def execute(context):
acquisition_sample_size = context.config("acquisition_sample_size")
feeder = zip(
bs.get_stages(context, "synthesis.population.spatial.home.locations",
acquisition_sample_size),
bs.get_stages(context,
"synthesis.population.spatial.primary.locations",
acquisition_sample_size),
bs.get_stages(context, "synthesis.population.sampled",
acquisition_sample_size),
)
probabilities = np.linspace(0.0, 1.0, 20)
quantiles = {"work": [], "education": []}
with context.progress(label="Processing commute data ...",
total=acquisition_sample_size) as progress:
for df_home, df_spatial, df_persons in feeder:
# Prepare home
df_home = pd.merge(df_home,
df_persons[["person_id", "household_id"]],
on="household_id")
df_home = df_home[["person_id", "geometry"
]].set_index("person_id").sort_index()
assert len(df_home) == len(df_persons)
for index, name in enumerate(("work", "education")):
df_destination = df_spatial[index]
df_destination = df_destination[["person_id", "geometry"]]
df_destination = df_destination.set_index(
"person_id").sort_index()
df_compare = df_home.loc[df_destination.index]
assert len(df_destination) == len(df_compare)
distances = df_destination["geometry"].distance(
df_compare["geometry"]) * 1e-3
quantiles[name].append(
[distances.quantile(p) for p in probabilities])
progress.update()
result = {}
random = np.random.RandomState(0)
for name in ("work", "education"):
data = np.array(quantiles[name])
indices = np.random.randint(acquisition_sample_size,
size=ESTIMATION_SAMPLE_SIZE)
mean = np.mean(data[indices, :], axis=0)
q5 = np.percentile(data[indices, :], 5, axis=0)
q95 = np.percentile(data[indices, :], 95, axis=0)
df = pd.DataFrame(dict(mean=mean, q5=q5, q95=q95, cdf=probabilities))
result[name] = df
return result
def execute(context):
acquisition_sample_size = context.config("acquisition_sample_size")
person_marginals = []
household_marginals = []
feeder = zip(
bs.get_stages(context, "synthesis.population.enriched", acquisition_sample_size),
bs.get_stages(context, "synthesis.population.spatial.home.zones", acquisition_sample_size)
)
for df, df_home in feeder:
df = pd.merge(df, df_home[["household_id", "departement_id", "commune_id"]])
marginals.prepare_classes(df)
person_marginals.append(stats.marginalize(df, marginals.SPATIAL_PERSON_MARGINALS, weight_column = None))
household_marginals.append(stats.marginalize(df.drop_duplicates("household_id"), marginals.SPATIAL_HOUSEHOLD_MARGINALS, weight_column = None))
person_marginals = stats.combine_marginals(person_marginals)
household_marginals = stats.combine_marginals(household_marginals)
person_marginals = stats.apply_per_marginal(person_marginals, stats.analyze_sample_and_flatten)
household_marginals = stats.apply_per_marginal(household_marginals, stats.analyze_sample_and_flatten)
return dict(person = person_marginals, household = household_marginals)
def execute(context):
acquisition_sample_size = context.config("acquisition_sample_size")
probabilities = np.linspace(0.0, 1.0, 20)
modes = ["car", "car_passenger", "pt", "bike", "walk"]
quantiles = { mode : [] for mode in modes }
generator = zip(
bs.get_stages(context, "synthesis.population.spatial.locations", acquisition_sample_size),
bs.get_stages(context, "synthesis.population.trips", acquisition_sample_size)
)
with context.progress(label = "Processing distance data ...", total = acquisition_sample_size) as progress:
for df_locations, df_trips in generator:
# Load locations and calculate euclidean distances
df_locations = df_locations[["person_id", "activity_index", "geometry"]].rename(columns = { "activity_index": "trip_index" })
df_locations["euclidean_distance"] = df_locations["geometry"].distance(df_locations["geometry"].shift(-1))
# Merge mode into distances
df_trips = pd.merge(
df_trips[["person_id", "trip_index", "mode", "preceding_purpose", "following_purpose", "departure_time", "arrival_time"]],
