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bytes_per_category_per_user.py
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""" Bytes per category per user
"""
import altair as alt
import pandas as pd
import infra.dask
import infra.pd
import infra.platform
def reduce_to_pandas(outfile, dask_client):
flows = infra.dask.read_parquet(
"data/clean/flows/typical_fqdn_org_category_local_TM_DIV_none_INDEX_start")[["user", "category", "org", "bytes_up", "bytes_down"]]
# Do the grouping
flows = flows.groupby(["user", "category", "org"]).sum()
flows = flows.compute()
infra.pd.clean_write_parquet(flows, outfile)
def make_category_per_user_plots(infile):
grouped_flows = infra.pd.read_parquet(infile)
grouped_flows = grouped_flows.reset_index()
grouped_flows["bytes_total"] = grouped_flows["bytes_up"] + grouped_flows["bytes_down"]
user_category_total = grouped_flows[["user", "category", "bytes_total"]].groupby(
["user", "category"]
).sum().reset_index()
# Filter users by time in network to eliminate early incomplete samples
user_active_ranges = infra.pd.read_parquet(
"data/clean/user_active_deltas.parquet")[["user", "days_since_first_active", "days_active", "days_online"]]
# Drop users that joined less than a week ago.
users_to_analyze = user_active_ranges.loc[
user_active_ranges["days_since_first_active"] > 7
]
# Drop users active for less than one day
users_to_analyze = users_to_analyze.loc[
users_to_analyze["days_active"] > 1,
]
# Sort categories by total amount of bytes.
cat_totals = grouped_flows.groupby("category").sum().reset_index()
cat_sort_order = cat_totals.sort_values("bytes_total", ascending=False).set_index("bytes_total").reset_index()
cat_sort_order["cat_rank"] = cat_sort_order["bytes_total"].rank(method="min", ascending=False)
cat_sort_list = cat_sort_order["category"].tolist()
# Rank users by their daily use.
user_totals = user_category_total.groupby("user").sum().reset_index()
user_totals = user_totals.merge(users_to_analyze, on="user", how="inner")
user_totals["user_total_bytes_avg_online_day"] = user_totals["bytes_total"] / user_totals["days_online"]
user_totals["user_rank"] = user_totals["user_total_bytes_avg_online_day"].rank(method="min")
user_category_total = user_category_total.merge(
user_totals[["user", "user_rank", "days_online", "user_total_bytes_avg_online_day"]],
on="user",
how="inner"
)
user_category_total = user_category_total.merge(cat_sort_order[["category", "cat_rank"]], on="category", how="inner")
print(user_category_total)
user_category_total["bytes_avg_online_day"] = user_category_total["bytes_total"] / user_category_total["days_online"]
user_category_total["share_of_bytes_avg_online_day"] = \
user_category_total["bytes_avg_online_day"] / user_category_total["user_total_bytes_avg_online_day"]
print(user_category_total)
# This might not be showing exactly what I want to show, since in merging
# users some users that dominate video could be overrepresented. Maybe
# want to merge on the fraction of traffic to each part from each user?
# Are users counted equally or are bytes counted equally...
alt.Chart(user_category_total[["category", "user_rank", "cat_rank", "bytes_avg_online_day"]]).mark_bar().encode(
x="user_rank:O",
y=alt.Y(
"bytes_avg_online_day",
stack="normalize",
sort=cat_sort_list,
),
color=alt.Color(
"category:N",
scale=alt.Scale(scheme="tableau20"),
sort=cat_sort_list,
),
order=alt.Order(
"cat_rank",
sort="descending",
),
).properties(
width=500,
).save(
"renders/bytes_per_average_online_day_per_user_bar.png",
scale_factor=2,
)
alt.Chart(user_category_total[["category", "user_rank", "cat_rank", "bytes_avg_online_day"]]).mark_point(
size=10,
strokeWidth=2,
).encode(
x="user_rank:O",
y=alt.Y(
"bytes_avg_online_day",
sort=cat_sort_list,
title="average bytes per online day"
),
color=alt.Color(
"category:N",
scale=alt.Scale(scheme="tableau20"),
sort=cat_sort_list,
),
order=alt.Order(
"cat_rank",
sort="descending",
),
).properties(
width=500,
).save(
"renders/bytes_per_average_online_day_per_user_points.png",
scale_factor=2,
)
alt.Chart(user_category_total[["category", "user_rank", "cat_rank", "share_of_bytes_avg_online_day"]]).mark_point(
size=10,
strokeWidth=2,
).encode(
x="user_rank:O",
y=alt.Y(
"share_of_bytes_avg_online_day",
sort=cat_sort_list,
title="share of average bytes per online day"
),
color=alt.Color(
"category:N",
scale=alt.Scale(scheme="tableau20"),
sort=cat_sort_list,
),
order=alt.Order(
"cat_rank",
sort="descending",
),
).properties(
width=500,
).save(
"renders/share_of_bytes_per_average_online_day_per_user_points.png",
scale_factor=2,
)
if __name__ == "__main__":
platform = infra.platform.read_config()
graph_temporary_file = "scratch/graphs/bytes_per_category_per_user"
if platform.large_compute_support:
print("Running compute tasks")
client = infra.dask.setup_platform_tuned_dask_client(10, platform)
reduce_to_pandas(outfile=graph_temporary_file, dask_client=client)
client.close()
if platform.altair_support:
print("Running vis tasks")
pd.set_option('display.max_columns', None)
make_category_per_user_plots(graph_temporary_file)
print("Done!")