Sandy4321

## mta-credit-card-steps.py
def expected_steps(df):
    Q = df.drop(
        ['Null', 'Activation'], axis=1).drop(['Null', 'Activation'], axis=0)

    I = np.identity(Q.shape[1])

    N = np.linalg.inv(I - Q.to_numpy())

    t = np.sum(N, axis=1)


## black_friday_challenge_prediction.sh
python -m predict.test_predict \
--csv-data-path gs://${PROJECT_ID}-black-friday-demo-bucket/data/test.csv \
--model-name black_friday_forecast \
--project-id ${PROJECT_ID} \
--version-name $VERSION

## black_friday_challenge_setup.py
from setuptools import setup, find_packages

REQUIREMENTS = [
    "scikit-learn",
    "pandas",
    "numpy",
    "tensorflow==1.15.0",
    "gcsfs",
    "google-api-python-client"
]

## black_friday_challenge_deploy.sh
PROJECT_ID=<PROJECT_ID>
BUCKET_NAME="${PROJECT_ID}-black-friday-demo-bucket"
MODEL_NAME=black_friday_forecast
REGION=us-central1
VERSION='v'`date '+%Y%m%d_%H%M%S'`
MODEL_PATH=gs://$BUCKET_NAME/models/$VERSION/model
PACKAGE_PATH=gs://$BUCKET_NAME/models/$VERSION/package/black_friday_forecast-0.0.1.tar.gz

# Package solution
python setup.py sdist

## black_friday_challenge_train.py
# -*- coding: utf-8 -*-

import argparse
import datetime
import pandas as pd
import os


from sklearn import preprocessing
from sklearn.metrics import mean_squared_error, make_scorer

## black_friday_challenge_train.sh
PROJECT_ID=<PROJECT_ID>
BUCKET_NAME="${PROJECT_ID}-black-friday-demo-bucket"
MODEL_NAME=black_friday_forecast
REGION=us-central1
VERSION='v'`date '+%Y%m%d_%H%M%S'`
MODEL_PATH=gs://$BUCKET_NAME/models/$VERSION/model

gcloud ai-platform jobs submit training black_friday_forecast_$(date +"%Y%m%d_%H%M%S") \
  --job-dir gs://$BUCKET_NAME/models/$VERSION/job_dir \
  --package-path ./train \

## black_friday_challenge_predict.py
import googleapiclient.discovery
import argparse
import pandas as pd
import json

import logging
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

## black_friday_challenge_custom_transformer.py
import pandas as pd
import numpy as np

from sklearn.base import BaseEstimator
from sklearn import preprocessing
import json


class BlackFridayPreprocess(BaseEstimator):

## black_friday_challenge_pipeline.py
from sklearn.ensemble import RandomForestRegressor
from train.models.custom_transformers import BlackFridayPreprocess, BlackFridayIdTransformer, BlackFridayLabelEncoder
from sklearn.pipeline import Pipeline


def random_forest():
    pipeline = Pipeline(
        [
            ('preprocess', BlackFridayPreprocess()),
            ('id_filter', BlackFridayIdTransformer(

## black_friday_challenge_data.csv

          
            Variable
            Definition

            
              User_ID
              User ID

            
              Product_ID
              Product ID

            
              Gender
              Sex of User

            
              Age
              Age in bins

            
              Occupation
              Occupation (Masked)

            
              City_Category
              Category of the City (A,B,C)

            
              Stay_In_Current_City_Years
              Number of years stay in current city

            
              Marital_Status
              Marital Status

            
              Product_Category_1
              Product Category (Masked)
	def expected_steps(df):
	Q = df.drop(
	['Null', 'Activation'], axis=1).drop(['Null', 'Activation'], axis=0)

	I = np.identity(Q.shape[1])

	N = np.linalg.inv(I - Q.to_numpy())

	t = np.sum(N, axis=1)
	python -m predict.test_predict \
	--csv-data-path gs://${PROJECT_ID}-black-friday-demo-bucket/data/test.csv \
	--model-name black_friday_forecast \
	--project-id ${PROJECT_ID} \
	--version-name $VERSION
	from setuptools import setup, find_packages

	REQUIREMENTS = [
	"scikit-learn",
	"pandas",
	"numpy",
	"tensorflow==1.15.0",
	"gcsfs",
	"google-api-python-client"
	]
	PROJECT_ID=<PROJECT_ID>
	BUCKET_NAME="${PROJECT_ID}-black-friday-demo-bucket"
	MODEL_NAME=black_friday_forecast
	REGION=us-central1
	VERSION='v'`date '+%Y%m%d_%H%M%S'`
	MODEL_PATH=gs://$BUCKET_NAME/models/$VERSION/model
	PACKAGE_PATH=gs://$BUCKET_NAME/models/$VERSION/package/black_friday_forecast-0.0.1.tar.gz

	# Package solution
	python setup.py sdist
	# -- coding: utf-8 --

	import argparse
	import datetime
	import pandas as pd
	import os


	from sklearn import preprocessing
	from sklearn.metrics import mean_squared_error, make_scorer
	import googleapiclient.discovery
	import argparse
	import pandas as pd
	import json

	import logging
	logging.basicConfig()
	logger = logging.getLogger(__name__)
	logger.setLevel(logging.INFO)
	import pandas as pd
	import numpy as np

	from sklearn.base import BaseEstimator
	from sklearn import preprocessing
	import json


	class BlackFridayPreprocess(BaseEstimator):
	from sklearn.ensemble import RandomForestRegressor
	from train.models.custom_transformers import BlackFridayPreprocess, BlackFridayIdTransformer, BlackFridayLabelEncoder
	from sklearn.pipeline import Pipeline


	def random_forest():
	pipeline = Pipeline(
	[
	('preprocess', BlackFridayPreprocess()),
	('id_filter', BlackFridayIdTransformer(
	Variable	Definition
	User_ID	User ID
	Product_ID	Product ID
	Gender	Sex of User
	Age	Age in bins
	Occupation	Occupation (Masked)
	City_Category	Category of the City (A,B,C)
	Stay_In_Current_City_Years	Number of years stay in current city
	Marital_Status	Marital Status
	Product_Category_1	Product Category (Masked)