scikit-learn

# For interactive plots
from bokeh.plotting import figure, show, output_notebook
from bokeh.models import TeX
output_notebook()

加载 BokehJS ...

scikit-learn 项目开发的快照。

问题

query_date = np.datetime64("2020-01-01 00:00:00")

# Load data
with open("devstats-data/scikit-learn_issues.json", "r") as fh:
    issues = [item["node"] for item in json.loads(fh.read())]

glue("devstats-data/scikit-learn_query_date", str(query_date.astype("M8[D]")))

新问题

显示代码单元格源代码 隐藏代码单元格源代码

newly_created = [
    iss for iss in issues if np.datetime64(iss["createdAt"]) > query_date
]
new_issues_closed = [iss for iss in newly_created if iss["state"] == "CLOSED"]

new_issue_lifetime = np.array(
    [
        np.datetime64(iss["closedAt"]) - np.datetime64(iss["createdAt"])
        for iss in new_issues_closed
    ],
).astype("m8[h]")  # in hours

glue("scikit-learn_num_new_issues", len(newly_created))
glue("scikit-learn_num_new_issues_closed", percent_val(len(new_issues_closed), len(newly_created)))
glue("scikit-learn_new_issue_median_lifetime", f"{np.median(new_issue_lifetime)}")

2169 个新问题已自 2020-01-01 起被打开，其中 1478 (68%) 已关闭。

在此期间创建并关闭的新问题的平均生命周期为 69 小时。

query_date = np.datetime64("2020-01-01 00:00:00")

# Load data
with open("devstats-data/scikit-learn_issues.json", "r") as fh:
    issues = [item["node"] for item in json.loads(fh.read())]

glue("scikit-learn_query_date", str(query_date.astype("M8[D]")))

显示代码单元格源代码 隐藏代码单元格源代码

title = (
    f"Lifetime of issues created and closed in the last "
    f"{(np.datetime64(datetime.datetime.now()) - query_date).astype('m8[D]')}"
)
h, bedges = np.histogram(
    new_issue_lifetime.astype("m8[D]").astype(int), bins=np.arange(30)
)

p = figure(
    width=670,
    height=400,
    title=title,
    tooltips=[("lifetime", "@right days"), (r"# issues", "@top")],
)
p.quad(top=h, bottom=0, left=bedges[:-1], right=bedges[1:])
p.xaxis.axis_label = "Issue lifetime (days)"
p.yaxis.axis_label = "# Issues"
show(p)

响应时间

显示代码单元格源代码 隐藏代码单元格源代码

# Remove issues that are less than a day old for the following analysis
newly_created_day_old = [
    iss for iss in newly_created
    if np.datetime64(datetime.datetime.now()) - np.datetime64(iss["createdAt"])
    > np.timedelta64(1, "D")
]

# TODO: really need pandas here
commented_issues = [
    iss for iss in newly_created_day_old
    if any(
        e["node"]["__typename"] == "IssueComment" for e in iss["timelineItems"]["edges"]
    )
]
first_commenters, time_to_first_comment = [], []
for iss in commented_issues:
    for e in iss["timelineItems"]["edges"]:
        if e["node"]["__typename"] == "IssueComment":
            try:
                user = e["node"]["author"]["login"]
            except TypeError as err:
                # This can happen e.g. when a user deletes their GH acct
                user = "UNKNOWN"
            first_commenters.append(user)
            dt = np.datetime64(e["node"]["createdAt"]) - np.datetime64(iss["createdAt"])
            time_to_first_comment.append(dt.astype("m8[m]"))
            break  # Only want the first commenter
time_to_first_comment = np.array(time_to_first_comment)  # in minutes

median_time_til_first_response = np.median(time_to_first_comment.astype(int) / 60)

cutoffs = [
    np.timedelta64(1, "h"),
    np.timedelta64(12, "h"),
    np.timedelta64(24, "h"),
    np.timedelta64(3, "D"),
    np.timedelta64(7, "D"),
    np.timedelta64(14, "D"),
]
num_issues_commented_by_cutoff = np.array(
    [
        np.sum(time_to_first_comment < cutoff) for cutoff in cutoffs
    ]
)

# TODO: Update IssueComment query to include:
#  - whether the commenter is a maintainer
#  - datetime of comment
# This will allow analysis of what fraction of issues are addressed by
# maintainers vs. non-maintainer, and the distribution of how long an issue
# usually sits before it's at least commented on

glue(
    "scikit-learn_num_new_issues_responded",
    percent_val(len(commented_issues), len(newly_created_day_old))
)

glue("scikit-learn_new_issues_at_least_1_day_old", len(newly_created_day_old))
glue("scikit-learn_median_response_time", f"{median_time_til_first_response:1.0f}")