df_locations, on = ["person_id", "trip_index"], how = "inner"
)
df_trips["travel_time"] = df_trips["arrival_time"] - df_trips["departure_time"]
# Filter trips
primary_activities = ["home", "work", "education"]
#primary_activities = []
df_trips = df_trips[~(
df_trips["preceding_purpose"].isin(primary_activities) &
df_trips["following_purpose"].isin(primary_activities)
)]
# Calculate quantiles
for mode in modes:
df_mode = df_trips[df_trips["mode"] == mode]
quantiles[mode].append([df_mode["euclidean_distance"].quantile(p) for p in probabilities])
progress.update()
for mode in modes:
quantiles[mode] = np.array(quantiles[mode])
random = np.random.RandomState(0)
df_data = []
for mode in modes:
indices = np.random.randint(acquisition_sample_size, size = ESTIMATION_SAMPLE_SIZE)
mean = np.mean(quantiles[mode][indices,:], axis = 0)
q5 = np.percentile(quantiles[mode][indices,:], 5, axis = 0)
q95 = np.percentile(quantiles[mode][indices,:], 95, axis = 0)
df_data.append(pd.DataFrame(dict(mean = mean, q5 = q5, q95 = q95, cdf = probabilities)))
df_data[-1]["mode"] = mode
return pd.concat(df_data)
def execute(context):
acquisition_sample_size = context.config("acquisition_sample_size")
feeder = zip(
bs.get_stages(context, "synthesis.population.spatial.home.zones", acquisition_sample_size),
bs.get_stages(context, "synthesis.population.spatial.primary.locations", acquisition_sample_size),
bs.get_stages(context, "synthesis.population.sampled", acquisition_sample_size),
)
work_flows = []
education_flows = []
with context.progress(label = "Processing commute data ...", total = acquisition_sample_size) as progress:
for sample, (df_home, df_spatial, df_persons) in enumerate(feeder):
# Prepare home
df_home = pd.merge(df_persons[["person_id", "household_id"]], df_home, on = "household_id")
df_home = df_home[["person_id", "departement_id"]].rename(columns = { "departement_id": "home" })
# Prepare work
df_work = df_spatial[0]
df_work["departement_id"] = df_work["commune_id"] // 1000
df_work = df_work[["person_id", "departement_id"]].rename(columns = { "departement_id": "work" })
# Calculate work
df_work = pd.merge(df_home, df_work, on = "person_id").groupby(["home", "work"]).size().reset_index(name = "weight")
df_work = df_work.reset_index()
df_work["sample"] = sample
work_flows.append(df_work)
# Prepare work
df_education = df_spatial[1]
df_education["departement_id"] = df_education["commune_id"] // 1000
df_education = df_education[["person_id", "departement_id"]].rename(columns = { "departement_id": "education" })
# Calculate education
df_education = pd.merge(df_home, df_education, on = "person_id").groupby(["home", "education"]).size().reset_index(name = "weight")
df_education = df_education.reset_index()
df_education["sample"] = sample
education_flows.append(df_education)
progress.update()
df_work = pd.concat(work_flows)
df_education = pd.concat(education_flows)
df_work = stats.bootstrap(df_work, ESTIMATION_SAMPLE_SIZE)
df_education = stats.bootstrap(df_education, ESTIMATION_SAMPLE_SIZE)
return dict(work = df_work, education = df_education)
def execute(context):
acquisition_sample_size = context.config("acquisition_sample_size")
person_marginals = []
household_marginals = []
for df in bs.get_stages(context, "synthesis.population.enriched",
acquisition_sample_size):
marginals.prepare_classes(df)
person_marginals.append(
stats.marginalize(df,
marginals.ANALYSIS_PERSON_MARGINALS,
weight_column=None))
household_marginals.append(
stats.marginalize(df.drop_duplicates("household_id"),
marginals.ANALYSIS_HOUSEHOLD_MARGINALS,
weight_column=None))
person_marginals = stats.combine_marginals(person_marginals)
household_marginals = stats.combine_marginals(household_marginals)
person_marginals = stats.apply_per_marginal(
person_marginals, stats.analyze_sample_and_flatten)
household_marginals = stats.apply_per_marginal(
