def __init__(self,
df,
aggregates,
dimensions,
weight_col,
convergence_rate=1e-6,
max_iteration=50,
closure=1e-4):
self.df = df
self.aggregates = aggregates
self.dimensions = dimensions
self.weight_col = weight_col
self.convergence_rate = convergence_rate
self.max_iteration = max_iteration
self.closure = closure
self.ipfn = ipfn.ipfn(self.df,
self.aggregates,
self.dimensions,
weight_col=self.weight_col,
convergence_rate=self.convergence_rate,
max_iteration=self.max_iteration,
rate_tolerance=self.closure,
verbose=2)
def construct(self, a, b, relative = False, cf = np.asarray([])):
"""Construct a matrix based on known marginal sums"""
if relative:
a_i = a / a.sum()
b_i = b / b.sum()
else:
a_i = a
b_i = b
if np.allclose(a.sum() , b.sum(), rtol=0.01):
if self.verbose:
print('Marginal sums are align!')
else:
print('Marginal sums are not align\nTable example:')
print(
u"""
E.g.:
Sector---------------+
+---+---+---+ |
R | 5 | 6 | 7 | |
+---+---+---+---+ V
| . | . | . | 8 | Fuel Type
| . | . | . | 6 |
| . | . | . | 4 |
+---+---+---+---+
TI
∑ R = ∑ TI
""")
print("a = ", a)
print("b = ", b)
m = np.ones((len(a_i), len(b_i)))
IPF = ipfn(m, [b_i, a_i], [[1],[0]], verbose=(1 if self.verbose else 0))
m = IPF.iteration()
if isinstance(m, tuple):
n = m[0]
else:
n = m
if self.verbose:
print(u"a = ∑ m_i; {}".format(np.allclose(n.sum(1), a_i)))
print(u"b = ∑ m_j; {}".format(np.allclose(n.sum(0), b_i)))
if len(cf) > 0:
self.raw = n
n = n * cf
self.conversion_factor = cf
self.values = n
# update pandas
self.to_pandas()
return(n)
def tripDistribution(tripGeneration, costMatrix): costMatrix['ozone'] = costMatrix.columns costMatrix = costMatrix.melt(id_vars=['ozone']) costMatrix.columns = ['ozone', 'dzone', 'total'] production = tripGeneration['Production'] production.index.name = 'ozone' attraction = tripGeneration['Attraction'] attraction.index.name = 'dzone' aggregates = [production, attraction] dimensions = [['ozone'], ['dzone']] IPF = ipfn.ipfn(costMatrix, aggregates, dimensions) trips = IPF.iteration() return(trips.pivot(index='ozone', columns='dzone', values='total'))
def gravity_model(self, trip_generation, cost_matrix):
cost_matrix['ozone'] = cost_matrix.columns
cost_matrix = cost_matrix.melt(id_vars=['ozone'])
cost_matrix.columns = ['ozone', 'dzone', 'total']
production = trip_generation['Production']
production.index.name = 'ozone'
attraction = trip_generation['Attraction']
attraction.index.name = 'dzone'
aggregates = [production, attraction]
dimensions = [['ozone'], ['dzone']]
IPF = ipfn.ipfn(cost_matrix, aggregates, dimensions)
trips = IPF.iteration()
return (trips.pivot(index='ozone', columns='dzone', values='total'))
def from_ipfn(zones, cost_dataframe):
# TODO fix this method
cost_dataframe["origin_zone"] = cost_dataframe.index
cost_dataframe = cost_dataframe.melt(id_vars=["origin_zone"])
cost_dataframe.columns = ["origin_zone", "destination_zone", "total"]
production = zones["Production"]
production.index.name = "origin_zone"
attraction = zones["Attraction"]
attraction.index.name = "destination_zone"
aggregates = [production, attraction]
dimensions = [["origin_zone"], ["destination_zone"]]