在至少 24 小时龄的 2169 个问题中，有 1980 (91%) 个问题已被评论。问题首次得到回复的平均时间为 6 小时。

显示代码单元格源代码 隐藏代码单元格源代码

from bokeh.transform import dodge

title = f"Percentage of issues opened since {query_date} that are commented on within..."

x = [str(c) for c in cutoffs]
y = 100 * num_issues_commented_by_cutoff / len(newly_created_day_old)

p = figure(
    x_range=x,
    y_range=(0, 100),
    width=670,
    height=400,
    title=title,
    tooltips=[(r"%", "@top")],
)
p.vbar(x=x, top=y, width=0.8)
p.xaxis.axis_label = "Time interval"
p.yaxis.axis_label = "Percentage of issues commented on within interval"
show(p)

首个回复者

显示代码单元格源代码 隐藏代码单元格源代码

first_commenter_tab = pd.DataFrame(
    {
        k: v
        for k, v in zip(
            ("Contributor", "# of times commented first"),
            np.unique(first_commenters, return_counts=True),
        )
    }
)
first_commenter_tab.sort_values(
    "# of times commented first", ascending=False
).head(10)

	贡献者	首次评论次数
204	glemaitre	381
78	NicolasHug	200
240	jnothman	186
362	thomasjpfan	155
331	rth	115
304	ogrisel	98
123	adrinjalali	69
169	cmarmo	63
104	TomDLT	51
131	alfaro96	51

拉取请求

显示代码单元格源代码 隐藏代码单元格源代码

with open("devstats-data/scikit-learn_prs.json", "r") as fh:
    prs = [item["node"] for item in json.loads(fh.read())]

# Filters

# The following filters are applied to the PRs for the following analysis:
#
# - Only PRs to the default development branch (e.g `main`)[^master_to_main]
#  are considered.
# - Only PRs from users with _active_ GitHub accounts are considered. For example,
#   if a user opened a Pull Request in 2016, but then deleted their GitHub account
#   in 2017, then this PR is excluded from the analysis.
# - PRs opened by dependabot are excluded.

# Only look at PRs to the main development branch - ignore backports,
# gh-pages, etc.
default_branches = {"main", "master"}  # Account for default branch update
prs = [pr for pr in prs if pr["baseRefName"] in default_branches]

# Drop data where PR author is unknown (e.g. github account no longer exists)
prs = [pr for pr in prs if pr["author"]]  # Failed author query results in None

# Filter out PRs by bots
bot_filter = {
  "dependabot-preview",
  "github-actions",
  "meeseeksmachine",
  "pre-commit-ci[bot]"
}
prs = [pr for pr in prs if pr["author"]["login"] not in bot_filter]

随时间推移的已合并 PR

查看随时间推移的已合并 PR。

显示代码单元格源代码 隐藏代码单元格源代码

# All contributors
merged_prs = [pr for pr in prs if pr['state'] == 'MERGED']
merge_dates = np.array([pr['mergedAt'] for pr in merged_prs], dtype=np.datetime64)
binsize = np.timedelta64(30, "D")
date_bins = np.arange(merge_dates[0], merge_dates[-1], binsize)
h_all, bedges = np.histogram(merge_dates, date_bins)
bcenters = bedges[:-1] + binsize / 2
smoothing_interval = 4  # in units of bin-width

# First-time contributors
first_time_contributor = []
prev_contrib = set()
for record in merged_prs:
    try:
        author = record['author']['login']
    except TypeError:  # Author no longer has GitHub account
        first_time_contributor.append(None)
        continue
    if author not in prev_contrib:
        first_time_contributor.append(True)
        prev_contrib.add(author)
    else:
        first_time_contributor.append(False)
# Object dtype for handling None
first_time_contributor = np.array(first_time_contributor, dtype=object)
# Focus on first time contributors
ftc_mask = first_time_contributor == True
ftc_dates = merge_dates[ftc_mask]

h_ftc, bedges = np.histogram(ftc_dates, date_bins)

fig, axes = plt.subplots(1, 2, figsize=(16, 8))
for ax, h, whom in zip(
    axes.ravel(), (h_all, h_ftc), ("all contributors", "first-time contributors")
):
    ax.bar(bcenters, h, width=binsize, label="Raw")
    ax.plot(
        bcenters,
        np.convolve(h, np.ones(smoothing_interval), 'same') / smoothing_interval,
        label=f"{binsize * smoothing_interval} moving average",
        color='tab:orange',
        linewidth=2.0,
    )

    ax.set_title(f'{whom}')
    ax.legend()

fig.suptitle("Merged PRs from:")
axes[0].set_xlabel('Time')
axes[0].set_ylabel(f'# Merged PRs / {binsize} interval')
axes[1].set_ylim(axes[0].get_ylim())
fig.autofmt_xdate()

