-
Notifications
You must be signed in to change notification settings - Fork 50
docs: add a code sample for creating a kmeans model #267
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Merged
Changes from all commits
Commits
Show all changes
40 commits
Select commit
Hold shift + click to select a range
69fe5d7
k-means code sample
5fb1d4f
formatting
523255f
added test
3bb267a
docs: add code sampke for creating kmeans model
73d2a46
🦉 Updates from OwlBot post-processor
gcf-owl-bot[bot] b3c0578
license header + region tags added
c25aeb5
Resolved conflicts
db9f439
Update samples/snippets/create_kmeans_model_test.py
SalemJorden e7bd5ef
code corrections resolved
2a7d575
resolved merge conflicts
809ed05
code corrections commit 1
5dba2b9
descriptions of geospatial analysis functions
2207941
explantions revised for clarity
5e00a3c
🦉 Updates from OwlBot post-processor
gcf-owl-bot[bot] 7c64227
Update samples/snippets/create_kmeans_model_test.py
SalemJorden 11678e0
code corrections
f95cd9f
code revision
0df2dec
code changes
06a2490
revisions
1a9f7d9
expected output previews
464cf1c
revisions
d03f46c
Merge remote-tracking branch 'origin/main' into Salem
019e243
tests passing, expected output characters >80
72174f9
🦉 Updates from OwlBot post-processor
gcf-owl-bot[bot] 50a447d
Merge branch 'main' into Salem
tswast ac348bf
column wrapping
7ce5337
merge
29b2e1f
Merge branch 'main' into Salem
SalemJorden 7762f0f
Merge branch 'main' into Salem
SalemJorden 479a828
Merge branch 'main' into Salem
SalemJorden 1572ddd
reset session before running code smaples
SalemJorden 3d77ddd
Update samples/snippets/create_kmeans_model_test.py
SalemJorden 505b790
predict function added to tutorial
SalemJorden 4505c5c
replaced project_id with model_id
SalemJorden cad2185
Merge branch 'main' into Salem
SalemJorden 3ab8220
reformatting
SalemJorden 816881c
Merge branch 'Salem' of https://github.com/googleapis/python-bigquery…
SalemJorden 9b382d6
Merge branch 'main' into Salem
tswast ae9a362
reformat
SalemJorden 5eb59ec
Merge branch 'main' into Salem
tswast File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,152 @@ | ||
# 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_kmeans_sample(project_id: str, random_model_id_eu: str): | ||
your_gcp_project_id = project_id | ||
your_model_id = random_model_id_eu | ||
# [START bigquery_dataframes_bqml_kmeans] | ||
import datetime | ||
SalemJorden marked this conversation as resolved.
Show resolved
Hide resolved
|
||
|
||
import bigframes | ||
import bigframes.pandas as bpd | ||
SalemJorden marked this conversation as resolved.
Show resolved
Hide resolved
|
||
|
||
bigframes.options.bigquery.project = your_gcp_project_id | ||
# Compute in the EU multi-region to query the London bicycles dataset. | ||
bigframes.options.bigquery.location = "EU" | ||
|
||
# Extract the information you'll need to train the k-means model in this | ||
# tutorial. Use the read_gbq function to represent cycle hires | ||
# data as a DataFrame. | ||
h = bpd.read_gbq( | ||
"bigquery-public-data.london_bicycles.cycle_hire", | ||
col_order=["start_station_name", "start_station_id", "start_date", "duration"], | ||
).rename( | ||
columns={ | ||
"start_station_name": "station_name", | ||
"start_station_id": "station_id", | ||
} | ||
) | ||
|
||
s = bpd.read_gbq( | ||
tswast marked this conversation as resolved.
