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from sklearn.datasets import load_iris
from sklearn.model_selection import ParameterGrid, StratifiedKFold

iris = load_iris()
param_grid = [{'kernel': ['rbf'],
               'C': [0.01, 1],
               'gamma': [0.1, 1]},
              {'kernel': ['linear'],
               'C': [0.01, 1]}]
print("List of parameter grids:\n{}".format(param_grid))

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version: '3'
services:
  elasticsearch:
    build: elasticsearch
    container_name: elasticsearch
    environment:
      - discovery.type=single-node
      - bootstrap.memory_lock=true
      - "ES_JAVA_OPTS=-Xms256m -Xmx256m"
    ulimits:

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{
  "btcjpy" : {
    "aliases" : { },
    "mappings" : {
      "properties" : {
        "close" : {
          "type" : "long"
        },
        "timestamp" : {
          "type" : "date",

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#!/usr/bin/python
import json
import time
import python_bitbankcc
from datetime import datetime
from elasticsearch import Elasticsearch

def get_ticker(pub):
    ret = pub.get_ticker('btc_jpy')
    return ret

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#!/usr/bin/python
import json
import time
from datetime import datetime
from elasticsearch import Elasticsearch

size = 5

def print_search_stats(results):
    print("=" * 80)

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# Build an image that can do training and inference in SageMaker
# This is a Python 3.7.3 image with pyenv that uses the nginx, gunicorn, flask stack
# for serving inferences in a stable way.
FROM python:3.7

RUN apt-get -y update && apt-get install -y --no-install-recommends git wget nginx ca-certificates && \
    mkdir -p /opt/program && \
    rm -rf /var/lib/apt/lists/*
ADD requirements.txt ./
RUN pip3 install --no-cache --upgrade pip & pip3 install -r ./requirements.txt

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# S3 prefix
prefix = 'sagemaker/scikit-iris'

import sagemaker
from sagemaker import get_execution_role
sagemaker_session = sagemaker.Session()

# Get a SageMaker-compatible role used by this Notebook Instance.
role = get_execution_role()

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import argparse
import pandas as pd
import os

# GradientBoosting Classifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.externals import joblib

# Pipeline and StandardScaler
from sklearn.preprocessing import StandardScaler

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def lambda_handler(event, context):
    response = event
    
    # Lambda event
    if 'Records' in event:
        for records in event['Records']:
            if 's3' in records:
                handle_s3_event(records['s3'])
    else:
        pass

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def handle_s3_event(s3):
    print("s3=", s3)
    bucket = s3['bucket']['name']
    print("bucket: ", bucket)
    fn = s3['object']['key']
    print("fn: ", fn)
    jobid = fn.split("/")[-3]
    print("jobid=", jobid)
    return deploy_model(jobid)