Montana Low montanalow

## init.sh
$ lore init my_app --python-version=3.6.4 --keras

## install.sh
# On Linux
$ pip install lore
# On OS X use homebrew python 2 or 3
$ brew install python3 && pip3 install lore

## generate.sh
$ cd my_app
$ lore generate scaffold product_popularity --keras --regression --holdout

## my_app.pipelines.product_popularity.py
# my_app/pipelines/product_popularity.py part 1

import os

from lore.encoders import Token, Unique, Norm
import lore.io
import lore.pipelines.holdout
import lore.env

import pandas

## departments.csv

          
            department_id
            department

            
              1
              frozen

            
              2
              other

            
              3
              bakery

            
              4
              produce

            
              5
              alcohol

## products.csv

          
            product_id
            product_name
            aisle_id
            department_id

            
              1
              Chocolate Sandwich Cookies
              61
              19

            
              2
              All-Seasons Salt
              104
              13

            
              3
              Robust Golden Unsweetened Oolong Tea
              94
              7

            
              4
              Smart Ones Classic Favorites Mini Rigatoni With Vodka Cream Sauce
              38
              1

            
              5
              Green Chile Anytime Sauce
              5
              13

## order_products.csv

          
            order_id
            product_id
            add_to_cart_order
            reordered

            
              2
              33120
              1
              1

            
              2
              28985
              2
              1

            
              2
              9327
              3
              0

            
              2
              45918
              4
              1

            
              2
              30035
              5
              0

## my_app.pipelines.product_popularity.get_encoders.py
# my_app/pipelines/product_popularity.py part 2

    def get_encoders(self):
        return (
            # An encoder to tokenize product names into max 15 tokens that
            # occur in the corpus at least 10 times. We also want the
            # estimator to spend 5x as many resources on name vs department
            # since there are so many more words in english than there are
            # grocery store departments.
            Token('product_name', sequence_length=15, minimum_occurrences=10, embed_scale=5),

## my_app.estimators.product_popularity.py
# my_app/estimators/product_popularity.py

import lore.estimators.keras


class Keras(lore.estimators.keras.Regression):
    pass

## my_app.models.product_popularity.py
# my_app/models/product_popularity.py

import lore.models.keras

import my_app.pipelines.product_popularity
import my_app.estimators.product_popularity


class Keras(lore.models.keras.Base):
    def __init__(self, pipeline=None, estimator=None):
	# On Linux
	$ pip install lore
	# On OS X use homebrew python 2 or 3
	$ brew install python3 && pip3 install lore
	$ cd my_app
	$ lore generate scaffold product_popularity --keras --regression --holdout
	# my_app/pipelines/product_popularity.py part 1

	import os

	from lore.encoders import Token, Unique, Norm
	import lore.io
	import lore.pipelines.holdout
	import lore.env

	import pandas
product_id	product_name	aisle_id	department_id
1	Chocolate Sandwich Cookies	61	19
2	All-Seasons Salt	104	13
3	Robust Golden Unsweetened Oolong Tea	94	7
4	Smart Ones Classic Favorites Mini Rigatoni With Vodka Cream Sauce	38	1
5	Green Chile Anytime Sauce	5	13
order_id	product_id	add_to_cart_order	reordered
2	33120	1	1
2	28985	2	1
2	9327	3	0
2	45918	4	1
2	30035	5	0
	# my_app/pipelines/product_popularity.py part 2

	def get_encoders(self):
	return (
	# An encoder to tokenize product names into max 15 tokens that
	# occur in the corpus at least 10 times. We also want the
	# estimator to spend 5x as many resources on name vs department
	# since there are so many more words in english than there are
	# grocery store departments.
	Token('product_name', sequence_length=15, minimum_occurrences=10, embed_scale=5),
	# my_app/estimators/product_popularity.py

	import lore.estimators.keras


	class Keras(lore.estimators.keras.Regression):
	pass
	# my_app/models/product_popularity.py

	import lore.models.keras

	import my_app.pipelines.product_popularity
	import my_app.estimators.product_popularity


	class Keras(lore.models.keras.Base):
	def __init__(self, pipeline=None, estimator=None):