Show resolved
Hide resolved
|
||
# Use ST_GEOPOINT and ST_DISTANCE to analyze geographical | ||
# data. These functions determine spatial relationships between | ||
# geographical features. | ||
""" | ||
SELECT | ||
id, | ||
ST_DISTANCE( | ||
ST_GEOGPOINT(s.longitude, s.latitude), | ||
ST_GEOGPOINT(-0.1, 51.5) | ||
) / 1000 AS distance_from_city_center | ||
FROM | ||
`bigquery-public-data.london_bicycles.cycle_stations` s | ||
""" | ||
) | ||
|
||
# Define Python datetime objects in the UTC timezone for range comparison, | ||
# because BigQuery stores timestamp data in the UTC timezone. | ||
sample_time = datetime.datetime(2015, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc) | ||
sample_time2 = datetime.datetime(2016, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc) | ||
|
||
h = h.loc[(h["start_date"] >= sample_time) & (h["start_date"] <= sample_time2)] | ||
|
||
# Replace each day-of-the-week number with the corresponding "weekday" or | ||
# "weekend" label by using the Series.map method. | ||
h = h.assign( | ||
isweekday=h.start_date.dt.dayofweek.map( | ||
{ | ||
0: "weekday", | ||
1: "weekday", | ||
2: "weekday", | ||
3: "weekday", | ||
4: "weekday", | ||
5: "weekend", | ||
6: "weekend", | ||
} | ||
) | ||
) | ||
|
||
# Supplement each trip in "h" with the station distance information from | ||
# "s" by merging the two DataFrames by station ID. | ||
merged_df = h.merge( | ||
right=s, | ||
how="inner", | ||
left_on="station_id", | ||
right_on="id", | ||
) | ||
|
||
# Engineer features to cluster the stations. For each station, find the | ||
# average trip duration, number of trips, and distance from city center. | ||
stationstats = merged_df.groupby(["station_name", "isweekday"]).agg( | ||
{"duration": ["mean", "count"], "distance_from_city_center": "max"} | ||
) | ||
stationstats.columns = ["duration", "num_trips", "distance_from_city_center"] | ||
stationstats = stationstats.sort_values( | ||
by="distance_from_city_center", ascending=True | ||
).reset_index() | ||
|
||
# Expected output results: >>> stationstats.head(3) | ||
# station_name isweekday duration num_trips distance_from_city_center | ||
# Borough Road... weekday 1110 5749 0.12624 | ||
# Borough Road... weekend 2125 1774 0.12624 | ||
# Webber Street... weekday 795 6517 0.164021 | ||
# 3 rows × 5 columns | ||
|
||
# [END bigquery_dataframes_bqml_kmeans] | ||
tswast marked this conversation as resolved.
Show resolved
Hide resolved
|
||
|
||
# [START bigquery_dataframes_bqml_kmeans_fit] | ||
|
||
from bigframes.ml.cluster import KMeans | ||
|
||
# To determine an optimal number of clusters, construct and fit several | ||
# K-Means objects with different values of num_clusters, find the error | ||
# measure, and pick the point at which the error measure is at its minimum | ||
# value. | ||
cluster_model = KMeans(n_clusters=4) | ||
tswast marked this conversation as resolved.
Show resolved
Hide resolved
|
||
cluster_model.fit(stationstats) | ||
cluster_model.to_gbq( | ||
your_model_id, # For example: "bqml_tutorial.london_station_clusters" | ||
replace=True, | ||
) | ||
# [END bigquery_dataframes_bqml_kmeans_fit] | ||
|
||
# [START bigquery_dataframes_bqml_kmeans_predict] | ||
|
||
tswast marked this conversation as resolved.
Show resolved
Hide resolved
|
||
# Select model you'll use for predictions. `read_gbq_model` loads model | ||
# data from BigQuery, but you could also use the `cluster_model` object | ||
# from previous steps. | ||
cluster_model = bpd.read_gbq_model( | ||
your_model_id, | ||
# For example: "bqml_tutorial.london_station_clusters", | ||
) | ||
|
||
# Use 'contains' function to filter by stations containing the string | ||
# "Kennington". | ||
stationstats = stationstats.loc[ | ||
stationstats["station_name"].str.contains("Kennington") | ||
] | ||
|
||
result = cluster_model.predict(stationstats) | ||
|
||
tswast marked this conversation as resolved.
Show resolved
Hide resolved
|
||
# Expected output results: >>>results.peek(3) | ||
# CENTROID... NEAREST... station_name isweekday duration num_trips dist... | ||
# 1 [{'CENTROID_ID'... Borough... weekday 1110 5749 0.13 | ||
# 2 [{'CENTROID_ID'... Borough... weekend 2125 1774 0.13 | ||
# 1 [{'CENTROID_ID'... Webber... weekday 795 6517 0.16 | ||
# 3 rows × 7 columns | ||
|
||
# [END bigquery_dataframes_bqml_kmeans_predict] | ||
|
||
assert result is not None |
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Uh oh!
There was an error while loading. Please reload this page.