# TODO: Replace this with `glue` once the glue:figure directive supports
# alt-text
import os
os.makedirs("thumbs", exist_ok=True)
plt.savefig("thumbs/scikit-learn.png", bbox_inches="tight")

PR 生命周期

下图显示了 PR 随时间的“存活率”。这意味着，该图显示了有多少 PR 至少开放了这么多天。这被分为已合并的 PR 和仍处于打开状态的 PR（当前不包含已关闭但未合并的 PR）。

显示代码单元格源代码 隐藏代码单元格源代码

merged_prs = [pr for pr in prs if pr['state'] == 'MERGED']
lifetimes_merged = np.array(
    [isoparse(pr["mergedAt"]) - isoparse(pr["createdAt"]) for pr in merged_prs],
    dtype="m8[m]").view("int64") / (60 * 24)  # days
lifetimes_merged.sort()

#closed_prs = [pr for pr in prs if pr['state'] == 'CLOSED']
#lifetimes_closed = np.array(
#    [isoparse(pr["mergedAt"]) - isoparse(pr["createdAt"]) for pr in closed_prs],
#    dtype="m8[m]").view("int64") / (60 * 24)  # days
#lifetimes_closed.sort()


# Use the newest issue to guess a time when the data was generated.
# Can this logic be improved?
current_time = isoparse(max(iss["createdAt"] for iss in issues))

open_prs = [pr for pr in prs if pr['state'] == 'OPEN']
age_open = np.array(
    [current_time - isoparse(pr["createdAt"]) for pr in open_prs],
    dtype="m8[m]").view("int64") / (60 * 24)  # days
age_open.sort()

fig, ax = plt.subplots(figsize=(6, 4))
number_merged = np.arange(1, len(lifetimes_merged)+1)[::-1]
ax.step(lifetimes_merged, number_merged, label="Merged")

#ax.step(lifetimes_closed, np.arange(1, len(lifetimes_closed)+1)[::-1])

number_open = np.arange(1, len(age_open)+1)[::-1]
ax.step(age_open, number_open, label="Open")

# Find the first point where closed have a bigger survival than open PRs:
all_lifetimes = np.concatenate([lifetimes_merged, age_open])
all_lifetimes.sort()

number_merged_all_t = np.interp(all_lifetimes, lifetimes_merged, number_merged)
number_open_all_t = np.interp(all_lifetimes, age_open, number_open)

first_idx = np.argmax(number_merged_all_t < number_open_all_t)
first_time = all_lifetimes[first_idx]

ax.vlines(
        [first_time], 0, 1, transform=ax.get_xaxis_transform(), colors='k',
        zorder=0, linestyle="--")

ax.annotate(
    f"{round(first_time)} days",
    xy=(first_time, number_open_all_t[first_idx]),
    xytext=(5, 5), textcoords="offset points",
    va="bottom", ha="left")

ax.legend()
ax.set_xlabel("Time until merged or time open [days]")
ax.set_ylabel(r"# of PRs open this long or longer")
ax.set_xscale("log")
fig.autofmt_xdate()
fig.tight_layout();

打开 PR 的可合并性

显示代码单元格源代码 隐藏代码单元格源代码

open_prs = [pr for pr in prs if pr["state"] == "OPEN"]

# The GraphQL query does not reliably return information on PR mergeability.
# Warn if there are problems
if any([pr["mergeable"] == "UNKNOWN" for pr in open_prs]):
    warnings.warn(
        (
            "\n\nThe data contains PRs with unknown merge status.\n"
            "Please re-download the data to get accurate info about PR mergeability."
        ),
        UserWarning,
        stacklevel=2,
    )

conflicting_prs = [isoparse(pr["createdAt"]) for pr in open_prs if pr["mergeable"] == "CONFLICTING"]
mergeable_prs = [isoparse(pr["createdAt"]) for pr in open_prs if pr["mergeable"] == "MERGEABLE"]

fig, ax = plt.subplots(figsize=(6, 4))
ax.hist(
    [conflicting_prs, mergeable_prs],
    bins="auto",
    histtype="bar",
    label=("conflicting", "mergeable"),
    color=("tab:red", "tab:blue"),
)
ax.legend()
ax.set_xlabel("Date of PR creation")
ax.set_ylabel(r"# of conflicting PRs")
fig.autofmt_xdate()
fig.tight_layout();

