Showing preview only (1,559K chars total). Download the full file or copy to clipboard to get everything.
Repository: HDI-Project/ATMSeer
Branch: master
Commit: 35e40b5245ca
Files: 127
Total size: 1.5 MB
Directory structure:
gitextract_l1u8z3d0/
├── .dockerignore
├── .editorconfig
├── .github/
│ └── ISSUE_TEMPLATE.md
├── .gitignore
├── .gitmodules
├── Dockerfile
├── README.md
├── Vagrantfile
├── cleanup.sh
├── docker-compose.yml
├── install-docker.sh
├── install.sh
├── kill.sh
├── log.txt
├── package.json
├── public/
│ ├── index.html
│ ├── manifest.json
│ ├── morley.csv
│ └── viz/
│ ├── blood.csv
│ ├── datarun1_gp.csv
│ ├── datarun1_grid.csv
│ ├── datarun2_gp.csv
│ ├── datarun3_gp.csv
│ ├── dataset_31_credit-g.csv
│ ├── pollution_1.csv
│ └── pollution_1_test.csv
├── scripts/
│ ├── build_with_docker.sh
│ └── start_docker.sh
├── server/
│ ├── .gitignore
│ ├── atm_server/
│ │ ├── __init__.py
│ │ ├── api.py
│ │ ├── atm_helper/
│ │ │ ├── __init__.py
│ │ │ ├── btb_wireup.py
│ │ │ ├── datarun_config.py
│ │ │ ├── helpers.py
│ │ │ └── worker.py
│ │ ├── atmvis.py
│ │ ├── cache.py
│ │ ├── cli.py
│ │ ├── config.py
│ │ ├── db.py
│ │ ├── error.py
│ │ ├── recommender/
│ │ │ ├── OneHotEncoder.py
│ │ │ ├── __init__.py
│ │ │ ├── encoder.py
│ │ │ ├── logging_.py
│ │ │ ├── metafeature.py
│ │ │ ├── metafeatures.py
│ │ │ └── predict_dataset.py
│ │ ├── server.py
│ │ └── utils.py
│ ├── config/
│ │ ├── run.1.yaml
│ │ ├── run.yaml
│ │ └── sql.yaml
│ ├── recommender_dataset/
│ │ ├── data_meta_first.csv
│ │ └── dataset_method_PMA.csv
│ ├── requirements.txt
│ └── setup.py
├── src/
│ ├── Const.tsx
│ ├── assets/
│ │ ├── methodsDef.1.json
│ │ └── methodsDef.json
│ ├── components/
│ │ ├── App.css
│ │ ├── App.test.tsx
│ │ ├── App.tsx
│ │ ├── DataRuns/
│ │ │ ├── AskModal.tsx
│ │ │ ├── BarChart.tsx
│ │ │ ├── DataRuns.tsx
│ │ │ ├── Histogram.tsx
│ │ │ ├── HyperPartitions.tsx
│ │ │ ├── Methods.css
│ │ │ ├── Methods.tsx
│ │ │ ├── MethodsDotBarChart.css
│ │ │ ├── MethodsDotBarChart.tsx
│ │ │ ├── MethodsLineChart.css
│ │ │ ├── MethodsLineChart.tsx
│ │ │ ├── MethodsLineChart_backup.css
│ │ │ ├── MethodsLineChart_backup.tsx
│ │ │ ├── MethodsSearchSpace.css
│ │ │ ├── MethodsSearchSpace.tsx
│ │ │ ├── ThreeLevel/
│ │ │ │ ├── HyperParameters.tsx
│ │ │ │ ├── HyperPartitions.css
│ │ │ │ ├── HyperPartitions.tsx
│ │ │ │ ├── Methods.css
│ │ │ │ ├── Methods.tsx
│ │ │ │ ├── ThreeLevel.css
│ │ │ │ ├── ThreeLevel.tsx
│ │ │ │ └── index.tsx
│ │ │ ├── ThreeLevel_Horizontal/
│ │ │ │ ├── HyperParameters.tsx
│ │ │ │ ├── HyperPartitions.css
│ │ │ │ ├── HyperPartitions.tsx
│ │ │ │ ├── Methods.css
│ │ │ │ ├── Methods.tsx
│ │ │ │ ├── ThreeLevel.tsx
│ │ │ │ └── index.tsx
│ │ │ └── index.tsx
│ │ ├── DataView.css
│ │ ├── DataView.tsx
│ │ ├── SidePanel/
│ │ │ ├── DataSelector.css
│ │ │ ├── DataSelector.tsx
│ │ │ ├── DataView.css
│ │ │ ├── DataView.tsx
│ │ │ ├── FeatureChart.tsx
│ │ │ ├── LeaderBoard.css
│ │ │ ├── LeaderBoard.tsx
│ │ │ ├── LineChart.tsx
│ │ │ ├── OverallHistogram.tsx
│ │ │ ├── SettingsModal.tsx
│ │ │ ├── SidePanel.tsx
│ │ │ ├── index.css
│ │ │ └── index.tsx
│ │ ├── UploadModal.tsx
│ │ └── Utils/
│ │ └── GenerateSvg.tsx
│ ├── helper/
│ │ └── index.tsx
│ ├── images.d.ts
│ ├── index.css
│ ├── index.tsx
│ ├── registerServiceWorker.ts
│ ├── service/
│ │ └── dataService.ts
│ └── types/
│ └── index.ts
├── start.sh
├── startserver.sh
├── tsconfig.json
├── tsconfig.prod.json
├── tsconfig.test.json
├── tslint.json
├── vagrantProvision.sh
└── vagrantStart.sh
================================================
FILE CONTENTS
================================================
================================================
FILE: .dockerignore
================================================
node_modules/
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
.idea/
.vscode
.atm_server_cache
atm/
logs/
metrics/
models/
atm.db/
================================================
FILE: .editorconfig
================================================
[*]
indent_style = space
indent_size = 4
trim_trailing_whitespace = true
[{.travis.yml,*.json}]
indent_style = space
indent_size = 2
================================================
FILE: .github/ISSUE_TEMPLATE.md
================================================
## Description
## Expected Behavior
## Actual Behavior
## Steps to Reproduce the Problem
1.
1.
1.
