יצירת מודל k-means לאשכול נתונים של השכרת אופניים בלונדון

import datetime
import typing

import pandas as pd
from shapely.geometry import Point

import bigframes
import bigframes.bigquery as bbq
import bigframes.geopandas
import bigframes.pandas as bpd

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"

# Set partial ordering mode for BigQuery DataFrames.
# For more information, see the BigQuery DataFrames performance documentation:
# https://cloud.google.com/bigquery/docs/dataframes-performance#partial-ordering-mode
bpd.options.bigquery.ordering_mode = "partial"

# 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",
    }
)

# Use GeoSeries.from_xy and BigQuery.st_distance to analyze geographical
# data. These functions determine spatial relationships between
# geographical features.
cycle_stations = bpd.read_gbq("bigquery-public-data.london_bicycles.cycle_stations")
s = bpd.DataFrame(
    {
        "id": cycle_stations["id"],
        "xy": bigframes.geopandas.GeoSeries.from_xy(
            cycle_stations["longitude"], cycle_stations["latitude"]
        ),
    }
)
s_distance = bbq.st_distance(s["xy"], Point(-0.1, 51.5), use_spheroid=False) / 1000
s = bpd.DataFrame({"id": s["id"], "distance_from_city_center": s_distance})

# 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[(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.case_when method.
dayofweek = h["start_date"].dt.dayofweek
h = h.assign(
    isweekday=dayofweek.case_when(
        [
            (dayofweek.isin([5, 6]), "weekend"),
            (True, "weekday"),
        ]
    )
)

# 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 = typing.cast(
    bpd.DataFrame,
    merged_df.groupby(["station_name", "isweekday"]).agg(
        {"duration": ["mean", "count"], "distance_from_city_center": "max"}
    ),
)
stationstats.columns = pd.Index(
    ["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

from bigframes.bigquery import ml

# A k-means model groups data into clusters, which is useful for
# descriptive analytics.
#
# Extract only the numerical feature columns for model training.
# 'station_name' and 'isweekday' are excluded so clustering is based on
# bicycle usage patterns rather than station identity or day of week.
features = stationstats[["duration", "num_trips", "distance_from_city_center"]]

# Use ml.create_model to create and train the model in BigQuery.
# The options parameter specifies the model type and the number of clusters.
# For more information, see the BigQuery DataFrames API reference documentation:
# https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ml.create_model.html#bigframes.bigquery.ml.create_model
ml.create_model(
    your_model_id,  # For example: "bqml_tutorial.london_station_clusters",
    options={
        "model_type": "KMEANS",
        "num_clusters": 4,
    },
    training_data=features,
    replace=True,
)

from bigframes.bigquery import ml

# Use 'contains' function to filter by stations containing the string
# "Kennington".
stationstats = stationstats[stationstats["station_name"].str.contains("Kennington")]

# Use the ml.predict method to predict results using your model.
# For more information, see the BigQuery DataFrames API reference documentation:
# https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ml.predict.html#bigframes.bigquery.ml.predict
ml.predict(
    your_model_id,  # For example: "bqml_tutorial.london_station_clusters",
    input_=stationstats,
)

# Expected output results:
# 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