bgweber

import featuretools as ft
from pyspark.sql.functions import pandas_udf, PandasUDFType

@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def apply_feature_generation(pandasInputDF):
    
    # create Entity Set representation 
    es = ft.EntitySet(id="events")
    es = es.entity_from_dataframe(entity_id="events", dataframe=pandasInputDF)
    es = es.normalize_entity(base_entity_id="events", new_entity_id="users", index="user_id")

bgweber

import pandas as pd

df = pd.read_csv("game_skater_stats.csv")
df = df[df['player_id'] == 8467412]
print(df.head(3))

for index, row in df.iterrows():
    event = { "playerID": int(row['player_id']), "Game_ID": int(row['game_id']),
              "goals": int(row['goals']), "assists": int(row['assists']), 
              "shots": int(row['shots']), "hits": int(row['hits']) }

bgweber

import fakeredis
import json

server = fakeredis.FakeServer()
redis = fakeredis.FakeStrictRedis(server=server)
print(redis)

# try fetching a record 
userID = 12345
record = redis.get(userID)

bgweber

import flask
import fakeredis
import json

server = fakeredis.FakeServer()
redis = fakeredis.FakeStrictRedis(server=server)
app = flask.Flask(__name__)

# endpoint for profile updates
@app.route("/update", methods=["GET","POST"])

bgweber

# define a schema for the result set, the user ID and model prediction
schema = StructType([StructField('user_id', LongType(), True),
                     StructField('prediction', DoubleType(), True)])  

# define the Pandas UDF 
@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def apply_model(sample_pd):

    # run the model on the partitioned data set 
    ids = sample_df['user_id']

bgweber

import dash
from flask import Flask
from flask_dance.contrib.google import google as flask_google
from datetime import datetime
import dash_html_components as html
from dash_google_auth import GoogleOAuth

server = Flask(__name__)

server.config["GOOGLE_OAUTH_CLIENT_ID"] = 'YOUR_CLIENT_ID'

bgweber

from flask import Flask
from flask_httpauth import HTTPTokenAuth

app = Flask(__name__)
auth = HTTPTokenAuth(scheme='Token')

@auth.verify_token
def verify_token(token):
        return '1234567890abcdefg' == token

bgweber

import requests
headers = { 'Authorization' : 'Token 1234567890abcdefg' }

result = requests.post("http://localhost:8000", headers=headers, \
      json = { 'G1':'1', 'G2':'0', 'G3':'0', 'G4':'0', 'G5':'0', \
               'G6':'0', 'G7':'0', 'G8':'0', 'G9':'0', 'G10':'0'})
print(result)
print(result.text)

bgweber

import requests

result = requests.post("http://localhost", \
      json = { 'G1':'1', 'G2':'0', 'G3':'0', 'G4':'0', 'G5':'0', \
               'G6':'0', 'G7':'0', 'G8':'0', 'G9':'0', 'G10':'0'})
print(result)
print(result.json())

bgweber

import pandas as pd
from sklearn.linear_model import LogisticRegression
import flask

df = pd.read_csv("https://github.com/bgweber/Twitch/raw/master/Recommendations/games-expand.csv")
model = LogisticRegression()
model.fit(df.drop(['label'], axis=1), df['label'])

app = flask.Flask(__name__)