## Specifications
- ATMSeer Version:
- Vagrant Version:
- VirtualBox Version:
- OS Version:
================================================
FILE: .gitignore
================================================
# See https://help.github.com/ignore-files/ for more about ignoring files.
# dependencies
/node_modules
# data
public/data/csvs/
atm/data/
# env
/pyenv
# testing
/coverage
# production
/build
# misc
.DS_Store
.env.local
.env.development.local
.env.test.local
.env.production.local
npm-debug.log*
yarn-debug.log*
yarn-error.log*
yarn.lock
# Editor configs
.vscode/
.idea/
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
atm.db
/atm/
logs
*.log
metrics/
models/
datasets/
.atm_server_cache/
.vagrant/*
node.log
================================================
FILE: .gitmodules
================================================
[submodule "lib/atm"]
path = lib/atm
url = http://github.com/HDI-Project/ATM.git
================================================
FILE: Dockerfile
================================================
FROM ubuntu:16.04
# install utilities
RUN apt-get update -yqq \
&& apt-get install -yqq \
unzip \
curl \
git \
ssh \
gcc \
make \
build-essential \
libkrb5-dev \
sudo \
apt-utils
RUN sudo apt-get install -y python3 python3-pip && \
pip3 install --upgrade pip
RUN sudo apt-get install -y libmysqlclient-dev sqlite3
RUN curl -sL https://deb.nodesource.com/setup_10.x | sudo -E bash -
RUN sudo apt-get install -yq nodejs \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
ADD . /code
WORKDIR /code
RUN pip3 install -r lib/atm/requirements.txt
RUN pip3 install lib/atm/
RUN pip3 install -r server/requirements.txt
RUN pip3 uninstall -y scikit_learn
RUN pip3 install scikit_learn==0.19.2
RUN npm install --quiet
RUN npm run build
EXPOSE 5000
# ENTRYPOINT [ "python" ]
ENV PYTHONPATH "${PYTHONPATH}:/code/server"
================================================
FILE: README.md
================================================
<p align="left">
<img width=15% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png" alt=“DAI-Lab” />
<i>An open source project from Data to AI Lab at MIT.</i>
</p>
# ATMSeer
ATMSeer is an interactive visualization tool for automated machine learning (AutoML). It supports users to monitor an ongoing AutoML process, analyze the searched models, and refine the search space in real-time through a multi-granularity visualization. In this instantiation, we build on top of the [ATM AutoML system](https://github.com/HDI-Project/ATM).
Our paper, "ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning", was presented at CHI 2019 ([pdf](https://arxiv.org/abs/1902.05009), [site](https://dai.lids.mit.edu/projects/atmseer/)).
[](http://www.youtube.com/watch?v=7QwN3mmiCzY "Video Title")
## Installation
### Prerequisites
Download and install or update [VirtualBox](https://www.virtualbox.org/wiki/Downloads) and [Vagrant](https://www.vagrantup.com/downloads.html).
### Download ATMSeer
```
git clone https://github.com/HDI-Project/ATMSeer.git
```
### Install and launch
Then go to ATMSeer project from the terminal and run
```
sh install.sh
```
This will install all the necessary packages in a virtual environment and launch the ATMSeer server.
If you see these messages in terminal:
```
...
default: [INFO] [13:07:26:werkzeug] * Running on http://0.0.0.0:7777/ (Press CTRL+C to quit)
default: [INFO] [13:07:26:werkzeug] * Restarting with stat
default: [WARNING] [13:07:31:werkzeug] * Debugger is active!
default: [INFO] [13:07:31:werkzeug] * Debugger PIN: 295-249-971
default: No valid rules have been specified for JavaScript files
```
then ATMSeer is up and running.
Finally, navigate to `http://localhost:7779/` in your broswer (preferably Chrome) to see ATMSeer.
### Debugging
In case you experience any issues, please try the following.
1. Open a terminal and navigate to ATMSeer project location.
2. Run `vagrant up` command (in case vagrant is not already running).
3. From the same terminal run `vagrant ssh`.
4. Run `cd /vagrant` and `sh start.sh`.
Open a second terminal and navigate to the ATMSeer project directory.
Run `vagrant ssh`, `cd /vagrant`, and `npm start`.
If you are still experiencing issues, please [open an issue](https://github.com/HDI-Project/ATMSeer/issues/new) and include as much detail as you can on your problem.
### Deploy with Docker [alternative]
If you have problem using [VirtualBox](https://www.virtualbox.org/wiki/Downloads) and [Vagrant](https://www.vagrantup.com/downloads.html) (especially in Windows), you can choose to use Docker instead.
Download and Install [Docker](https://docs.docker.com/install/) and [Compose](https://docs.docker.com/compose/install/)
```
sh install-docker.sh
```
If you see the following message:
```
atmseer | * Serving Flask app "server" (lazy loading)
atmseer | * Environment: production
atmseer | WARNING: Do not use the development server in a production environment.
atmseer | Use a production WSGI server instead.
atmseer | * Debug mode: off
atmseer | [INFO] [13:43:13:werkzeug] * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
```
You can navigate to `http://localhost:5000/` in your broswer (preferably Chrome) to see ATMSeer.
## Using ATMSeer
To see ATMSeer in action, you will first upload a dataset to use with the AutoML process, create a "datarun", and then monitor and control the ongoing AutoML process.
### Upload a dataset
You first need to upload a dataset to use with the AutoML process. We have provided several example datasets in `public/viz`. For example, press "Upload" and navigate to your ATMSeer installation and upload `public/viz/blood.csv`.
To use your own dataset, take care to provide it in the required data format (https://hdi-project.github.io/ATM/readme.html#data-format).
### Create a datarun
A datarun is a single AutoML process, comprising a dataset, a configuration for the AutoML process, and associated state information.
To create a new datarun, click the add icon, adjust methods, budget type, and budget, and press "Submit". You should not need to adjust advanced settings in most cases. If you do, please see https://hdi-project.github.io/ATM/database.html#dataruns for some details on the setting options.
### Start the AutoML process
Now, select your datarun from the list and press "Run" to begin the AutoML process. You should see completed trials populate on the top panel and details of algorithms, hyperpartitions, and hyperparameters in the three-level visualization on the right.