IPF = ipfn.ipfn(cost_dataframe, aggregates, dimensions)
trips = IPF.iteration()
return OriginDestinationDataFrame(
trips.pivot(index="origin_zone",
columns="destination_zone",
values="total").stack())
def update(self, b, dimension=1):
"""Update inputs of matrix `m` given the outputs `b`"""
if self.verbose:
print("updating table")
m = self.values
# a = np.dot(m.T, m)
# b = np.diag(np.ones(a.shape[0])) - a
# q = np.linalg.solve(b, (b_o - m.sum(0)))
IPF = ipfn(m.copy(), [b], [[dimension]], verbose=self.verbose)
self.values = IPF.iteration()
if isinstance(self.values, tuple):
self.values = self.values[0]
# update pandas
self.to_pandas()
def __compute(self, strata, year, state):
mar = self.marginals[year][state]
if strata == RefinerIO.STRATA1:
self.__ui.display_text_box.AppendText(
"\nIPF by race, gender and age..\n")
self.m_sex = self.__sex_marginals(mar)
self.m_age_cat = self.__age_marginals(mar)
self.m_race = self.__race_marginals(mar)
aggregates = [self.m_race, self.m_sex, self.m_age_cat]
dimension = [["RACE_ETH"], ["SEX_"], ["AGE_CAT"]]
ipf = ipfn.ipfn(self.seed_matrix, aggregates, dimension)
df = ipf.iteration(self.__ui)
df = self.__remove_rows(df)
self.m_age_cat = df.groupby("AGE_CAT")["total"].sum()
self.m_sex = df.groupby("SEX_")["total"].sum()
self.m_race = df.groupby("RACE_ETH")["total"].sum()
elif strata == RefinerIO.STRATA2:
self.__ui.display_text_box.AppendText(
"\nIPF by race, gender, age and education..\n")
self.m_edu = self.__edu_marginals(mar)
print()
print(self.m_race)
print()
print(self.m_sex)
print()
print(self.m_age_cat)
print()
print(self.m_edu)
print()
aggregates = [self.m_race, self.m_sex, self.m_age_cat, self.m_edu]
dimension = [["RACE_ETH"], ["SEX_"], ["AGE_CAT"], ["EDU"]]
ipf = ipfn.ipfn(self.seed_matrix, aggregates, dimension)
df = ipf.iteration(self.__ui)
df_education = self.__merge_education(df=df)
# df_some_college = self.__remove_education(df=df)
df_education = self.__add_column(df=df_education,
col_name="FIPS",
value=int(mar["ID2"]),
index=0)
df_education = self.__add_column(df=df_education,
col_name="YEAR",
value="20" + year,
index=1)
df_education = self.__fill_dataframe(df=df_education,
year=year,
fips=int(mar["ID2"]))
print()
print(df_education)
if self.ipf_acs_data.empty:
self.ipf_acs_data = df_education
else:
self.ipf_acs_data = self.ipf_acs_data.append(df_education,
ignore_index=True)
def survey_cleanup_weighting(path, file):
df = pd.read_csv(path + file)
# dropping duplicate survey takers and columns don't need
df.drop_duplicates('What is your Amazon Worker ID?', inplace=True)
#df.drop(['What is your Amazon Worker ID?', 'Unnamed: 9', 'Timestamp', 'In what zip code do you live?'], axis=1, inplace=True)
# making human readable names
df.rename(
{
'Before the coronavirus crisis, how often did you eat out at a sit-down restaurant?':
'precovid',
'Currently how often do you eat out at a sit-down restaurant?':
'current',
"Next year how often do you think you'll eat out at a sit-down restaurant?":
'postcovid',
'What is your gender?':
'gender',
'What is the highest level of education you have completed?':
'educ'
},
axis=1,
inplace=True)
df['age_years'] = (2020 - df['In what year were you born?'])
