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docs: add predict sample to samples/snippets/bqml_getting_started_tes…
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…t.py (#388)

* docs: Add a sample to demonstrate the evaluation results

* Adding comments explaining logistic regression results

* editing read_gbd explanation

* docs: add predict sample to samples/snippets/bqml_getting_started_test.py

* correcting variable names

* Correcting python variables

* feat: add predict by visit to samples/snippets/bqml_getting_started_test.py

* file

* file

* file

---------

Co-authored-by: Tim Sweña (Swast, formerly) <[email protected]>
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@DevStephanie @tswast
DevStephanie and tswast authored Mar 8, 2024
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Showing 1 changed file with 171 additions and 42 deletions.
213 changes: 171 additions & 42 deletions samples/snippets/bqml_getting_started_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@


def test_bqml_getting_started(random_model_id):
your_model_id = random_model_id
your_model_id = random_model_id # for example: bqml_tutorial.sample_model

# [START bigquery_dataframes_bqml_getting_started_tutorial]
from bigframes.ml.linear_model import LogisticRegression
Expand All @@ -26,17 +26,12 @@ def test_bqml_getting_started(random_model_id):
# https://github.com/googleapis/python-bigquery-dataframes/issues/169
# for updates to `read_gbq` to support wildcard tables.

df = bpd.read_gbq(
"""
-- Since the order of rows isn't useful for the model training,
-- generate a random ID to use as the index for the DataFrame.
SELECT GENERATE_UUID() AS rowindex, *
FROM
`bigquery-public-data.google_analytics_sample.ga_sessions_*`
WHERE
_TABLE_SUFFIX BETWEEN '20160801' AND '20170630'
""",
index_col="rowindex",
df = bpd.read_gbq_table(
"bigquery-public-data.google_analytics_sample.ga_sessions_*",
filters=[
("_table_suffix", ">=", "20160801"),
("_table_suffix", "<=", "20170630"),
],
)

# Extract the total number of transactions within
Expand All @@ -53,14 +48,14 @@ def test_bqml_getting_started(random_model_id):
# ecommerce transactions within the Google Analytics session.
# If the number of transactions is NULL, the value in the label
# column is set to 0. Otherwise, it is set to 1.
label = transactions.notnull().map({True: 1, False: 0})
label = transactions.notnull().map({True: 1, False: 0}).rename("label")

# Extract the operating system of the visitor's device.
operatingSystem = df["device"].struct.field("operatingSystem")
operatingSystem = operatingSystem.fillna("")
operating_system = df["device"].struct.field("operatingSystem")
operating_system = operating_system.fillna("")

# Extract whether the visitor's device is a mobile device.
isMobile = df["device"].struct.field("isMobile")
is_mobile = df["device"].struct.field("isMobile")

# Extract the country from which the sessions originated, based on the IP address.
country = df["geoNetwork"].struct.field("country").fillna("")
Expand All @@ -72,8 +67,8 @@ def test_bqml_getting_started(random_model_id):
# to use as training data.
features = bpd.DataFrame(
{
"os": operatingSystem,
"is_mobile": isMobile,
"os": operating_system,
"is_mobile": is_mobile,
"country": country,
"pageviews": pageviews,
}
Expand All @@ -95,39 +90,36 @@ def test_bqml_getting_started(random_model_id):
# [START bigquery_dataframes_bqml_getting_started_tutorial_evaluate]
import bigframes.pandas as bpd

# Select model you'll use for training. `read_gbq_model` loads model data from a
# Select model you'll use for evaluating. `read_gbq_model` loads model data from a
# BigQuery, but you could also use the `model` object from the previous steps.
model = bpd.read_gbq_model(
your_model_id, # For example: "bqml_tutorial.sample_model",
)

# The WHERE clause — _TABLE_SUFFIX BETWEEN '20170701' AND '20170801' —
# limits the number of tables scanned by the query. The date range scanned is
# July 1, 2017 to August 1, 2017. This is the data you're using to evaluate the predictive performance
# of the model. It was collected in the month immediately following the time
# period spanned by the training data.

df = bpd.read_gbq(
"""
SELECT GENERATE_UUID() AS rowindex, *
FROM
`bigquery-public-data.google_analytics_sample.ga_sessions_*`
WHERE
_TABLE_SUFFIX BETWEEN '20170701' AND '20170801'
""",
index_col="rowindex",
# The filters parameter limits the number of tables scanned by the query.
# The date range scanned is July 1, 2017 to August 1, 2017. This is the
# data you're using to evaluate the predictive performance of the model.
# It was collected in the month immediately following the time period
# spanned by the training data.
df = bpd.read_gbq_table(
"bigquery-public-data.google_analytics_sample.ga_sessions_*",
filters=[
("_table_suffix", ">=", "20170701"),
("_table_suffix", "<=", "20170801"),
],
)

transactions = df["totals"].struct.field("transactions")
label = transactions.notnull().map({True: 1, False: 0})
operatingSystem = df["device"].struct.field("operatingSystem")
operatingSystem = operatingSystem.fillna("")
isMobile = df["device"].struct.field("isMobile")
label = transactions.notnull().map({True: 1, False: 0}).rename("label")
operating_system = df["device"].struct.field("operatingSystem")
operating_system = operating_system.fillna("")
is_mobile = df["device"].struct.field("isMobile")
country = df["geoNetwork"].struct.field("country").fillna("")
pageviews = df["totals"].struct.field("pageviews").fillna(0)
features = bpd.DataFrame(
{
"os": operatingSystem,
"is_mobile": isMobile,
"os": operating_system,
"is_mobile": is_mobile,
"country": country,
"pageviews": pageviews,
}
Expand Down Expand Up @@ -163,6 +155,143 @@ def test_bqml_getting_started(random_model_id):
# [1 rows x 6 columns]
# [END bigquery_dataframes_bqml_getting_started_tutorial_evaluate]