## Reference
If you use ATMSeer, please consider citing our paper:
``` bibtex
@inproceedings{wang2019atmseer,
author = {Wang, Qianwen and Ming, Yao and Jin, Zhihua and Shen, Qiaomu and Liu, Dongyu and Smith, Micah J. and Veeramachaneni, Kalyan and Qu, Huamin},
title = {ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning},
booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems},
series = {CHI '19},
year = {2019},
location = {Glasgow, Scotland UK},
publisher = {ACM},
address = {New York, NY, USA},
url = {http://doi.acm.org/10.1145/3290605.3300911}
}
```
================================================
FILE: Vagrantfile
================================================
# -*- mode: ruby -*-
# vi: set ft=ruby :
# All Vagrant configuration is done below. The "2" in Vagrant.configure
# configures the configuration version (we support older styles for
# backwards compatibility). Please don't change it unless you know what
# you're doing.
Vagrant.configure("2") do |config|
# The most common configuration options are documented and commented below.
# For a complete reference, please see the online documentation at
# https://docs.vagrantup.com.
# Every Vagrant development environment requires a box. You can search for
# boxes at https://vagrantcloud.com/search.
# config.vm.box = "ubuntu/trusty64"
config.vm.box = "ubuntu/xenial64"
config.vm.network "forwarded_port", guest: 7777, host: 7777
config.vm.network "forwarded_port", guest: 7779, host: 7779
config.vm.provision "vagrantprovision", type: "shell", run: "never" do |s|
s.path = "vagrantProvision.sh"
end
config.vm.provision "vagrantstart", type: "shell", run: "always" do |s|
s.path = "vagrantStart.sh"
end
# Disable automatic box update checking. If you disable this, then
# boxes will only be checked for updates when the user runs
# `vagrant box outdated`. This is not recommended.
# config.vm.box_check_update = false
# Create a forwarded port mapping which allows access to a specific port
# within the machine from a port on the host machine. In the example below,
# accessing "localhost:8080" will access port 80 on the guest machine.
# NOTE: This will enable public access to the opened port
# config.vm.network "forwarded_port", guest: 80, host: 8080
# Create a forwarded port mapping which allows access to a specific port
# within the machine from a port on the host machine and only allow access
# via 127.0.0.1 to disable public access
# config.vm.network "forwarded_port", guest: 80, host: 8080, host_ip: "127.0.0.1"
# Create a private network, which allows host-only access to the machine
# using a specific IP.
# config.vm.network "private_network", ip: "192.168.33.10"
# Create a public network, which generally matched to bridged network.
# Bridged networks make the machine appear as another physical device on
# your network.
# config.vm.network "public_network"
# Share an additional folder to the guest VM. The first argument is
# the path on the host to the actual folder. The second argument is
# the path on the guest to mount the folder. And the optional third
# argument is a set of non-required options.
# config.vm.synced_folder "../data", "/vagrant_data"
# Provider-specific configuration so you can fine-tune various
# backing providers for Vagrant. These expose provider-specific options.
# Example for VirtualBox:
#
config.vm.provider "virtualbox" do |vb|
# # Display the VirtualBox GUI when booting the machine
# vb.gui = true
#
# # Customize the amount of memory on the VM:
vb.memory = "4096"
end
#
# View the documentation for the provider you are using for more
# information on available options.
# Enable provisioning with a shell script. Additional provisioners such as
# Puppet, Chef, Ansible, Salt, and Docker are also available. Please see the
# documentation for more information about their specific syntax and use.
# config.vm.provision "shell" #, inline: <<-SHELL
# sudo apt-get update
# apt-get install -y apache2
# SHELL
end
================================================
FILE: cleanup.sh
================================================
rm -rf models/* metrics/* logs/* atm.db
================================================
FILE: docker-compose.yml
================================================
version: "3"
services:
web:
build: .
container_name: atmseer
ports:
- "5000:5000"
command: sudo python3 server/atm_server/server.py --port 5000
volumes:
- ./logs:/code/logs
- ./metrics:/code/models
- ./models:/code/metrics
================================================
FILE: install-docker.sh
================================================
git submodule init
git submodule update
mkdir logs && chmod 0777 logs
mkdir metrics && chmod 0777 metrics
mkdir models && chmod 0777 models
docker-compose up
================================================
FILE: install.sh
================================================
git submodule init
git submodule update
mkdir logs && chmod 0777 logs
mkdir metrics && chmod 0777 metrics
mkdir models && chmod 0777 models
vagrant up --provision-with vagrantprovision
vagrant reload
================================================
FILE: kill.sh
================================================
#!/bin/bash
pkill -9 python
================================================
FILE: log.txt
================================================
* Serving Flask app "server" (lazy loading)
* Environment: production
WARNING: Do not use the development server in a production environment.
Use a production WSGI server instead.
* Debug mode: on
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f793d8c65c0>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f793d8c60f0>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793906f8d0>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793906f588>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f79401cf160>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f7939061160>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7939061978>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7940144e80>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f79471b89e8>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f79388f04e0>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f794013f438>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793d901b00>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f7938ed2240>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f7938ed20f0>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7934009278>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7934009208>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f7933f10320>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f79389773c8>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7942960a90>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7942960b00>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f7938df9eb8>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f793b4e4ba8>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793b4e4c18>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793b4e4c88>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f7938922208>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f793b185208>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793b4dfa90>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793b4df4e0>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f79470f5080>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f79470f5d68>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793b563048>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793b563b38>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f7933f54358>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f7938e4fba8>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793d95b908>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793d95bb00>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f793b2b8240>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f793b2b8048>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7933c85b38>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7933b8a2b0>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f7931742518>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f7931742f28>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f79310f7400>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f79310f7470>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f793170ab00>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f793170af60>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7933d3bd68>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f79312e8ef0>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f793b1bd198>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f793b6a1c88>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793b567908>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f79339a35c0>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f793b28aac8>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f793b28a6d8>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f79314634e0>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7933e7b860>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f7933fe06a0>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f7933d07f98>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793d943588>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793d943eb8>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f7938f10fd0>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f793b20e0b8>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7942926668>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7942926588>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f793404e2b0>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f793404ec88>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f793892f390>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f7931637358>)]
[('n_estimators', 100), ('n_jobs', -1)]
[('criterion', 'entropy')]
[('max_features', <btb.hyper_parameter.HyperParameter object at 0x7f793b21d4e0>), ('max_depth', <btb.hyper_parameter.HyperParameter object at 0x7f793b201208>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f794997c2e8>), ('min_samples_leaf', <btb.hyper_parameter.HyperParameter object at 0x7f79315e9470>)]
[('n_estimators', 100), ('n_jobs', -1)]
================================================
FILE: package.json
================================================
{
"name": "vis_atm",
"version": "0.1.0",
"author": "qianwen Wang",
"private": true,
"dependencies": {
"@types/echarts": "0.0.13",
"antd": "3.5.4",
"axios": "^0.18.1",
"d3": "^5.6.0",
"echarts": "^4.1.0",
"echarts-for-react": "^2.0.12-beta.0",
"ml-pca": "^2.1.0",
"react": "^16.3.2",
"react-dom": "^16.3.2",
"react-scripts-ts": "2.16.0"
},
"scripts": {
"start": "cross-env PORT=7779 react-scripts-ts start",
"build": "react-scripts-ts build",
"test": "react-scripts-ts test --env=jsdom",
"eject": "react-scripts-ts eject"
},
"devDependencies": {
"@types/jest": "^22.2.3",
"@types/node": "^10.1.2",
"@types/react": "^16.3.14",
"@types/react-dom": "^16.0.5",
"cross-env": "^5.2.0",
"typescript": "^2.8.3"
}
}
================================================
FILE: public/index.html
================================================
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta name="theme-color" content="#000000">
<!--
manifest.json provides metadata used when your web app is added to the
homescreen on Android. See https://developers.google.com/web/fundamentals/engage-and-retain/web-app-manifest/
-->
<link rel="manifest" href="%PUBLIC_URL%/manifest.json">
<link rel="shortcut icon" href="%PUBLIC_URL%/favicon.ico">
<!--
Notice the use of %PUBLIC_URL% in the tags above.
