module benchmark.mlprediction
¶
Short summary¶
module jupytalk.benchmark.mlprediction
Functions¶
function |
truncated documentation |
---|---|
Builds a dataframe from multiple arrays. |
|
Measures the time for a given expression. |
|
Optimizes the rendering of time. |
Documentation¶
- jupytalk.benchmark.mlprediction.make_dataframe(labels, arrays)¶
Builds a dataframe from multiple arrays.
- Parameters
labels – list of labels
arrays – list of arrays (or one array)
- Returns
dataframes
- jupytalk.benchmark.mlprediction.timeexec(legend, code, number=50, repeat=200, verbose=True, context=None)¶
Measures the time for a given expression.
- Parameters
legend – name of the experiment
code – code to measure (as a string)
number – number of time to run the expression (and then divide by this number to get an average)
repeat – number of times to repeat the computation of the above average
verbose – print the time
globals – context (usuable equal to
globals()
)
- Returns
dictionary
<<<
from jupytalk.benchmark.mlprediction import timeexec code = "3 * 45535266234653452" print(timeexec("multiplication", code))
>>>
Average: 41.75 ns deviation 2.11 ns (with 50 runs) in [40.61 ns, 43.03 ns] {'legend': 'multiplication', 'average': 4.174597561359405e-08, 'deviation': 2.1054607494149415e-09, 'first': 6.940215826034546e-08, 'first3': 5.206714073816935e-08, 'last3': 4.10651167233785e-08, 'repeat': 200, 'min5': 4.0605664253234865e-08, 'max5': 4.302710294723511e-08, 'code': '3 * 45535266234653452', 'run': 50}
- jupytalk.benchmark.mlprediction.unit(x)¶
Optimizes the rendering of time.
<<<
from jupytalk.benchmark.mlprediction import unit print(unit(34)) print(unit(3.4)) print(unit(0.34)) print(unit(0.034)) print(unit(0.0034)) print(unit(0.00034)) print(unit(0.000034)) print(unit(0.0000034)) print(unit(0.00000034))
>>>
34.00 s 3.40 s 340.00 ms 34.00 ms 3.40 ms 340.00 µs 34.00 µs 3.40 µs 340.00 ns