# [START bigquery_dataframes_bqml_getting_started_tutorial_predict]
# [START bigquery_dataframes_bqml_getting_started_tutorial_predict_by_country]
import bigframes.pandas as bpd

# Select model you'll use for predicting.
# `read_gbq_model` loads model data from
# BigQuery, but you could also use the `model`
# object from the previous steps.
model = bpd.read_gbq_model(
your_model_id, # For example: "bqml_tutorial.sample_model",
)

# The filters parameter limits the number of tables scanned by the query.
# The date range scanned is July 1, 2017 to August 1, 2017. This is the
# data you're using to make the prediction.
# It was collected in the month immediately following the time period
# spanned by the training data.
df = bpd.read_gbq_table(
"bigquery-public-data.google_analytics_sample.ga_sessions_*",
filters=[
("_table_suffix", ">=", "20170701"),
("_table_suffix", "<=", "20170801"),
],
)

operating_system = df["device"].struct.field("operatingSystem")
operating_system = operating_system.fillna("")
is_mobile = df["device"].struct.field("isMobile")
country = df["geoNetwork"].struct.field("country").fillna("")
pageviews = df["totals"].struct.field("pageviews").fillna(0)
features = bpd.DataFrame(
{
"os": operating_system,
"is_mobile": is_mobile,
"country": country,
"pageviews": pageviews,
}
)
# Use Logistic Regression predict method to predict results
# using your model.
# Find more information here in
# [BigFrames](https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.linear_model.LogisticRegression#bigframes_ml_linear_model_LogisticRegression_predict)

predictions = model.predict(features)

# Call groupby method to group predicted_label by country.
# Call sum method to get the total_predicted_label by country.
total_predicted_purchases = predictions.groupby(["country"])[
["predicted_label"]
].sum()

# Call the sort_values method with the parameter
# ascending = False to get the highest values.
# Call head method to limit to the 10 highest values.
total_predicted_purchases.sort_values(ascending=False).head(10)

# country
# United States 220
# Taiwan 8
# Canada 7
# India 2
# Japan 2
# Turkey 2
# Australia 1
# Brazil 1
# Germany 1
# Guyana 1
# Name: predicted_label, dtype: Int64

# [END bigquery_dataframes_bqml_getting_started_tutorial_predict_by_country]

# [START bigquery_dataframes_bqml_getting_started_tutorial_predict_by_visitor]

import bigframes.pandas as bpd

# Select model you'll use for predicting.
# `read_gbq_model` loads model data from
# BigQuery, but you could also use the `model`
# object from the previous steps.
model = bpd.read_gbq_model(
your_model_id, # For example: "bqml_tutorial.sample_model",
)

# The filters parameter limits the number of tables scanned by the query.
# The date range scanned is July 1, 2017 to August 1, 2017. This is the
# data you're using to make the prediction.
# It was collected in the month immediately following the time period
# spanned by the training data.
df = bpd.read_gbq_table(
"bigquery-public-data.google_analytics_sample.ga_sessions_*",
filters=[
("_table_suffix", ">=", "20170701"),
("_table_suffix", "<=", "20170801"),
],
)

operating_system = df["device"].struct.field("operatingSystem")
operating_system = operating_system.fillna("")
is_mobile = df["device"].struct.field("isMobile")
country = df["geoNetwork"].struct.field("country").fillna("")
pageviews = df["totals"].struct.field("pageviews").fillna(0)
full_visitor_id = df["fullVisitorId"]

features = bpd.DataFrame(
{
"os": operating_system,
"is_mobile": is_mobile,
"country": country,
"pageviews": pageviews,
"fullVisitorId": full_visitor_id,
}
)

predictions = model.predict(features)

# Call groupby method to group predicted_label by visitor.
# Call sum method to get the total_predicted_label by visitor.
total_predicted_purchases = predictions.groupby(["fullVisitorId"])[
["predicted_label"]
].sum()

# Call the sort_values method with the parameter
# ascending = False to get the highest values.
# Call head method to limit to the 10 highest values.
total_predicted_purchases.sort_values(ascending=False).head(10)

# fullVisitorId
# 9417857471295131045 4
# 0376394056092189113 2
# 0456807427403774085 2
# 057693500927581077 2
# 112288330928895942 2
# 1280993661204347450 2
# 2105122376016897629 2
# 2158257269735455737 2
# 2969418676126258798 2
# 489038402765684003 2
# Name: predicted_label, dtype: Int64


# [END bigquery_dataframes_bqml_getting_started_tutorial_predict]
# [END bigquery_dataframes_bqml_getting_started_tutorial_predict_by_visitor]

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