/opt/buildhome/python3.8/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3508: UserWarning: 

The data contains PRs with unknown merge status.
Please re-download the data to get accurate info about PR mergeability.
  exec(code_obj, self.user_global_ns, self.user_ns)

PR 参与者数量

显示代码单元格源代码 隐藏代码单元格源代码

# Get the lifetimes and number of participants for merged PRs
lifetimes = np.array(
    [isoparse(pr["mergedAt"]) - isoparse(pr["createdAt"]) for pr in merged_prs],
    dtype="m8[h]",
)
num_participants = np.array([pr["participants"]["totalCount"] for pr in merged_prs])

title = "Distribution of lifetimes for merged PRs based on the number of participants"

p = figure(width=600, height=300, title=title)
p.xgrid.grid_line_color = None
p.xaxis.ticker = sorted(np.unique(num_participants))
p.yaxis.axis_label = "PR lifetime (hours)"
p.scatter(x=num_participants, y=lifetimes.astype(int), size=9, alpha=0.4)
show(p)

贡献来源

总共有 8337 个已合并的 PR[1] 由 2146 位唯一作者提交。其中 1413 (66%) 为“飞过”PR，即来自仅为项目贡献过一次（至今）的用户提交的 PR。

显示代码单元格源代码 隐藏代码单元格源代码

# Remap PRs by author
contributions_by_author = defaultdict(list)
for pr in merged_prs:
    author = pr["author"]["login"]
    contributions_by_author[author].append(pr)

num_merged_prs_per_author = np.array(
    [len(prs) for prs in contributions_by_author.values()]
)

num_flybys = np.sum(num_merged_prs_per_author == 1)

glue("scikit-learn_num_merged_prs_with_known_authors", len(merged_prs))
glue("scikit-learn_num_unique_authors_of_merged_prs", len(contributions_by_author))
glue("scikit-learn_num_flyby", percent_val(num_flybys, len(num_merged_prs_per_author)))

显示代码单元格源代码 隐藏代码单元格源代码

title = "Distribution of number of merged PRs per contributor"

x = ["1", "2", "3", "4", "5", "6 - 10", "10 - 20", "20 - 50", "> 50"]
bedges = np.array([0, 1, 2, 3, 4, 5, 10, 20, 50, sum(num_merged_prs_per_author)]) + 0.5
y, _ = np.histogram(num_merged_prs_per_author, bins=bedges)

p = figure(
    x_range=x,
    y_range=(0, 1.05 * y.max()),
    width=670,
    height=400,
    title=title,
    tooltips=[(r"# PRs merged", "@x"), ("# contributors", f"@top")],
)
p.vbar(x=x, top=y, width=0.8)
p.xaxis.axis_label = "# Merged PRs per user"
p.yaxis.axis_label = "# of unique contributors with N PRs merged"
show(p)

小马因子

查看这些数据的另一种方法是根据小马因子，其定义为

贡献总量构成多数贡献的贡献者人数的最小值。

对于此分析，我们将考虑已合并的 PR 作为贡献指标。考虑到项目整个生命周期中所有已合并的 PR，小马因子为：37。

显示代码单元格源代码 隐藏代码单元格源代码

# Sort by number of merged PRs in descending order
num_merged_prs_per_author.sort()
num_merged_prs_per_author = num_merged_prs_per_author[::-1]

num_merged_prs = num_merged_prs_per_author.sum()
pf_thresh = 0.5
pony_factor = np.searchsorted(
    np.cumsum(num_merged_prs_per_author), num_merged_prs * pf_thresh
) + 1

fig, ax = plt.subplots()
ax.plot(
    np.arange(len(num_merged_prs_per_author)) + 1,
    np.cumsum(num_merged_prs_per_author),
    "."
)
ax.set_title(f"How the pony factor is calculated")
ax.set_xlabel("# unique contributors")
ax.set_xscale("log")
ax.set_ylabel("Cumulative sum of merged PRs / contributor")
ax.hlines(
    xmin=0,
    xmax=len(contributions_by_author),
    y=num_merged_prs * pf_thresh,
    color="tab:green",
    label=f"Pony factor threshold = {100 * pf_thresh:1.0f}%",
)
ax.legend();

glue("scikit-learn_pony_factor", pony_factor)

---

[1]

这仅包括来自具有活动 GitHub 帐户的用户的 PR。