It will be replaced with the URL of the `public` folder during the build.
Only files inside the `public` folder can be referenced from the HTML.
Unlike "/favicon.ico" or "favicon.ico", "%PUBLIC_URL%/favicon.ico" will
work correctly both with client-side routing and a non-root public URL.
Learn how to configure a non-root public URL by running `npm run build`.
-->
<title>ATMSeer</title>
</head>
<body>
<noscript>
You need to enable JavaScript to run this app.
</noscript>
<div id="root"></div>
<!--
This HTML file is a template.
If you open it directly in the browser, you will see an empty page.
You can add webfonts, meta tags, or analytics to this file.
The build step will place the bundled scripts into the <body> tag.
To begin the development, run `npm start` or `yarn start`.
To create a production bundle, use `npm run build` or `yarn build`.
-->
</body>
</html>
================================================
FILE: public/manifest.json
================================================
{
"short_name": "React App",
"name": "Create React App Sample",
"icons": [
{
"src": "favicon.ico",
"sizes": "64x64 32x32 24x24 16x16",
"type": "image/x-icon"
}
],
"start_url": "./index.html",
"display": "standalone",
"theme_color": "#000000",
"background_color": "#ffffff"
}
================================================
FILE: public/morley.csv
================================================
Expt,Run,Speed
1,1,850
1,2,740
1,3,900
1,4,1070
1,5,930
1,6,850
1,7,950
1,8,980
1,9,980
1,10,880
1,11,1000
1,12,980
1,13,930
1,14,650
1,15,760
1,16,810
1,17,1000
1,18,1000
1,19,960
1,20,960
2,1,960
2,2,940
2,3,960
2,4,940
2,5,880
2,6,800
2,7,850
2,8,880
2,9,900
2,10,840
2,11,830
2,12,790
2,13,810
2,14,880
2,15,880
2,16,830
2,17,800
2,18,790
2,19,760
2,20,800
3,1,880
3,2,880
3,3,880
3,4,860
3,5,720
3,6,720
3,7,620
3,8,860
3,9,970
3,10,950
3,11,880
3,12,910
3,13,850
3,14,870
3,15,840
3,16,840
3,17,850
3,18,840
3,19,840
3,20,840
4,1,890
4,2,810
4,3,810
4,4,820
4,5,800
4,6,770
4,7,760
4,8,740
4,9,750
4,10,760
4,11,910
4,12,920
4,13,890
4,14,860
4,15,880
4,16,720
4,17,840
4,18,850
4,19,850
4,20,780
5,1,890
5,2,840
5,3,780
5,4,810
5,5,760
5,6,810
5,7,790
5,8,810
5,9,820
5,10,850
5,11,870
5,12,870
5,13,810
5,14,740
5,15,810
5,16,940
5,17,950
5,18,800
5,19,810
5,20,870
================================================
FILE: public/viz/blood.csv
================================================
V1,V2,V3,V4,class
2,50,12500,98,2
0,13,3250,28,2
1,16,4000,35,2
2,20,5000,45,2
1,24,6000,77,1
4,4,1000,4,1
2,7,1750,14,2
1,12,3000,35,1
2,9,2250,22,2
5,46,11500,98,2
4,23,5750,58,1
0,3,750,4,1
2,10,2500,28,2
1,13,3250,47,1
2,6,1500,15,2
2,5,1250,11,2
2,14,3500,48,2
2,15,3750,49,2
2,6,1500,15,2
2,3,750,4,2
2,3,750,4,2
4,11,2750,28,1
2,6,1500,16,2
2,6,1500,16,2
9,9,2250,16,1
4,14,3500,40,1
4,6,1500,14,1
4,12,3000,34,2
4,5,1250,11,2
4,8,2000,21,1
1,14,3500,58,1
4,10,2500,28,2
4,10,2500,28,2
4,9,2250,26,2
2,16,4000,64,1
2,8,2000,28,2
2,12,3000,47,2
4,6,1500,16,2
2,14,3500,57,2
4,7,1750,22,2
2,13,3250,53,2
2,5,1250,16,1
2,5,1250,16,2
2,5,1250,16,1
4,20,5000,69,2
4,9,2250,28,2
2,9,2250,36,1
2,2,500,2,1
2,2,500,2,1
2,2,500,2,1
2,11,2750,46,1
2,11,2750,46,2
2,6,1500,22,1
2,12,3000,52,1
4,5,1250,14,2
4,19,4750,69,2
4,8,2000,26,2
2,7,1750,28,2
2,16,4000,81,1
3,6,1500,21,1
2,7,1750,29,1
2,8,2000,35,2
2,10,2500,49,1
4,5,1250,16,2
2,3,750,9,2
3,16,4000,74,1
2,4,1000,14,2
0,2,500,4,1
4,7,1750,25,1
1,9,2250,51,1
2,4,1000,16,1
2,4,1000,16,1
4,17,4250,71,2
2,2,500,4,1
2,2,500,4,2
2,2,500,4,2
2,4,1000,16,2
2,2,500,4,1
2,2,500,4,1
2,2,500,4,1
4,6,1500,23,2
2,4,1000,16,1
2,4,1000,16,1
2,4,1000,16,1
2,6,1500,28,2
2,6,1500,28,1
4,2,500,4,1
4,2,500,4,1
4,2,500,4,1
2,7,1750,35,2
4,2,500,4,2
4,2,500,4,1
4,2,500,4,1
4,2,500,4,1
12,11,2750,23,1
4,7,1750,28,1
3,17,4250,86,1
4,9,2250,38,2
4,4,1000,14,2
5,7,1750,26,2
4,8,2000,34,2
2,13,3250,76,2
4,9,2250,40,1
2,5,1250,26,1
2,5,1250,26,1
6,17,4250,70,1
0,8,2000,59,1
3,5,1250,26,1
2,3,750,14,1
2,10,2500,64,1
4,5,1250,23,2
4,9,2250,46,1
4,5,1250,23,1
4,8,2000,40,2
2,12,3000,82,1
11,24,6000,64,1
2,7,1750,46,2
4,11,2750,61,1
1,7,1750,57,1
2,11,2750,79,2
2,3,750,16,2
4,5,1250,26,2
2,6,1500,41,2
2,5,1250,33,2
2,4,1000,26,1
2,5,1250,34,1
4,8,2000,46,2
2,4,1000,26,1
4,8,2000,48,2
2,2,500,10,2
4,5,1250,28,1
2,12,3000,95,1
2,2,500,10,1
4,6,1500,35,1
2,11,2750,88,1
2,3,750,19,1
2,5,1250,37,1
