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# Copyright 2023 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 run_quickstart(project_id: str) -> None:
your_gcp_project_id = project_id
# [START bigquery_bigframes_quickstart_create_dataframe]
import bigframes.pandas as bpd
# Set BigQuery DataFrames options
# Note: The project option is not required in all environments.
# On BigQuery Studio, the project ID is automatically detected.
bpd.options.bigquery.project = your_gcp_project_id
# Use "partial" ordering mode to generate more efficient queries, but the
# order of the rows in DataFrames may not be deterministic if you have not
# explictly sorted it. Some operations that depend on the order, such as
# head() will not function until you explictly order the DataFrame. Set the
# ordering mode to "strict" (default) for more pandas compatibility.
bpd.options.bigquery.ordering_mode = "partial"
# Create a DataFrame from a BigQuery table
query_or_table = "bigquery-public-data.ml_datasets.penguins"
df = bpd.read_gbq(query_or_table)
# Efficiently preview the results using the .peek() method.
df.peek()
# [END bigquery_bigframes_quickstart_create_dataframe]
# [START bigquery_bigframes_quickstart_calculate_print]
# Use the DataFrame just as you would a pandas DataFrame, but calculations
# happen in the BigQuery query engine instead of the local system.
average_body_mass = df["body_mass_g"].mean()
print(f"average_body_mass: {average_body_mass}")
# [END bigquery_bigframes_quickstart_calculate_print]
# [START bigquery_bigframes_quickstart_eval_metrics]
# Create the Linear Regression model
from bigframes.ml.linear_model import LinearRegression
# Filter down to the data we want to analyze
adelie_data = df[df.species == "Adelie Penguin (Pygoscelis adeliae)"]
# Drop the columns we don't care about
adelie_data = adelie_data.drop(columns=["species"])
# Drop rows with nulls to get our training data
training_data = adelie_data.dropna()
# Pick feature columns and label column
X = training_data[
[
"island",
"culmen_length_mm",
"culmen_depth_mm",
"flipper_length_mm",
"sex",
]
]
y = training_data[["body_mass_g"]]
model = LinearRegression(fit_intercept=False)
model.fit(X, y)
model.score(X, y)
# [END bigquery_bigframes_quickstart_eval_metrics]
# close session and reset option so not to affect other tests
bpd.close_session()
bpd.options.reset()