#creating category for min number of times currently eat out per category per month
df['current_freq'] = df.current
df['current_freq'] = df.current_freq.map({
'Less than once per month': 0,
'Once a month': 1,
'Multiple time per month': 3,
'Once a week': 4,
'Multiple times per week': 8
})
# get age, bin, and drop year column
df['age'] = pd.cut(
2020 - df['In what year were you born?'],
bins=[-1, 24, 34, 44, 54, 64, 1000],
labels=['18-24', '25-34', '35-44', '45-54', '55-64', '65+'])
df.drop(['In what year were you born?'], axis=1, inplace=True)
# too few responses to estimate
df = df.loc[df.gender != 'Other', :]
# grouping educ into college, not college
df['educ'] = df.educ.map({
'College graduate': 'college',
'Post-graduate (e.g. MS, MBA, PhD, etc.)': 'college',
'High school': 'not_college',
'Did not finish high school': 'not_college'
})
# fixing indexing after dropping some rows
df.reset_index(drop=True, inplace=True)
# Weighting
np.random.seed(1)
bias_vars = ['gender', 'educ', 'age']
samp_aggs = [
df[col].value_counts().sort_index().values for col in bias_vars
] # marginal counts
dims = [[i] for i in range(len(samp_aggs))] # i'm using 1-d marginals
# print(aggs, dims)
# random initial values, using poisson to get count-like values
samp_init = np.random.poisson(np.mean([np.mean(x) for x in samp_aggs]),
(2, 2, 6)) + 1
ipf = ipfn(samp_init, samp_aggs, dims)
samp_dist = ipf.iteration()
samp_dist = samp_dist / np.sum(
samp_dist) # normalizing it to a proper distribution
# repeating for population marginals
np.random.seed(1)
from collections import OrderedDict
# ['gender', 'educ', 'age']
refdist = OrderedDict({
'gender': {
'Female': .52,
'Male': .48
},
'educ': {
'college': .35,
'not_college': .65
},
# i added .001 to '65+' to make it exactly sum to 1
'age': {
'18-24': .141,
'25-34': .194,
'35-44': .187,
'45-54': .194,
'55-64': .160,
'65+': .124
}
})
## checking that everything looks ok
for i, k in enumerate(refdist.keys()):
assert bias_vars[i] == k # names in right order?
for k, v in refdist.items():
assert set(v.keys()) == set(df[k]) # unique values all correct?
assert sum(v.values()) == 1. # each a proper distribution?
ref_aggs = [pd.Series(v).sort_index().values for v in refdist.values()]
# random initial values, using unif(.25,.75) to get probability-like values
ref_init = np.random.uniform(.25, .75, (2, 2, 6))
ipf = ipfn(ref_init, ref_aggs, dims) # reusing same dims
pop_dist = ipf.iteration()
# creating table for weights
wt_arr = pop_dist / samp_dist
dimnames = [df[col].value_counts().sort_index().index for col in bias_vars]
wt_df = pd.DataFrame(np.zeros(
(np.prod(wt_arr.shape), len(refdist) + 1))) # +1 for wt column
wt_df.columns = list(refdist.keys()) + ['wt']
l = 0
for i, f in enumerate(refdist['gender'].keys()):
for j, e in enumerate(refdist['educ'].keys()):
for k, a in enumerate(refdist['age'].keys()):
wt_df.iloc[l, :len(refdist)] = [f, e, a]
wt_df.iloc[l, len(refdist)] = wt_arr[i, j, k]
l += 1
#Adding the weights back to the data frame
df = pd.merge(df, wt_df, on=['gender', 'educ', 'age'])
df['wt'] = df.wt / df.wt.mean()
# new quesitons added for wave2
df.rename(
{
'Currently, about how often do you order takeout?':
'how_often_takeout'
},
axis=1,
inplace=True)
df.rename({'What is your annual household income?': 'household_income'},
axis=1,
inplace=True)
print(df.head(3))
return df