household_marginals, stats.analyze_sample_and_flatten)
return dict(person=person_marginals, household=household_marginals)
def execute(context):
acquisition_sample_size = context.config("acquisition_sample_size")
person_marginals = []
household_marginals = []
for df in bs.get_stages(context, "synthesis.population.enriched",
acquisition_sample_size):
marginals.prepare_classes(df)
person_marginals.append(
stats.marginalize(df,
marginals.ANALYSIS_PERSON_MARGINALS,
weight_column=None))
household_marginals.append(
stats.marginalize(df.drop_duplicates("household_id"),
marginals.ANALYSIS_HOUSEHOLD_MARGINALS,
weight_column=None))
person_marginals = stats.collect_marginalized_sample(person_marginals)
household_marginals = stats.collect_marginalized_sample(
household_marginals)
person_marginals = stats.bootstrap_sampled_marginals(
person_marginals, ESTIMATION_SAMPLE_SIZE)
household_marginals = stats.bootstrap_sampled_marginals(
household_marginals, ESTIMATION_SAMPLE_SIZE)
return dict(person=person_marginals, household=household_marginals)
def execute(context):
acquisition_sample_size = context.config("acquisition_sample_size")
data = []
feeder = zip(
bs.get_stages(context, "synthesis.population.sampled",
acquisition_sample_size),
bs.get_stages(context, "synthesis.population.activities",
acquisition_sample_size))
with context.progress(label="Marginalizing chain data ...",
total=acquisition_sample_size):
with context.parallel() as parallel:
data = list(parallel.imap_unordered(execute_parallel, feeder))
data = stats.combine_marginals(data)
data = stats.apply_per_marginal(data, stats.analyze_sample_and_flatten)
return data
def execute(context):
acquisition_sample_size = context.config("acquisition_sample_size")
data = []
feeder = zip(
bs.get_stages(context, "synthesis.population.sampled",
acquisition_sample_size),
bs.get_stages(context, "synthesis.population.activities",
acquisition_sample_size))
with context.progress(label="Marginalizing chain data ...",
total=acquisition_sample_size):
with context.parallel() as parallel:
data = list(parallel.imap_unordered(execute_parallel, feeder))
data = stats.collect_marginalized_sample(data)
data = stats.bootstrap_sampled_marginals(data, ESTIMATION_SAMPLE_SIZE)
return data
def execute(context):
acquisition_sample_size = context.config("acquisition_sample_size")
probabilities = np.linspace(0.0, 1.0, 20)
quantiles = []
with context.progress(label = "Processing commute data ...", total = acquisition_sample_size) as progress:
for df_income in bs.get_stages(context, "synthesis.population.income", acquisition_sample_size):
income = 12 * df_income["household_income"] / df_income["consumption_units"]
quantiles.append([income.quantile(p) for p in probabilities])
progress.update()
quantiles = np.array(quantiles)
mean = np.mean(quantiles, axis = 0)
min = np.min(quantiles, axis = 0)
max = np.max(quantiles, axis = 0)
return pd.DataFrame(dict(mean = mean, min = min, max = max, cdf = probabilities))
def execute(context):
reference = context.stage("analysis.reference.census.sociodemographics")["person"]
output = { marginal: [] for marginal in MARGINALS }
total = len(SAMPLING_RATES) * len(MARGINALS) * ACQUISITION_SAMPLE_SIZE
with context.progress(label = "Running Monte Carlo analysis ...", total = total) as progress:
for sampling_rate in SAMPLING_RATES:
partial_marginals = list(bt.get_stages(context, "sample_%f" % sampling_rate, sample_size = ACQUISITION_SAMPLE_SIZE))
with context.parallel(data = dict(partial_marginals = partial_marginals, reference = reference, sampling_rate = sampling_rate)) as parallel:
for partial_output in parallel.imap_unordered(process, np.arange(1, ACQUISITION_SAMPLE_SIZE + 1)):
for marginal in MARGINALS:
output[marginal].append(partial_output[marginal])
for marginal in MARGINALS:
output[marginal] = pd.concat(output[marginal])
return output
def execute(context):
acquisition_sample_size = context.config("acquisition_sample_size")
probabilities = np.linspace(0.0, 1.0, 20)
quantiles = []
with context.progress(label = "Processing commute data ...", total = acquisition_sample_size) as progress:
for df_income in bs.get_stages(context, "synthesis.population.income", acquisition_sample_size):
income = 12 * df_income["household_income"] / df_income["consumption_units"]
quantiles.append([income.quantile(p) for p in probabilities])
progress.update()
random = np.random.RandomState(0)
quantiles = np.array(quantiles)
indices = np.random.randint(acquisition_sample_size, size = ESTIMATION_SAMPLE_SIZE)
mean = np.mean(quantiles[indices,:], axis = 0)
q5 = np.percentile(quantiles[indices,:], 5, axis = 0)
q95 = np.percentile(quantiles[indices,:], 95, axis = 0)
return pd.DataFrame(dict(mean = mean, q5 = q5, q95 = q95, cdf = probabilities))
def execute(context):
df_codes = context.stage("data.spatial.municipalities")[[
"commune_id", "departement_id"
]]
acquisition_sample_size = context.config("acquisition_sample_size")
feeder = zip(
bs.get_stages(context, "synthesis.population.spatial.home.zones",
acquisition_sample_size),
bs.get_stages(context,
"synthesis.population.spatial.primary.locations",
acquisition_sample_size),
bs.get_stages(context, "synthesis.population.sampled",
acquisition_sample_size),
)
work_flows = []
education_flows = []
with context.progress(label="Processing commute data ...",
total=acquisition_sample_size) as progress:
for realization, (df_home, df_spatial,
df_persons) in enumerate(feeder):
# Prepare home
df_home = pd.merge(df_persons[["person_id", "household_id"]],
df_home,
on="household_id")
df_home = df_home[["person_id", "departement_id"
]].rename(columns={"departement_id": "home"})
# Prepare work
df_work = df_spatial[0]
df_work = pd.merge(df_work, df_codes, how="left", on="commune_id")
df_work["departement_id"] = df_work[
"departement_id"].cat.remove_unused_categories()
df_work = df_work[["person_id", "departement_id"
]].rename(columns={"departement_id": "work"})
# Calculate work
df_work = pd.merge(df_home, df_work, on="person_id").groupby(
["home", "work"]).size().reset_index(name="weight")
df_work["realization"] = realization
work_flows.append(df_work)
# Prepare work
df_education = df_spatial[1]
df_education = pd.merge(df_education,
df_codes,
how="left",
on="commune_id")
df_education["departement_id"] = df_education[
"departement_id"].cat.remove_unused_categories()
df_education = df_education[[
"person_id", "departement_id"
]].rename(columns={"departement_id": "education"})
# Calculate education
df_education = pd.merge(df_home, df_education,
on="person_id").groupby([
"home", "education"
]).size().reset_index(name="weight")
df_education["realization"] = realization
education_flows.append(df_education)
progress.update()
df_work = pd.concat(work_flows)
df_education = pd.concat(education_flows)
df_work = stats.analyze_sample_and_flatten(df_work)
df_education = stats.analyze_sample_and_flatten(df_education)
return dict(work=df_work, education=df_education)
def execute(context):
# Obtain reference data
reference = context.stage("analysis.reference.census.sociodemographics")
reference = reference[MARGINAL_LEVEL][MARGINAL]
reference = reference[np.logical_and.reduce([
reference[name] == value for name, value in zip(MARGINAL, VALUES)
])]["weight"].values[0]
# Gather marginal information
df_data = []
for sampling_rate in SAMPLING_RATES:
df_marginals = []
for df_stage in bt.get_stages(context,
"sample_%f" % sampling_rate,
sample_size=ACQUISITION_SAMPLE_SIZE):
marginals.prepare_classes(df_stage)
df_stage = stats.marginalize(df_stage, [MARGINAL],
weight_column=None)[MARGINAL]
df_stage["sampling_rate"] = sampling_rate
df_marginals.append(df_stage)
df_marginals = stats.collect_sample(df_marginals)
df_marginals = df_marginals[np.logical_and.reduce([
df_marginals[name] == value
for name, value in zip(MARGINAL, VALUES)
])]
df_data.append(df_marginals)
df_data = pd.concat(df_data)
sample_sizes = np.arange(1, MAXIMUM_SAMPLE_SIZE + 1)
df_figure = []
for sampling_rate in SAMPLING_RATES:
for sample_size in context.progress(
sample_sizes, label="Calculating sample sizes ..."):
df_marginals = df_data[df_data["sampling_rate"] == sampling_rate]
df_marginals = df_marginals.drop(columns=["sampling_rate"])
df_bootstrap = stats.bootstrap(
df_marginals,
ESTIMATION_SAMPLES,
sample_size,
metrics={
"mean":
"mean",
"q5":
lambda x: x.quantile(0.05),
"q95":
lambda x: x.quantile(0.95),
"precision":
lambda x: np.mean(
np.abs(x / sampling_rate - reference) / reference <=
ERROR_THRESHOLD)
})
df_bootstrap["sample_size"] = sample_size
df_bootstrap["sampling_rate"] = sampling_rate
df_figure.append(df_bootstrap)
df_figure = pd.concat(df_figure)
# Plotting
plotting.setup()
plt.figure(figsize=plotting.SHORT_FIGSIZE)
for index, sampling_rate in enumerate(SAMPLING_RATES):
df_rate = df_figure[df_figure["sampling_rate"] == sampling_rate]
plt.plot(df_rate["sample_size"],
df_rate["precision"],
label=SAMPLING_RATE_LABELS[sampling_rate],
color=SAMPLING_RATE_COLORS[sampling_rate])
plt.plot([0, MAXIMUM_SAMPLE_SIZE + 1], [0.9, 0.9], 'k:')
plt.xlim([1, MAXIMUM_SAMPLE_SIZE])
plt.ylim([0, 1.05])
plt.xlabel("Number of seeds $K$")
plt.ylabel(r"Error probability")
plt.gca().xaxis.set_major_locator(tck.FixedLocator([1, 10, 20, 30, 40]))
plt.gca().yaxis.set_major_formatter(
tck.FuncFormatter(lambda x, p: "%d%%" % (x * 100, )))
plt.grid()
plt.gca().set_axisbelow(True)
plt.legend(loc="best", title="Sampling rate $s$")
plt.tight_layout()
plt.savefig("%s/error_probability.pdf" % context.path())
plt.close()
def execute(context):
# Obtain reference data
reference = context.stage("analysis.reference.census.sociodemographics")
reference = reference[MARGINAL_LEVEL][MARGINAL]
reference = reference[np.logical_and.reduce([
reference[name] == value for name, value in zip(MARGINAL, VALUES)
])]["weight"].values[0]
# Gather information
df_marginals = []
for df_stage in bt.get_stages(context,
"sample",
sample_size=ACQUISITION_SAMPLE_SIZE):
marginals.prepare_classes(df_stage)
df_marginals.append(
stats.marginalize(df_stage, [MARGINAL],
weight_column=None)[MARGINAL])
df_marginals = stats.collect_sample(df_marginals)
df_marginals = df_marginals[np.logical_and.reduce([
df_marginals[name] == value for name, value in zip(MARGINAL, VALUES)
])]
sample_sizes = np.arange(1, MAXIMUM_SAMPLE_SIZE + 1)
df_figure = []
for sample_size in context.progress(sample_sizes,
label="Calculating sample sizes ..."):
df_bootstrap = stats.bootstrap(df_marginals, ESTIMATION_SAMPLES,
sample_size)
df_bootstrap["sample_size"] = sample_size
df_figure.append(df_bootstrap)
df_figure = pd.concat(df_figure)
df_figure["mean"] /= SAMPLING_RATE
df_figure["q5"] /= SAMPLING_RATE
df_figure["q95"] /= SAMPLING_RATE
# Prepare plot
plotting.setup()
plt.figure(figsize=plotting.SHORT_FIGSIZE)
plt.fill_between(df_figure["sample_size"],
df_figure["q5"],
df_figure["q95"],
alpha=0.25,
label="90% Conf.",
color=plotting.COLORSET[0],
linewidth=0.0)
plt.plot([1, MAXIMUM_SAMPLE_SIZE], [reference] * 2,
'k--',
label="Ref. $w$")
plt.plot([1, MAXIMUM_SAMPLE_SIZE], [reference * 0.99] * 2,
'k:',
label="1% Err.")
plt.plot([1, MAXIMUM_SAMPLE_SIZE], [reference * 1.01] * 2, 'k:')
plt.plot(df_figure["sample_size"],
df_figure["mean"],
label=r"$\mathrm{\mathbf{E}}[\tilde w_K]$",
color=plotting.COLORSET[0])
plt.xlim([1, MAXIMUM_SAMPLE_SIZE])
plt.xlabel("Number of seeds $K$")
plt.ylabel("Stratum weight")
plt.gca().xaxis.set_major_locator(tck.FixedLocator([1, 5, 10, 15, 20, 25]))
plt.gca().yaxis.set_major_formatter(
tck.FuncFormatter(lambda x, p: "%.2fM" % (x * 1e-6, )))
plt.grid()
plt.gca().set_axisbelow(True)
plt.legend(loc="best", ncol=2)
plt.tight_layout()
plt.savefig("%s/sample_count.pdf" % context.path())
plt.close()
