googleapis · DevStephanie · Oct 25, 2023 · Oct 25, 2023 · Oct 25, 2023
@@ -0,0 +1,71 @@
+# 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 test_bqml_getting_started():
+    # [START bigquery_getting_Started_bqml_tutorial]
+    # DataFrame created from a BigQuery table:
+    import bigframes
+    import bigframes.pandas as bpd
+
+    # Original sql query from tutorial, translated to Python using BigQuery BigFrames dataframes
+    df = bpd.read_gbq(
+        """
+    SELECT GENERATE_UUID() AS rowindex, *
+    FROM
+    `bigquery-public-data.google_analytics_sample.ga_sessions_*`
+    WHERE
+    _TABLE_SUFFIX BETWEEN '20160801' AND '20170630'
+    """,
+        index_col="rowindex",
+    )
+
+    # Printing dataframe, setting totals value
+    totals = df["totals"]
+
+    # Using totals, selecting id for transaction example
+    totals["0000fb2c-2861-40be-9c6c-309afd7e7883"]
+    transactions = totals.struct.field("transactions")
+    # Columns to indicate whether there was purchase
+    label = transactions.notnull().map({True: 1, False: 0})
+
+    # Operating systems of users
+    operatingSystem = df["device"].struct.field("operatingSystem")
+    operatingSystem = operatingSystem.fillna("")
+
+    # Indicates whether the users devices are mobile
+    isMobile = df["device"].struct.field("isMobile")
+
+    # Country from which the sessions originate, IP address based
+    country = df["geoNetwork"].struct.field("country").fillna("")
+
+    # Total number of pageviews within the session
+    pageviews = totals.struct.field("pageviews").fillna(0)
+
+    # Setting features for dataframe,
+    features = bpd.DataFrame(
+        {"os": operatingSystem, "is_mobile": isMobile, "pageviews": pageviews}
+    )
+
+    # Printing out the dataframe
+    df
+
+    # Creating a logistics regression model -
+    from bigframes.ml.linear_model import LogisticRegression
+
+    model = LogisticRegression()
+    # Model training parameters,
+    model.fit(features, label)
+    # Write a DataFRame to a BigQuery table-
+    model.to_gbq("bqml_tutorial.sample_model", replace=True)