Synthetic Schedules Generation¶

In [1]:

from pathlib import Path

import pandas as pd
from acteval.describe.times import (
    joint_time_distributions_plot,
    times_distributions_plot,
)
from acteval.describe.transitions import sequence_prob_plot

from caveat.data.synth import ActivityGen
from caveat.data.utils import generate_population, trace_to_pam

from pathlib import Path import pandas as pd from acteval.describe.times import ( joint_time_distributions_plot, times_distributions_plot, ) from acteval.describe.transitions import sequence_prob_plot from caveat.data.synth import ActivityGen from caveat.data.utils import generate_population, trace_to_pam

In [4]:

write_path = Path("tmp/synthetic_population.csv")

write_path = Path("tmp/synthetic_population.csv")

In [5]:

# Example
generator = ActivityGen()
generator.build()

trace = generator.run()
plan = trace_to_pam(trace, generator.map)
plan.plot()

# Example generator = ActivityGen() generator.build() trace = generator.run() plan = trace_to_pam(trace, generator.map) plan.plot()

In [6]:

population = generate_population(gen=generator, size=100)
population.act = population.act.map(generator.map)
population = population[["pid", "act", "start", "end", "duration"]]
population

population = generate_population(gen=generator, size=100) population.act = population.act.map(generator.map) population = population[["pid", "act", "start", "end", "duration"]] population

Out[6]:

	pid	act	start	end	duration
0	0	home	0	375	375
1	0	work	375	795	420
2	0	education	795	1005	210
3	0	home	1005	1440	435
4	1	home	0	375	375
...	...	...	...	...	...
491	99	home	0	375	375
492	99	shop	375	405	30
493	99	work	405	825	420
494	99	shop	825	960	135
495	99	home	960	1440	480

496 rows × 5 columns

In [7]:

write_path.parent.mkdir(exist_ok=True)
population.to_csv(write_path, index=False)

write_path.parent.mkdir(exist_ok=True) population.to_csv(write_path, index=False)

In [8]:

def describe_col(population, col: str) -> pd.DataFrame:
    description = population.groupby("act")[col].describe()[
        ["count", "mean", "std", "min", "max"]
    ]
    description["attribute"] = col
    return description


def describe_cols(population, cols: list[str]) -> pd.DataFrame:
    description = pd.concat(
        [describe_col(population, c) for c in cols], ignore_index=False
    )
    description = description.reset_index().set_index(["attribute", "act"])
    return description


describe_cols(population, ["start", "end", "duration"]).round()

def describe_col(population, col: str) -> pd.DataFrame: description = population.groupby("act")[col].describe()[ ["count", "mean", "std", "min", "max"] ] description["attribute"] = col return description def describe_cols(population, cols: list[str]) -> pd.DataFrame: description = pd.concat( [describe_col(population, c) for c in cols], ignore_index=False ) description = description.reset_index().set_index(["attribute", "act"]) return description describe_cols(population, ["start", "end", "duration"]).round()

Out[8]:

		count	mean	std	min	max
attribute	act
start	education	32.0	908.0	87.0	795.0	990.0
	home	216.0	555.0	521.0	0.0	1260.0
	leisure	56.0	829.0	302.0	375.0	1155.0
	shop	92.0	569.0	258.0	375.0	1020.0
	work	100.0	412.0	27.0	375.0	480.0
end	education	32.0	1039.0	42.0	990.0	1080.0
	home	216.0	928.0	512.0	375.0	1440.0
	leisure	56.0	911.0	321.0	405.0	1260.0
	shop	92.0	608.0	265.0	390.0	1035.0
	work	100.0	922.0	75.0	795.0	1020.0
duration	education	32.0	131.0	50.0	90.0	210.0
	home	216.0	373.0	71.0	180.0	480.0
	leisure	56.0	81.0	35.0	30.0	150.0
	shop	92.0	39.0	33.0	15.0	135.0
	work	100.0	511.0	69.0	420.0	615.0

In [9]:

def time_distributions(population: pd.DataFrame, mapping: dict):
    starts = {k: [] for k in mapping.values()}
    ends = {k: [] for k in mapping.values()}
    durations = {k: [] for k in mapping.values()}
    for act, acts in population.groupby("act"):
        starts[act] = list(acts.start)
        ends[act] = list(acts.end)
        durations[act] = list(acts.duration)
    return starts, ends, durations

def time_distributions(population: pd.DataFrame, mapping: dict): starts = {k: [] for k in mapping.values()} ends = {k: [] for k in mapping.values()} durations = {k: [] for k in mapping.values()} for act, acts in population.groupby("act"): starts[act] = list(acts.start) ends[act] = list(acts.end) durations[act] = list(acts.duration) return starts, ends, durations

In [10]:

starts, ends, durations = time_distributions(population, generator.map)

starts, ends, durations = time_distributions(population, generator.map)

In [11]:

_ = times_distributions_plot(population, ys={})

_ = times_distributions_plot(population, ys={})

In [12]:

_ = joint_time_distributions_plot(population, ys={})

_ = joint_time_distributions_plot(population, ys={})

In [13]:

_ = sequence_prob_plot(population, ys={}, figsize=(8, 6))

_ = sequence_prob_plot(population, ys={}, figsize=(8, 6))

In [ ]: