def execute_parallel(context, data):
acquisition_sample_size = context.config("acquisition_sample_size")
df_population, df_chains = data
df_chains = df_chains[["person_id", "activity_index", "purpose"
]].sort_values(by=["person_id", "activity_index"])
df_chains = aggregate_chains(df_chains)
marginals.prepare_classes(df_population)
df_chains = pd.merge(
df_population[["person_id", "age_class", "sex", "age"]],
df_chains,
on="person_id")
df_chains["chain_length_class"] = np.minimum(df_chains["chain_length"],
CHAIN_LENGTH_LIMIT)
top_k_chains = df_chains.groupby("chain").size().reset_index(
name="weight").sort_values(
by="weight", ascending=False).head(CHAIN_TOP_K)["chain"].values
df_chains = df_chains[df_chains["chain"].isin(top_k_chains)]
df_chains["age_range"] = (df_chains["age"] >= 18) & (df_chains["age"] <=
40)
context.progress.update()
return stats.marginalize(df_chains, CHAIN_MARGINALS, weight_column=None)
def execute(context):
df_chains = context.stage("analysis.reference.hts.activities")[[
"person_id", "activity_id", "purpose"
]].sort_values(by=["person_id", "activity_id"])
df_chains = aggregate_chains(df_chains)
df_population = context.stage("hts")[1]
marginals.prepare_classes(df_population)
df_chains = pd.merge(df_population[[
"person_id", "age_class", "sex", "person_weight", "age"
]],
df_chains,
on="person_id")
df_chains["chain_length_class"] = np.minimum(df_chains["chain_length"],
CHAIN_LENGTH_LIMIT)
top_k_chains = df_chains.groupby(
"chain")["person_weight"].sum().reset_index().sort_values(
by="person_weight",
ascending=False).head(CHAIN_TOP_K)["chain"].values
df_chains = df_chains[df_chains["chain"].isin(top_k_chains)]
df_chains["age_range"] = (df_chains["age"] >= 18) & (df_chains["age"] <=
40)
return stats.marginalize(df_chains,
CHAIN_MARGINALS,
weight_column="person_weight")
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):
person_marginals = marginals.combine(
marginals.TOTAL_MARGINAL, marginals.CENSUS_PERSON_MARGINALS,
marginals.CENSUS_HOUSEHOLD_MARGINALS,
marginals.cross(marginals.CENSUS_PERSON_MARGINALS,
marginals.CENSUS_PERSON_MARGINALS),
marginals.cross(marginals.CENSUS_HOUSEHOLD_MARGINALS,
marginals.CENSUS_HOUSEHOLD_MARGINALS),
marginals.cross(marginals.CENSUS_PERSON_MARGINALS,
marginals.CENSUS_HOUSEHOLD_MARGINALS),
marginals.SPATIAL_MARGINALS,
marginals.cross(marginals.SPATIAL_MARGINALS,
marginals.CENSUS_PERSON_MARGINALS))
household_marginals = marginals.combine(
marginals.TOTAL_MARGINAL, marginals.CENSUS_HOUSEHOLD_MARGINALS,
marginals.cross(marginals.CENSUS_HOUSEHOLD_MARGINALS,
marginals.CENSUS_HOUSEHOLD_MARGINALS),
marginals.SPATIAL_MARGINALS,
marginals.cross(marginals.SPATIAL_MARGINALS,
marginals.CENSUS_HOUSEHOLD_MARGINALS))
df_persons = context.stage("data.census.filtered")
marginals.prepare_classes(df_persons)
df_households = df_persons.drop_duplicates("household_id").copy()
return dict(person=stats.marginalize(df_persons, person_marginals),
household=stats.marginalize(df_households,
household_marginals))
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):
df_households, df_persons, _ = context.stage("hts")
person_columns = set(df_persons.columns)
household_columns = set(df_households.columns)
household_columns -= person_columns
household_columns.add("household_id")
df = pd.merge(df_persons,
df_households[household_columns],
on="household_id")
assert len(df_persons) == len(df)
df_persons = df
spatial_marginals = [("departement_id", )]
person_marginals = marginals.combine(
marginals.TOTAL_MARGINAL, marginals.HTS_PERSON_MARGINALS,
marginals.HTS_HOUSEHOLD_MARGINALS,
marginals.cross(marginals.HTS_PERSON_MARGINALS,
marginals.HTS_PERSON_MARGINALS),
marginals.cross(marginals.HTS_HOUSEHOLD_MARGINALS,
marginals.HTS_HOUSEHOLD_MARGINALS),
marginals.cross(marginals.HTS_PERSON_MARGINALS,
marginals.HTS_HOUSEHOLD_MARGINALS), spatial_marginals,
marginals.cross(spatial_marginals, marginals.HTS_PERSON_MARGINALS))
household_marginals = marginals.combine(
marginals.TOTAL_MARGINAL, marginals.HTS_HOUSEHOLD_MARGINALS,
marginals.cross(marginals.HTS_HOUSEHOLD_MARGINALS,
marginals.HTS_HOUSEHOLD_MARGINALS), spatial_marginals,
marginals.cross(spatial_marginals, marginals.HTS_HOUSEHOLD_MARGINALS))
marginals.prepare_classes(df_persons)
df_households = df_persons.drop_duplicates("household_id").copy()
df_persons = df_persons.rename(columns={"person_weight": "weight"})
df_households = df_households.rename(
columns={"household_weight": "weight"})
return dict(person=stats.marginalize(df_persons, person_marginals),
household=stats.marginalize(df_households,
household_marginals))
def execute(context):
df = context.stage("sample")
marginals.prepare_classes(df)
return stats.marginalize(df, MARGINALS, weight_column=None)
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()
