_names=partition_column_names,\n",
" cv_step_size=\"auto\",\n",
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5 changes: 3 additions & 2 deletions ...torials/automl-with-azureml/forecasting-many-models/auto-ml-forecasting-many-models.ipynb
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Expand Up @@ -433,7 +433,6 @@
"\n",
"forecasting_parameters = ForecastingParameters(\n",
" time_column_name=\"WeekStarting\",\n",
" drop_column_names=\"Revenue\",\n",
" forecast_horizon=6,\n",
" time_series_id_column_names=partition_column_names,\n",
" cv_step_size=\"auto\",\n",
Expand Down Expand Up @@ -469,7 +468,9 @@
"\n",
"Reuse of previous results (``allow_reuse``) is key when using pipelines in a collaborative environment since eliminating unnecessary reruns offers agility. Reuse is the default behavior when the ``script_name``, ``inputs``, and the parameters of a step remain the same. When reuse is allowed, results from the previous run are immediately sent to the next step. If ``allow_reuse`` is set to False, a new run will always be generated for this step during pipeline execution.\n",
"\n",
"> Note that we only support partitioned FileDataset and TabularDataset without partition when using such output as input."
"> Note that we only support partitioned FileDataset and TabularDataset without partition when using such output as input.\n",
"\n",
"> Note that we **drop column** \"Revenue\" from the dataset in this step as this is not relevant for forecasting with the dataset used in this example. **Please modify the logic based on your data**."
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]
},
{
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6 changes: 6 additions & 0 deletions ...torials/automl-with-azureml/forecasting-many-models/scripts/data_preprocessing_tabular.py
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Expand Up @@ -11,6 +11,12 @@ def main(args):
dataset = run_context.input_datasets["train_10_models"]
df = dataset.to_pandas_dataframe()

# Drop the column "Revenue" from the dataset
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# Please remove if this is not required
drop_column_name = "Revenue"
if drop_column_name in df.columns:
df.drop(drop_column_name, axis=1, inplace=True)

# Apply any data pre-processing techniques here

df.to_parquet(output / "data_prepared_result.parquet", compression=None)
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