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docs: add python code sample to multiple timeseries forecasting #531

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tswast merged 8 commits into from
May 6, 2024
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docs: add python code sample to multiple timeseries forecasting #531

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docs: add python code sample to multiple timeseries forecasting
Mar 27, 2024
5712534
Merge branch 'main' into Stephanie446
DevStephanie Mar 27, 2024
25e8d63
docs: add python code sample to multiple timeseries forecasting
Mar 27, 2024
37341e5
Merge branch 'Stephanie446' of https://github.com/googleapis/python-b…
Mar 27, 2024
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Merge branch 'main' into Stephanie446
DevStephanie Mar 27, 2024
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Merge branch 'main' into Stephanie446
DevStephanie Apr 1, 2024
cec173a
fix: if setting recurison limit fails, still succeed the import
May 6, 2024
58d08ae
Merge branch 'main' into Stephanie446
tswast May 6, 2024
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98 changes: 98 additions & 0 deletions samples/snippets/create_multiple_timeseries_forecasting_model.py
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


def test_multiple_timeseries_forecasting_model(random_model_id):
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your_model_id = random_model_id

# [START bigquery_dataframes_bqml_arima_multiple_step_2_visualize]

import bigframes.pandas as bpd

df = bpd.read_gbq("bigquery-public-data.new_york.citibike_trips")

features = bpd.DataFrame(
{
"num_trips": df.starttime,
"date": df["starttime"].dt.date,
}
)
date = df["starttime"].dt.date
df.groupby([date])
num_trips = features.groupby(["date"]).count()

# Results from running "print(num_trips)"

# num_trips
# date
# 2013-07-01 16650
# 2013-07-02 22745
# 2013-07-03 21864
# 2013-07-04 22326
# 2013-07-05 21842
# 2013-07-06 20467
# 2013-07-07 20477
# 2013-07-08 21615
# 2013-07-09 26641
# 2013-07-10 25732
# 2013-07-11 24417
# 2013-07-12 19006
# 2013-07-13 26119
# 2013-07-14 29287
# 2013-07-15 28069
# 2013-07-16 29842
# 2013-07-17 30550
# 2013-07-18 28869
# 2013-07-19 26591
# 2013-07-20 25278
# 2013-07-21 30297
# 2013-07-22 25979
# 2013-07-23 32376
# 2013-07-24 35271
# 2013-07-25 31084

num_trips.plot.line(
# Rotate the x labels so they are more visible.
rot=45,
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)

# [END bigquery_dataframes_bqml_arima_multiple_step_2_visualize]

# [START bigquery_dataframes_bqml_arima_multiple_step_3_fit]
from bigframes.ml import forecasting
import bigframes.pandas as bpd

df = bpd.read_gbq("bigquery-public-data.new_york.citibike_trips")

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features = bpd.DataFrame(
{
"num_trips": df.starttime,
"date": df["starttime"].dt.date,
}
)
num_trips = features.groupby(["date"], as_index=False).count()
model = forecasting.ARIMAPlus()

X = num_trips["date"].to_frame()
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@GarrettWu GarrettWu Apr 1, 2024

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to_frame() not needed.

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@DevStephanie DevStephanie May 6, 2024

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ok, will update that.

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@DevStephanie DevStephanie May 6, 2024

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Looks like we might need .to_frame() because without it, I see

AttributeError                            Traceback (most recent call last)
Cell In[12], line 18
     15 X = num_trips["date"]
     16 y = num_trips["num_trips"]
---> 18 model.fit(X, y)
     19 # The model.fit() call above created a temporary model.
     20 # Use the to_gbq() method to write to a permanent location.
     22 model.to_gbq(
     23     your_model_id,  # For example: "bqml_tutorial.sample_model",
     24     replace=True,
     25 )

File ~/python-bigquery-dataframes/bigframes/ml/base.py:163, in SupervisedTrainablePredictor.fit(self, X, y)
    158 def fit(
    159     self: _T,
    160     X: Union[bpd.DataFrame, bpd.Series],
    161     y: Union[bpd.DataFrame, bpd.Series],
    162 ) -> _T:
--> 163     return self._fit(X, y)

File ~/python-bigquery-dataframes/bigframes/core/log_adapter.py:44, in method_logger.<locals>.wrapper(*args, **kwargs)
     42 if api_method_name.startswith("__") or not api_method_name.startswith("_"):
     43     add_api_method(full_method_name)
---> 44 return method(*args, **kwargs)

File ~/python-bigquery-dataframes/bigframes/ml/forecasting.py:218, in ARIMAPlus._fit(self, X, y, transforms)
    197 def _fit(
    198     self,
    199     X: Union[bpd.DataFrame, bpd.Series],
    200     y: Union[bpd.DataFrame, bpd.Series],
    201     transforms: Optional[List[str]] = None,
    202 ):
    203     """Fit the model to training data.
    204 
    205     Args:
   (...)
    216         ARIMAPlus: Fitted estimator.
    217     """
--> 218     if X.columns.size != 1:
    219         raise ValueError(
    220             "Time series timestamp input X must only contain 1 column."
    221         )
    222     if y.columns.size != 1:

File ~/python-bigquery-dataframes/bigframes/series.py:1062, in Series.__getattr__(self, key)
   1053     raise AttributeError(
   1054         textwrap.dedent(
   1055             f"""
   (...)
   1059         )
   1060     )
   1061 else:
-> 1062     raise AttributeError(key)

AttributeError: columns

y = num_trips["num_trips"].to_frame()

model.fit(X, y)
# 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]