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stats: interpolate, remove some broken stats

This commit is contained in:
Jacek Sieka 2024-06-29 06:36:35 +02:00
parent b751d3adee
commit 55ebd70d1e
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1 changed files with 23 additions and 17 deletions
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40
scripts/block-import-stats.py
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@ -42,7 +42,10 @@ def prettySecs(s: float):
def formatBins(df: pd.DataFrame, bins: int): def formatBins(df: pd.DataFrame, bins: int):
if bins > 0: if bins > 0:
bins = np.linspace( bins = np.linspace(
df.block_number.iloc[0] - df.blocks.iloc[0], df.block_number.iloc[-1], bins, dtype=int df.block_number.iloc[0] - df.blocks.iloc[0],
df.block_number.iloc[-1],
bins,
dtype=int,
) )
return df.groupby(pd.cut(df["block_number"], bins), observed=True) return df.groupby(pd.cut(df["block_number"], bins), observed=True)
else: else:
@ -71,9 +74,16 @@ min_block_number = args.min_block_number
baseline = readStats(args.baseline) baseline = readStats(args.baseline)
contender = readStats(args.contender) contender = readStats(args.contender)
# Pick out the rows to match - a more sophisticated version of this would start = max(min(baseline.index), min(contender.index))
# interpolate, perhaps - also, maybe should check for non-matching block/tx counts end = min(max(baseline.index), max(contender.index))
df = baseline.merge(contender, on=("block_number", "blocks", "txs"))
baseline = baseline.loc[baseline.index >= start and baseline.index <= end]
contender = contender.loc[contender.index >= start and contender.index <= end]
# Join the two frames then interpolate - this helps dealing with runs that
# haven't been using the same chunking and/or max-blocks
df = baseline.merge(contender, on=("block_number", "blocks"), how="outer")
df = df.interpolate(method="index").reindex(contender.index)
df.reset_index(inplace=True) df.reset_index(inplace=True)
if df.block_number.iloc[-1] > min_block_number + df.block_number.iloc[0]: if df.block_number.iloc[-1] > min_block_number + df.block_number.iloc[0]:
@ -83,11 +93,10 @@ if df.block_number.iloc[-1] > min_block_number + df.block_number.iloc[0]:
) )
df = df[df.block_number >= cutoff] df = df[df.block_number >= cutoff]
df["bpsd"] = ((df.bps_y - df.bps_x) / df.bps_x) df["bpsd"] = (df.bps_y - df.bps_x) / df.bps_x
df["tpsd"] = ((df.tps_y - df.tps_x) / df.tps_x.replace(0, 1)) df["tpsd"] = (df.tps_y - df.tps_x) / df.tps_x.replace(0, 1)
df["timed"] = (df.time_y - df.time_x) / df.time_x df["timed"] = (df.time_y - df.time_x) / df.time_x
if args.plot: if args.plot:
plt.rcParams["axes.grid"] = True plt.rcParams["axes.grid"] = True
@ -118,23 +127,20 @@ print(f"{os.path.basename(args.baseline)} vs {os.path.basename(args.contender)}"
print( print(
formatBins(df, args.bins) formatBins(df, args.bins)
.agg( .agg(
dict.fromkeys( dict.fromkeys(["bps_x", "bps_y", "tps_x", "tps_y"], "mean")
["bps_x", "bps_y", "tps_x", "tps_y", "bpsd", "tpsd", "timed"], "mean" | dict.fromkeys(["time_x", "time_y"], "sum")
), | dict.fromkeys(["bpsd", "tpsd", "timed"], "mean")
) )
.to_string( .to_string(
formatters=dict( formatters=dict.fromkeys(["bpsd", "tpsd", "timed"], "{:,.2%}".format)
dict.fromkeys(["bpsd", "tpsd", "timed"], "{:,.2%}".format), | dict.fromkeys(["bps_x", "bps_y", "tps_x", "tps_y"], "{:,.2f}".format)
**dict.fromkeys(["bps_x", "bps_y", "tps_x", "tps_y"], "{:,.2f}".format), | dict.fromkeys(["time_x", "time_y"], prettySecs),
)
) )
) )
print( print(
f"\nblocks: {df.blocks.sum()}, baseline: {prettySecs(df.time_x.sum())}, contender: {prettySecs(df.time_y.sum())}" f"\nblocks: {df.block_number.max() - df.block_number.min()}, baseline: {prettySecs(df.time_x.sum())}, contender: {prettySecs(df.time_y.sum())}"
) )
print(f"bpsd (mean): {df.bpsd.mean():.2%}")
print(f"tpsd (mean): {df.tpsd.mean():.2%}")
time_xt = df.time_x.sum() time_xt = df.time_x.sum()
time_yt = df.time_y.sum() time_yt = df.time_y.sum()