2,12,3000,98,1
9,5,1250,19,1
2,2,500,11,1
2,9,2250,74,1
5,14,3500,86,1
4,3,750,16,1
4,3,750,16,1
4,2,500,9,2
4,3,750,16,2
6,3,750,14,1
2,2,500,11,1
2,2,500,11,2
2,2,500,11,1
2,7,1750,58,2
4,6,1500,39,1
4,11,2750,78,1
2,1,250,2,2
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,2
2,1,250,2,2
2,1,250,2,2
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
11,10,2500,35,1
11,4,1000,16,2
4,5,1250,33,2
4,6,1500,41,2
2,3,750,22,1
4,4,1000,26,2
10,4,1000,16,1
2,4,1000,35,1
4,12,3000,88,1
13,8,2000,26,1
11,9,2250,33,1
4,5,1250,34,1
4,4,1000,26,1
8,15,3750,77,1
4,5,1250,35,2
4,7,1750,52,1
4,7,1750,52,1
2,4,1000,35,1
11,11,2750,42,1
2,2,500,14,1
2,5,1250,47,2
9,8,2000,38,2
4,6,1500,47,1
11,7,1750,29,1
9,9,2250,45,1
4,6,1500,52,1
4,7,1750,58,1
6,2,500,11,2
4,7,1750,58,1
11,9,2250,38,1
11,6,1500,26,1
2,2,500,16,1
2,7,1750,76,1
11,6,1500,27,1
11,3,750,14,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,2
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,2
4,1,250,4,2
4,1,250,4,1
4,1,250,4,2
4,1,250,4,2
4,1,250,4,1
4,3,750,24,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,2
4,1,250,4,1
10,8,2000,39,1
14,7,1750,26,1
8,10,2500,63,1
11,3,750,15,1
4,2,500,14,1
2,4,1000,43,1
8,9,2250,58,1
8,8,2000,52,2
11,22,5500,98,1
4,3,750,25,2
11,17,4250,79,2
9,2,500,11,1
4,5,1250,46,1
11,12,3000,58,1
7,12,3000,86,1
11,2,500,11,1
11,2,500,11,1
11,2,500,11,1
2,6,1500,75,1
11,8,2000,41,2
11,3,750,16,2
12,13,3250,59,1
2,3,750,35,1
16,8,2000,28,1
11,7,1750,37,1
4,3,750,28,1
12,12,3000,58,1
4,4,1000,41,1
11,14,3500,73,2
2,2,500,23,1
2,3,750,38,2
4,5,1250,58,1
4,4,1000,43,2
3,2,500,23,1
11,8,2000,46,1
4,7,1750,82,1
13,4,1000,21,1
16,11,2750,40,1
16,7,1750,28,1
7,2,500,16,1
4,5,1250,58,1
4,5,1250,58,1
4,4,1000,46,1
14,13,3250,57,1
4,3,750,34,1
14,18,4500,78,1
11,8,2000,48,1
14,16,4000,70,1
14,4,1000,22,2
14,5,1250,26,1
8,2,500,16,1
11,5,1250,33,1
11,2,500,14,1
4,2,500,23,1
9,2,500,16,2
14,5,1250,28,2
14,3,750,19,2
14,4,1000,23,2
16,12,3000,50,1
11,4,1000,28,1
11,5,1250,35,1
11,5,1250,35,1
2,4,1000,70,1
14,5,1250,28,1
14,2,500,14,1
14,2,500,14,1
14,2,500,14,1
14,2,500,14,1
14,2,500,14,1
14,2,500,14,1
2,3,750,52,1
14,6,1500,34,1
11,5,1250,37,2
4,5,1250,74,1
11,3,750,23,1
16,4,1000,23,1
16,3,750,19,1
11,5,1250,38,1
11,2,500,16,1
12,9,2250,60,1
9,1,250,9,1
9,1,250,9,1
4,2,500,29,1
11,2,500,17,1
14,4,1000,26,1
11,9,2250,72,2
11,5,1250,41,1
15,16,4000,82,1
9,5,1250,51,2
11,4,1000,34,1
14,8,2000,50,2
16,7,1750,38,1
14,2,500,16,1
2,2,500,41,1
14,16,4000,98,1
14,4,1000,28,2
16,7,1750,39,1
14,7,1750,47,1
16,6,1500,35,1
16,6,1500,35,2
11,7,1750,62,2
16,2,500,16,1
16,3,750,21,2
11,3,750,28,1
11,7,1750,64,1
11,1,250,11,2
9,3,750,34,1
14,4,1000,30,1
23,38,9500,98,1
11,6,1500,58,1
11,1,250,11,1
11,1,250,11,1
11,1,250,11,1
11,1,250,11,1
11,1,250,11,1
11,1,250,11,1
11,1,250,11,1
11,1,250,11,1
11,2,500,21,1
11,5,1250,50,1
11,2,500,21,1
16,4,1000,28,1
4,2,500,41,1
16,6,1500,40,1
14,3,750,26,1
9,2,500,26,1
21,16,4000,64,1
14,6,1500,51,1
11,2,500,24,1
4,3,750,71,1
21,13,3250,57,1
11,6,1500,71,1
14,2,500,21,2
23,15,3750,57,1
14,4,1000,38,1
11,2,500,26,1
16,5,1250,40,2
4,2,500,51,2
14,3,750,31,1
4,2,500,52,1
9,4,1000,65,1
14,4,1000,40,1
11,3,750,40,2
14,5,1250,50,1
14,1,250,14,1
14,1,250,14,1
14,1,250,14,1
14,1,250,14,1
14,1,250,14,1
14,1,250,14,1
14,1,250,14,1
14,1,250,14,1
14,7,1750,72,1
14,1,250,14,1
14,1,250,14,1
9,3,750,52,1
14,7,1750,73,1
11,4,1000,58,1
11,4,1000,59,1
4,2,500,59,1
11,4,1000,61,1
16,4,1000,40,1
16,10,2500,89,1
21,2,500,21,2
21,3,750,26,1
16,8,2000,76,1
21,3,750,26,2
18,2,500,23,1
23,5,1250,33,1
23,8,2000,46,1
16,3,750,34,1
14,5,1250,64,1
14,3,750,41,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
16,4,1000,45,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
16,2,500,26,1
21,2,500,23,1
16,2,500,27,1
21,2,500,23,1
21,2,500,23,1
14,4,1000,57,1
16,5,1250,60,1
23,2,500,23,1
14,5,1250,74,1
23,3,750,28,1
16,3,750,40,1
9,2,500,52,1
9,2,500,52,1
16,7,1750,87,2
14,4,1000,64,1
14,2,500,35,1
16,7,1750,93,1
21,2,500,25,1
14,3,750,52,1
23,14,3500,93,1
18,8,2000,95,1
16,3,750,46,1
11,3,750,76,1
11,2,500,52,1
11,3,750,76,1
23,12,3000,86,1
21,3,750,35,1
23,2,500,26,1
23,2,500,26,1
23,8,2000,64,1
16,3,750,50,1
23,3,750,33,1
21,3,750,38,1
23,2,500,28,1
21,1,250,21,1
21,1,250,21,1
21,1,250,21,1
21,1,250,21,1
21,1,250,21,1
21,1,250,21,1
21,1,250,21,1
21,1,250,21,1
21,1,250,21,1
21,1,250,21,2
21,1,250,21,1
21,1,250,21,1
21,5,1250,60,1
23,4,1000,45,1
21,4,1000,52,1
22,1,250,22,2
11,2,500,70,1
23,5,1250,58,1
23,3,750,40,1
23,3,750,41,1
14,3,750,83,1
21,2,500,35,1
26,5,1250,49,2
23,6,1500,70,1
23,1,250,23,1
23,1,250,23,1
23,1,250,23,1
23,1,250,23,1
23,1,250,23,1
23,1,250,23,1
23,1,250,23,1
23,1,250,23,1
23,4,1000,53,1
21,6,1500,86,1
23,3,750,48,1
21,2,500,41,1
21,3,750,64,1
16,2,500,70,1
21,3,750,70,1
23,4,1000,87,1
23,3,750,89,1
23,2,500,87,1
35,3,750,64,1
38,1,250,38,1
38,1,250,38,1
40,1,250,40,1
74,1,250,74,1
2,43,10750,86,2
6,22,5500,28,2
2,34,8500,77,2
2,44,11000,98,1
0,26,6500,76,2
2,41,10250,98,2
3,21,5250,42,2
2,11,2750,23,1
2,21,5250,52,2
2,13,3250,32,2
4,4,1000,4,2
2,11,2750,26,1
2,11,2750,28,1
3,14,3500,35,1
4,16,4000,38,2
4,6,1500,14,1
3,5,1250,12,2
4,33,8250,98,2
3,10,2500,33,2
4,10,2500,28,2
2,11,2750,40,2
2,11,2750,41,2
4,13,3250,39,2
1,10,2500,43,2
4,9,2250,28,1
2,4,1000,11,1
2,5,1250,16,2
2,15,3750,64,1
5,24,6000,79,1
2,6,1500,22,2
4,5,1250,16,2
2,4,1000,14,2
4,8,2000,28,1
2,4,1000,14,1
2,6,1500,26,1
4,5,1250,16,2
2,7,1750,32,2
2,6,1500,26,2
2,8,2000,38,2
2,2,500,4,2
2,6,1500,28,2
2,10,2500,52,1
4,16,4000,70,2
4,2,500,4,2
1,14,3500,95,1
4,2,500,4,2
7,14,3500,48,1
2,3,750,11,1
2,12,3000,70,2
4,7,1750,32,2
4,4,1000,16,1
2,6,1500,35,2
4,6,1500,28,2
2,3,750,14,1
2,4,1000,23,1
4,4,1000,18,1
5,6,1500,28,1
4,6,1500,30,1
14,5,1250,14,1
3,8,2000,50,1
4,11,2750,64,2
4,9,2250,52,1
4,16,4000,98,2
7,10,2500,47,1
4,14,3500,86,1
2,9,2250,75,1
4,6,1500,35,1
4,9,2250,55,1
4,6,1500,35,2
2,6,1500,45,1
2,6,1500,47,1
4,2,500,9,1
2,2,500,11,2
2,2,500,11,1
2,2,500,11,2
4,6,1500,38,2
3,4,1000,29,2
9,9,2250,38,1
11,5,1250,18,1
2,3,750,21,1
2,1,250,2,1
2,1,250,2,2
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,2
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
2,1,250,2,1
11,11,2750,38,1
2,3,750,22,1
9,11,2750,49,2
5,11,2750,75,1
3,5,1250,38,1
3,1,250,3,2
4,6,1500,43,1
2,3,750,24,1
12,11,2750,39,1
2,2,500,14,1
4,6,1500,46,1
9,3,750,14,1
14,8,2000,26,1
4,2,500,13,1
4,11,2750,95,1
2,7,1750,77,1
2,7,1750,77,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,2
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,1
4,1,250,4,2
4,1,250,4,1
4,7,1750,62,1
4,1,250,4,1
4,4,1000,34,2
11,6,1500,28,1
13,3,750,14,2
7,5,1250,35,1
9,9,2250,54,1
11,2,500,11,1
2,5,1250,63,1
7,11,2750,89,1
8,9,2250,64,1
2,2,500,22,1
6,3,750,26,1
12,15,3750,71,1
13,3,750,16,1
11,16,4000,89,1
4,5,1250,58,1
14,7,1750,35,1
11,4,1000,27,1
7,9,2250,89,2
11,8,2000,52,2
7,5,1250,52,1
11,6,1500,41,1
10,5,1250,38,1
14,2,500,14,2
14,2,500,14,1
14,2,500,14,1
2,2,500,33,1
11,3,750,23,1
14,8,2000,46,1
9,1,250,9,1
16,5,1250,27,1
14,4,1000,26,1
4,2,500,30,1
14,3,750,21,1
16,16,4000,77,1
4,2,500,31,1
14,8,2000,50,1
11,3,750,26,1
14,7,1750,45,1
15,5,1250,33,1
16,2,500,16,1
16,3,750,21,1
11,8,2000,72,1
11,1,250,11,1
11,1,250,11,1
11,1,250,11,1
11,1,250,11,2
11,1,250,11,1
2,3,750,75,2
2,3,750,77,1
16,4,1000,28,1
16,15,3750,87,1
16,14,3500,83,1
16,10,2500,62,1
16,3,750,23,1
14,3,750,26,1
23,19,4750,62,1
11,7,1750,75,1
14,3,750,28,1
20,14,3500,69,2
4,2,500,46,1
11,2,500,25,1
11,3,750,37,1
16,4,1000,33,1
21,7,1750,38,1
13,7,1750,76,1
16,6,1500,50,1
14,3,750,33,1
14,1,250,14,1
14,1,250,14,1
14,1,250,14,1
14,1,250,14,1
14,1,250,14,1
14,1,250,14,1
17,7,1750,58,2
14,3,750,35,1
14,3,750,35,1
16,7,1750,64,1
21,2,500,21,1
16,3,750,35,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
16,1,250,16,1
14,2,500,29,1
11,4,1000,74,1
11,2,500,38,2
21,6,1500,48,1
23,2,500,23,1
23,6,1500,45,1
14,2,500,35,2
16,6,1500,81,1
16,4,1000,58,1
16,5,1250,71,1
21,2,500,26,1
21,3,750,35,1
21,3,750,35,1
23,8,2000,69,1
21,3,750,38,1
23,3,750,35,1
21,3,750,40,1
23,2,500,28,1
21,1,250,21,1
21,1,250,21,1
25,6,1500,50,1
21,1,250,21,1
21,1,250,21,1
23,3,750,39,1
21,2,500,33,1
14,3,750,79,1
23,1,250,23,2
23,1,250,23,1
23,1,250,23,1
23,1,250,23,1
23,1,250,23,1
23,1,250,23,1
23,1,250,23,1
23,4,1000,52,1
23,1,250,23,1
23,7,1750,88,1
16,3,750,86,1
23,2,500,38,1
21,2,500,52,1
23,3,750,62,1
39,1,250,39,1
72,1,250,72,1
================================================
FILE: public/viz/datarun1_gp.csv
================================================
trail ID,method,parameters,metrics,performance
0,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 7; weights = distance,f1 and cv,0.686 +- 0.463
1,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 14; weights = distance,f1 and cv,0.857 +- 0.229
2,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 14; weights = distance,f1 and cv,0.816 +- 0.261
3,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 14; weights = distance,f1 and cv,0.661 +- 0.372
4,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 13; p = 3; weights = distance,f1 and cv,0.619 +- 0.687
5,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 13; p = 1; weights = distance,f1 and cv,0.719 +- 0.477
6,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 13; p = 1; weights = distance,f1 and cv,0.721 +- 0.257
7,mlp,_scale = True; activation = logistic; alpha = 0.00530026476357; batch_size = auto; hidden_layer_sizes[0] = 195; hidden_layer_sizes[1] = 205; learning_rate = adaptive; learning_rate_init = 0.424197908329; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.533 +- 0.533
8,mlp,_scale = True; activation = logistic; alpha = 0.00798119444472; batch_size = auto; hidden_layer_sizes[0] = 216; hidden_layer_sizes[1] = 12; learning_rate = adaptive; learning_rate_init = 0.398794294569; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.895 +- 0.132
9,mlp,_scale = True; activation = logistic; alpha = 0.00379155634861; batch_size = auto; hidden_layer_sizes[0] = 172; hidden_layer_sizes[1] = 99; learning_rate = adaptive; learning_rate_init = 0.598925397734; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.431 +- 0.704
10,mlp,_scale = True; activation = identity; alpha = 0.00137991259211; batch_size = auto; hidden_layer_sizes[0] = 203; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.597 +- 0.515
11,mlp,_scale = True; activation = identity; alpha = 0.000932548884706; batch_size = auto; hidden_layer_sizes[0] = 155; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.641 +- 0.339
12,knn,_scale = True; algorithm = ball_tree; leaf_size = 11; metric = minkowski; n_neighbors = 1; p = 2; weights = distance,f1 and cv,0.737 +- 0.143
13,knn,_scale = True; algorithm = ball_tree; leaf_size = 24; metric = minkowski; n_neighbors = 11; p = 2; weights = distance,f1 and cv,0.790 +- 0.305
14,knn,_scale = True; algorithm = ball_tree; leaf_size = 24; metric = minkowski; n_neighbors = 11; p = 3; weights = distance,f1 and cv,0.658 +- 0.285
15,mlp,_scale = True; activation = logistic; alpha = 0.00689815348979; batch_size = auto; hidden_layer_sizes[0] = 57; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.728 +- 0.174
16,mlp,_scale = True; activation = logistic; alpha = 0.0039549442717; batch_size = auto; hidden_layer_sizes[0] = 259; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.753 +- 0.189
17,mlp,_scale = True; activation = logistic; alpha = 0.00281476836221; batch_size = auto; hidden_layer_sizes[0] = 259; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.696 +- 0.524
18,mlp,_scale = True; activation = relu; alpha = 0.0034843610708; batch_size = auto; hidden_layer_sizes[0] = 285; hidden_layer_sizes[1] = 123; hidden_layer_sizes[2] = 251; learning_rate = adaptive; learning_rate_init = 0.224074210427; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.795 +- 0.154
19,mlp,_scale = True; activation = relu; alpha = 0.00542506698297; batch_size = auto; hidden_layer_sizes[0] = 176; hidden_layer_sizes[1] = 80; hidden_layer_sizes[2] = 106; learning_rate = adaptive; learning_rate_init = 0.0211862121565; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.714 +- 0.211
20,mlp,_scale = True; activation = relu; alpha = 0.00842941088502; batch_size = auto; hidden_layer_sizes[0] = 245; hidden_layer_sizes[1] = 215; hidden_layer_sizes[2] = 215; learning_rate = adaptive; learning_rate_init = 0.841552109636; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.629 +- 0.098
21,mlp,_scale = True; activation = tanh; alpha = 0.00597255742769; batch_size = auto; hidden_layer_sizes[0] = 233; hidden_layer_sizes[1] = 118; learning_rate = constant; learning_rate_init = 0.931582061106; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.748 +- 0.310
22,mlp,_scale = True; activation = tanh; alpha = 0.00758679628786; batch_size = auto; hidden_layer_sizes[0] = 212; hidden_layer_sizes[1] = 159; learning_rate = constant; learning_rate_init = 0.511327574962; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.764 +- 0.172
