docs: add samples for ArimiaPlus time_series_id_col feature

.gitignore

@@ -60,5 +60,6 @@ coverage.xml
 system_tests/local_test_setup
 
 # Make sure a generated file isn't accidentally committed.
+demo.ipynb
 pylintrc
 pylintrc.test

owlbot.py

@@ -64,6 +64,13 @@
 # Fixup files
 # ----------------------------------------------------------------------------
 
+# Add scratch space for experimentation to .gitignore.
+assert 1 == s.replace(
+ [".gitignore"],
+ re.escape("# Make sure a generated file isn't accidentally committed.\n"),
+ "# Make sure a generated file isn't accidentally committed.\ndemo.ipynb\n",
+)
+
 # Encourage sharring all relevant versions in bug reports.
 assert 1 == s.replace( # bug_report.md
  [".github/ISSUE_TEMPLATE/bug_report.md"],

samples/snippets/create_multiple_timeseries_forecasting_model_test.py

@@ -73,26 +73,91 @@ def test_multiple_timeseries_forecasting_model(random_model_id: str) -> None:
  from bigframes.ml import forecasting
  import bigframes.pandas as bpd
 
+ model = forecasting.ARIMAPlus(
+ # To reduce the query runtime with the compromise of a potential slight
+ # drop in model quality, you could decrease the value of the
+ # auto_arima_max_order. This shrinks the search space of hyperparameter
+ # tuning in the auto.ARIMA algorithm.
+ auto_arima_max_order=5,
+ )
+
  df = bpd.read_gbq("bigquery-public-data.new_york.citibike_trips")
 
+ # This query creates twelve time series models, one for each of the twelve
+ # Citi Bike start stations in the input data. If you remove this row
+ # filter, there would be 600+ time series to forecast.
+ df = df[df["start_station_name"].str.contains("Central Park")]
+
  features = bpd.DataFrame(
  {
- "num_trips": df.starttime,
+ "start_station_name": df["start_station_name"],
+ "num_trips": df["starttime"],
  "date": df["starttime"].dt.date,
  }
  )
- num_trips = features.groupby(["date"], as_index=False).count()
- model = forecasting.ARIMAPlus()
+ num_trips = features.groupby(
+ ["start_station_name", "date"],
+ as_index=False,
+ ).count()
 
  X = num_trips["date"].to_frame()
  y = num_trips["num_trips"].to_frame()
 
- model.fit(X, y)
+ model.fit(
+ X,
+ y,
+ # The input data that you want to get forecasts for,
+ # in this case the Citi Bike station, as represented by the
+ # start_station_name column.
+ id_col=num_trips["start_station_name"].to_frame(),
+ )
+
  # The model.fit() call above created a temporary model.
  # Use the to_gbq() method to write to a permanent location.
-
  model.to_gbq(
  your_model_id, # For example: "bqml_tutorial.nyc_citibike_arima_model",
  replace=True,
  )
  # [END bigquery_dataframes_bqml_arima_multiple_step_3_fit]
+
+ # [START bigquery_dataframes_bqml_arima_multiple_step_4_evaluate]
+ # Evaluate the time series models by using the summary() function. The summary()
+ # function shows you the evaluation metrics of all the candidate models evaluated
+ # during the process of automatic hyperparameter tuning.
+ summary = model.summary()
+ print(summary.peek())
+
+ # Expected output:
+ # start_station_name non_seasonal_p non_seasonal_d non_seasonal_q has_drift log_likelihood AIC variance ...
+ # 1 Central Park West & W 72 St 0 1 5 False -1966.449243 3944.898487 1215.689281 ...
+ # 8 Central Park W & W 96 St 0 0 5 False -274.459923 562.919847 655.776577 ...
+ # 9 Central Park West & W 102 St 0 0 0 False -226.639918 457.279835 258.83582 ...
+ # 11 Central Park West & W 76 St 1 1 2 False -1700.456924 3408.913848 383.254161 ...
+ # 4 Grand Army Plaza & Central Park S 0 1 5 False -5507.553498 11027.106996 624.138741 ...
+ # [END bigquery_dataframes_bqml_arima_multiple_step_4_evaluate]
+
+ # [START bigquery_dataframes_bqml_arima_multiple_step_5_coefficients]
+ coef = model.coef_
+ print(coef.peek())
+
+ # Expected output:
+ # start_station_name ar_coefficients ma_coefficients intercept_or_drift
+ # 5 Central Park West & W 68 St [] [-0.41014089 0.21979212 -0.59854213 -0.251438... 0.0
+ # 6 Central Park S & 6 Ave [] [-0.71488957 -0.36835772 0.61008532 0.183290... 0.0
+ # 0 Central Park West & W 85 St [] [-0.39270166 -0.74494638 0.76432596 0.489146... 0.0
+ # 3 W 82 St & Central Park West [-0.50219511 -0.64820817] [-0.20665325 0.67683137 -0.68108631] 0.0
+ # 11 W 106 St & Central Park West [-0.70442887 -0.66885553 -0.25030325 -0.34160669] [] 0.0
+ # [END bigquery_dataframes_bqml_arima_multiple_step_5_coefficients]
+
+ # [START bigquery_dataframes_bqml_arima_multiple_step_6_forecast]
+ prediction = model.predict(horizon=3, confidence_level=0.9)
+
+ print(prediction.peek())
+ # Expected output:
+ # forecast_timestamp start_station_name forecast_value standard_error confidence_level ...
+ # 4 2016-10-01 00:00:00+00:00 Central Park S & 6 Ave 302.377201 32.572948 0.9 ...
+ # 14 2016-10-02 00:00:00+00:00 Central Park North & Adam Clayton Powell Blvd 263.917567 45.284082 0.9 ...
+ # 1 2016-09-25 00:00:00+00:00 Central Park West & W 85 St 189.574706 39.874856 0.9 ...
+ # 20 2016-10-02 00:00:00+00:00 Central Park West & W 72 St 175.474862 40.940794 0.9 ...
+ # 12 2016-10-01 00:00:00+00:00 W 106 St & Central Park West 63.88163 18.088868 0.9 ...
+ # [END bigquery_dataframes_bqml_arima_multiple_step_6_forecast]