acocos

acocos

from iggy_metaflow_base import IggyFlow, LoadedDataset
from metaflow import FlowSpec, step, catch


class IggyPerDistrictFlow(FlowSpec, IggyFlow):
    @step
    def start(self):
        # load data (same as `iggy_baseline_flow`)

    @step

acocos

from metaflow import JSONType, Parameter
import json

DEFAULT_IGGY_PKG_CONFIG = {
    "iggy_version_id": "20211110214810",
    "crosswalk_prefix": "fl_pinellas_quadkeys",
    "iggy_prefix": "fl_pinellas_quadkeys",
}

IGGY_FEATURES = [

acocos

from iggy_metaflow_base import IggyFlow
from metaflow import FlowSpec, step, Parameter


class IggyEnrichFlow(FlowSpec, IggyFlow):
    @step
    def start(self):
        # Load Data (same as `iggy_baseline_flow`)
        ...

acocos

from iggy_metaflow_base import IggyFlow # implements basic functions for loading data, model training and evaluation
from metaflow import FlowSpec, step
 

class IggyBaselineFlow(FlowSpec, IggyFlow):
    @step
    def start(self):
        # Load Data
        self.file_prefix = "baseline"
        data, scaled_features = self.load_data(drop_cols=True)

acocos

# convert the enriched data to a Featuretools entity set, and describe the columns
es_iggy = ft.EntitySet(id='sales-data-iggy')
es_iggy = es_iggy.entity_from_dataframe(
    entity_id='sales',
    dataframe=df_iggy,
    index='strap'
)
iggy_datadict = pd.DataFrame(
  [(v.name, v.type_string, df_iggy.iloc[8][v.name]) 
   for v in es_iggy['sales'].variables],

acocos

# finally, combine everything in an IggyFeatureSet
fs = iggyfeature.IggyFeatureSet(
  distance_features + count_features + value_features + amenity_score_features)

# and enrich your entire data frame at once
df_iggy = fs.enrich_dataframe(df, latitude_col='latitude', longitude_col='longitude')

acocos

# add features for amenity score
amenity_score_features = [
  iggyfeature.IggyAmenitiesScoreFeature(iggy, within_minutes_driving=10),
  iggyfeature.IggyAmenitiesScoreFeature(iggy, within_minutes_walking=10)
]

acocos

# add features for physical characteristics of location
value_features = [
  iggyfeature.IggyLookupFeature(
    iggy, label='light-pollution', calc_method='value'),
  iggyfeature.IggyLookupFeature(
    iggy, label='tree-canopy', calc_method='value'),
  iggyfeature.IggyLookupFeature(
    iggy, label='air_quality', calc_method='value'),
  iggyfeature.IggyLookupFeature(
    iggy, label='population_density_per_km', calc_method='value')

acocos

# add feature encoding count of specific POI types within nearby radius
count_features = [
  iggyfeature.IggyPOIFeature(
    iggy, label='restaurants', within_minutes_walking=15, calc_method='count'),
  iggyfeature.IggyPOIFeature(
    iggy, label='bars', within_minutes_walking=15, calc_method='count'),
  iggyfeature.IggyPOIFeature(
    iggy, label='coffee_shops', within_minutes_walking=15, calc_method='count'),
  iggyfeature.IggyPOIFeature(
    iggy, label='bakeries', within_minutes_walking=15, calc_method='count'),