| """ | |
| This module is responsibile for communicating with the | |
| U.S. SEC EDGAR database | |
| """ | |
| import datetime | |
| import time | |
| import http.client | |
| from io import BytesIO | |
| import requests |
| def crosscorr(datax, datay, lag=0, wrap=False): | |
| """ Lag-N cross correlation. | |
| Shifted data filled with NaNs | |
| Parameters | |
| ---------- | |
| lag : int, default 0 | |
| datax, datay : pandas.Series objects of equal length | |
| Returns |
| workflow "Demo workflow" { | |
| on = "push" | |
| resolves = ["SNS Notification"] | |
| } | |
| action "Build Image" { | |
| uses = "actions/docker/cli@c08a5fc9e0286844156fefff2c141072048141f6" | |
| runs = ["/bin/sh", "-c", "docker build -t $IMAGE_URI ."] | |
| env = { | |
| IMAGE_URI = "xxxxxxxx.dkr.ecr.ap-northeast-1.amazonaws.com/github-action-demo:latest" |
| ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | |
| ## Created by: Hang Zhang, Rutgers University, Email: zhang.hang@rutgers.edu | |
| ## Modified by Thomas Wolf, HuggingFace Inc., Email: thomas@huggingface.co | |
| ## Copyright (c) 2017-2018 | |
| ## | |
| ## This source code is licensed under the MIT-style license found in the | |
| ## LICENSE file in the root directory of this source tree | |
| ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | |
| """Encoding Data Parallel""" |
| import np | |
| from torch.utils.data import Dataset | |
| class GenHelper(Dataset): | |
| def __init__(self, mother, length, mapping): | |
| # here is a mapping from this index to the mother ds index | |
| self.mapping=mapping | |
| self.length=length | |
| self.mother=mother |
| # Simple Google Drive backup script with automatic authentication | |
| # for Google Colaboratory (Python 3) | |
| # Instructions: | |
| # 1. Run this cell and authenticate via the link and text box. | |
| # 2. Copy the JSON output below this cell into the `mycreds_file_contents` | |
| # variable. Authentication will occur automatically from now on. | |
| # 3. Create a new folder in Google Drive and copy the ID of this folder | |
| # from the URL bar to the `folder_id` variable. | |
| # 4. Specify the directory to be backed up in `dir_to_backup`. |
| #WHAT IS COLAB AND FREE GPU | |
| #Colaboratory is a cloud version of Jupyter Kernels, working on Google Drive. | |
| #Colab supports computations (Tensorflow, Keras, Pytorch..) on a GPU(Tesla K80), for free. | |
| #CREATE A FOLDER ON GOOGLE DRIVE | |
| #To begin with, simply create a folder on Google Drive, or just use default 'Colab Notebooks' folder | |
| #Right click the folder -> Open with -> Connect more apps -> Connect Colaboratory | |
| #CREATE COLAB NOTEBOOK | |
| #My Google Drive -> New -> More -> Colaboratory |
By Emily Gill and Amber Rivera
The Pipeline constructor from sklearn allows you to chain transformers and estimators together into a sequence that functions as one cohesive unit. For example, if your model involves feature selection, standardization, and then regression, those three steps, each as it's own class, could be encapsulated together via Pipeline.
