Toy Schedules Generation¶

Generate a population (schedules and attributes). Where every schedule is home -> work -> home, with varying durations. Attributes are randomly assigned.

In [3]:

from pathlib import Path

import pandas as pd
import random

from caveat.data.synth import ActivityGen
from caveat.data.utils import generate_population, trace_to_pam
from caveat.evaluate.describe.times import (
    joint_time_distributions_plot,
    times_distributions_plot,
)
from caveat.evaluate.describe.transitions import sequence_prob_plot

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

In [13]:

n = 10000

schedules_write_path = Path("tmp/toy_schedules.csv")
attributes_write_path = Path("tmp/toy_attributes.csv")

n = 10000 schedules_write_path = Path("tmp/toy_schedules.csv") attributes_write_path = Path("tmp/toy_attributes.csv")

In [14]:

pids = []
acts = []
starts = []
ends = []
durations = []
for i in range(n):
    departure_time = random.randint(300, 600)
    work_duration = random.randint(120, 600)
    home_duration = 1440 - work_duration - departure_time
    t = 0
    for act, duration in zip(
        ["home", "work", "home"], [departure_time, work_duration, home_duration]
    ):
        pids.append(i)
        acts.append(act)
        starts.append(t)
        t += duration
        ends.append(t)
        durations.append(duration)

schedules = pd.DataFrame(
    {
        "pid": pids,
        "act": acts,
        "start": starts,
        "end": ends,
        "duration": durations,
    }
)
schedules.head()

pids = [] acts = [] starts = [] ends = [] durations = [] for i in range(n): departure_time = random.randint(300, 600) work_duration = random.randint(120, 600) home_duration = 1440 - work_duration - departure_time t = 0 for act, duration in zip( ["home", "work", "home"], [departure_time, work_duration, home_duration] ): pids.append(i) acts.append(act) starts.append(t) t += duration ends.append(t) durations.append(duration) schedules = pd.DataFrame( { "pid": pids, "act": acts, "start": starts, "end": ends, "duration": durations, } ) schedules.head()

Out[14]:

	pid	act	start	end	duration
0	0	home	0	571	571
1	0	work	571	842	271
2	0	home	842	1440	598
3	1	home	0	492	492
4	1	work	492	1028	536

In [22]:

pids = []
genders = []
ages = []
work_statuses = []
educations = []
licenses = []
car_accesses = []

for i in range(n):
    pids.append(i)
    genders.append(random.choice(["m", "f"]))
    ages.append(random.randint(18, 85))
    work_statuses.append(random.choice(["employed", "unemployed"]))
    educations.append(random.choice(["low", "medium", "high"]))
    licenses.append(random.choice(["yes", "no"]))
    car_accesses.append(random.choice(["yes", "no"]))

attributes = pd.DataFrame(
    {
        "pid": pids,
        "gender": genders,
        "age": ages,
        "work_status": work_statuses,
        "education": educations,
        "license": licenses,
        "car_access": car_accesses,
    }
)
attributes.head()

pids = [] genders = [] ages = [] work_statuses = [] educations = [] licenses = [] car_accesses = [] for i in range(n): pids.append(i) genders.append(random.choice(["m", "f"])) ages.append(random.randint(18, 85)) work_statuses.append(random.choice(["employed", "unemployed"])) educations.append(random.choice(["low", "medium", "high"])) licenses.append(random.choice(["yes", "no"])) car_accesses.append(random.choice(["yes", "no"])) attributes = pd.DataFrame( { "pid": pids, "gender": genders, "age": ages, "work_status": work_statuses, "education": educations, "license": licenses, "car_access": car_accesses, } ) attributes.head()

Out[22]:

	pid	gender	age	work_status	education	license	car_access
0	0	m	54	employed	high	no	no
1	1	f	32	employed	medium	no	no
2	2	f	42	unemployed	low	yes	no
3	3	f	20	unemployed	high	no	no
4	4	f	58	employed	medium	yes	yes

In [23]:

schedules_write_path.parent.mkdir(exist_ok=True)
attributes_write_path.parent.mkdir(exist_ok=True)
schedules.to_csv(schedules_write_path, index=False)
attributes.to_csv(attributes_write_path, index=False)

schedules_write_path.parent.mkdir(exist_ok=True) attributes_write_path.parent.mkdir(exist_ok=True) schedules.to_csv(schedules_write_path, index=False) attributes.to_csv(attributes_write_path, index=False)

In [24]:

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(schedules, ["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(schedules, ["start", "end", "duration"]).round()

Out[24]:

		count	mean	std	min	max
attribute	act
start	home	20000.0	405.0	422.0	0.0	1192.0
	work	10000.0	450.0	87.0	300.0	600.0
end	home	20000.0	945.0	499.0	300.0	1440.0
	work	10000.0	811.0	164.0	421.0	1192.0
duration	home	20000.0	539.0	159.0	248.0	1019.0
	work	10000.0	361.0	139.0	120.0	600.0

In [25]:

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 [26]:

_ = times_distributions_plot(schedules, ys={})

_ = times_distributions_plot(schedules, ys={})

In [27]:

_ = joint_time_distributions_plot(schedules, ys={})

_ = joint_time_distributions_plot(schedules, ys={})

In [28]:

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

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

In [ ]: