CeShine Lee ceshine

## frozen_lake.py
# -*- coding: utf-8 -*-
"""
@author: CeShine

Using keras-rl (https://github.com/matthiasplappert/keras-rl) to provide basic framework,
and embedding layer to make it essentially a Q-table lookup algorithm.
"""

import tempfile

## bactrader_sample.py
"""A Simple Strategy Trading Two Stocks

Original code: https://blog.csdn.net/qq_26948675/article/details/80016633
Modified based on: https://www.backtrader.com/blog/posts/2018-04-22-improving-code/improving-code.html

Replaced the local CSV files with online data from IEX.

Unfortunately, this strategy is not profitable for the two stocks picked.
"""

## birnn.ipynb

      
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                ceshine
                / birnn.ipynb
            
            
              Created
              November 12, 2017 08:52
            
              
                Figuring How Bidirectional RNN works in Pytorch
              
          
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## stlr.py
class STLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(self, optimizer, max_mul, ratio, steps_per_cycle, decay=1, last_epoch=-1):
        self.max_mul = max_mul - 1
        self.turning_point = steps_per_cycle // (ratio + 1)
        self.steps_per_cycle = steps_per_cycle
        self.decay = decay
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        residual = self.last_epoch % self.steps_per_cycle

## jupyter_notebook_config.py
# Reference: https://svds.com/jupyter-notebook-best-practices-for-data-science/
import os
from subprocess import check_call

def post_save(model, os_path, contents_manager):
    """post-save hook for converting notebooks to .py scripts"""
    if model['type'] != 'notebook':
        return # only do this for notebooks
    d, fname = os.path.split(os_path)
    check_call(['jupyter', 'nbconvert', '--to', 'script', fname], cwd=d)

## profiling.ipynb

      
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                ceshine
                / profiling.ipynb
            
            
              Created
              April 24, 2020 06:31
            
              
                Tensorflow Profiler with Custom Training Loop
              
          
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## run_tf_glue.ipynb

      
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              3 stars
            
          
                ceshine
                / run_tf_glue.ipynb
            
            
              Created
              December 26, 2019 06:55
            
              
                Train huggingface/transformers BERT model on Cloud CPU with TF 2.1 (nightly build)
              
          
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## avazu_ftrl_concurrent.go
// Based on tinrtgu's Python script here:
// https://www.kaggle.com/c/avazu-ctr-prediction/forums/t/10927/beat-the-benchmark-with-less-than-1mb-of-memory
package main

import (
    "encoding/csv"
    "os"
    "strconv"
    "hash/fnv"
    "math"

## streamlit.py
import os
import sqlite3
import datetime
from typing import List

import faiss
import numpy as np
import pandas as pd
import joblib
import requests

## demo.py
from fastcore.basics import patch_to


class Demo:
    val = 10
    def __init__(self, val):
        self.val = val

# ====================
# The default mode
	# -- coding: utf-8 --
	"""
	@author: CeShine

	Using keras-rl (https://github.com/matthiasplappert/keras-rl) to provide basic framework,
	and embedding layer to make it essentially a Q-table lookup algorithm.
	"""

	import tempfile
	"""A Simple Strategy Trading Two Stocks

	Original code: https://blog.csdn.net/qq_26948675/article/details/80016633
	Modified based on: https://www.backtrader.com/blog/posts/2018-04-22-improving-code/improving-code.html

	Replaced the local CSV files with online data from IEX.

	Unfortunately, this strategy is not profitable for the two stocks picked.
	"""
	class STLR(torch.optim.lr_scheduler._LRScheduler):
	def __init__(self, optimizer, max_mul, ratio, steps_per_cycle, decay=1, last_epoch=-1):
	self.max_mul = max_mul - 1
	self.turning_point = steps_per_cycle // (ratio + 1)
	self.steps_per_cycle = steps_per_cycle
	self.decay = decay
	super().__init__(optimizer, last_epoch)

	def get_lr(self):
	residual = self.last_epoch % self.steps_per_cycle
	# Reference: https://svds.com/jupyter-notebook-best-practices-for-data-science/
	import os
	from subprocess import check_call

	def post_save(model, os_path, contents_manager):
	"""post-save hook for converting notebooks to .py scripts"""
	if model['type'] != 'notebook':
	return # only do this for notebooks
	d, fname = os.path.split(os_path)
	check_call(['jupyter', 'nbconvert', '--to', 'script', fname], cwd=d)
	// Based on tinrtgu's Python script here:
	// https://www.kaggle.com/c/avazu-ctr-prediction/forums/t/10927/beat-the-benchmark-with-less-than-1mb-of-memory
	package main

	import (
	"encoding/csv"
	"os"
	"strconv"
	"hash/fnv"
	"math"
	import os
	import sqlite3
	import datetime
	from typing import List

	import faiss
	import numpy as np
	import pandas as pd
	import joblib
	import requests
	from fastcore.basics import patch_to


	class Demo:
	val = 10
	def __init__(self, val):
	self.val = val

	# ====================
	# The default mode