23,mlp,_scale = True; activation = tanh; alpha = 0.00293353681982; batch_size = auto; hidden_layer_sizes[0] = 207; hidden_layer_sizes[1] = 154; learning_rate = constant; learning_rate_init = 0.599525801536; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.743 +- 0.285
24,mlp,_scale = True; activation = identity; alpha = 0.00757508457799; batch_size = auto; hidden_layer_sizes[0] = 169; learning_rate = adaptive; learning_rate_init = 0.788346166024; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.789 +- 0.312
25,mlp,_scale = True; activation = identity; alpha = 0.00787731013044; batch_size = auto; hidden_layer_sizes[0] = 11; learning_rate = adaptive; learning_rate_init = 0.643536374804; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.809 +- 0.134
26,mlp,_scale = True; activation = identity; alpha = 0.00333255752017; batch_size = auto; hidden_layer_sizes[0] = 11; learning_rate = adaptive; learning_rate_init = 0.261893435241; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.765 +- 0.164
27,mlp,_scale = True; activation = identity; alpha = 0.00716646901959; batch_size = auto; hidden_layer_sizes[0] = 187; hidden_layer_sizes[1] = 224; hidden_layer_sizes[2] = 160; learning_rate = constant; learning_rate_init = 0.204547246006; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.625 +- 0.265
28,mlp,_scale = True; activation = identity; alpha = 0.000774703195752; batch_size = auto; hidden_layer_sizes[0] = 99; hidden_layer_sizes[1] = 200; hidden_layer_sizes[2] = 210; learning_rate = constant; learning_rate_init = 0.596451207957; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.450 +- 0.621
29,knn,_scale = True; algorithm = kd_tree; leaf_size = 38; metric = euclidean; n_neighbors = 1; weights = uniform,f1 and cv,0.820 +- 0.167
30,knn,_scale = True; algorithm = kd_tree; leaf_size = 1; metric = euclidean; n_neighbors = 13; weights = uniform,f1 and cv,0.626 +- 0.642
31,rf,criterion = gini; max_depth = 9; max_features = 0.367839067747; min_samples_leaf = 3; n_estimators = 100; n_jobs = -1,f1 and cv,0.785 +- 0.306
32,rf,criterion = gini; max_depth = 9; max_features = 0.402117977448; min_samples_leaf = 1; n_estimators = 100; n_jobs = -1,f1 and cv,0.860 +- 0.074
33,rf,criterion = gini; max_depth = 9; max_features = 0.382966272577; min_samples_leaf = 1; n_estimators = 100; n_jobs = -1,f1 and cv,0.752 +- 0.309
34,mlp,_scale = True; activation = logistic; alpha = 0.000791933609875; batch_size = auto; hidden_layer_sizes[0] = 100; learning_rate = invscaling; learning_rate_init = 0.630083279272; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.593 +- 0.665
35,mlp,_scale = True; activation = logistic; alpha = 0.00866503894322; batch_size = auto; hidden_layer_sizes[0] = 72; learning_rate = invscaling; learning_rate_init = 0.464195768863; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.612 +- 0.660
36,logreg,C = 0.0214259326819; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 0.000345805380725,f1 and cv,0.833 +- 0.230
37,logreg,C = 0.683408146675; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 1214.1761078,f1 and cv,0.752 +- 0.372
38,logreg,C = 0.017734143058; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 0.00123640541548,f1 and cv,0.683 +- 0.356
39,mlp,_scale = True; activation = logistic; alpha = 0.00888164070317; batch_size = auto; hidden_layer_sizes[0] = 102; learning_rate = constant; learning_rate_init = 0.00430280495011; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.750 +- 0.276
40,mlp,_scale = True; activation = logistic; alpha = 0.00476397875238; batch_size = auto; hidden_layer_sizes[0] = 16; learning_rate = constant; learning_rate_init = 0.883876615927; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.861 +- 0.268
41,mlp,_scale = True; activation = logistic; alpha = 0.00733166639002; batch_size = auto; hidden_layer_sizes[0] = 16; learning_rate = constant; learning_rate_init = 0.444678703048; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.711 +- 0.178
42,knn,_scale = True; algorithm = kd_tree; leaf_size = 29; metric = minkowski; n_neighbors = 9; p = 2; weights = uniform,f1 and cv,0.735 +- 0.230
43,knn,_scale = True; algorithm = kd_tree; leaf_size = 19; metric = minkowski; n_neighbors = 15; p = 3; weights = uniform,f1 and cv,0.617 +- 0.484
44,knn,_scale = True; algorithm = kd_tree; leaf_size = 32; metric = chebyshev; n_neighbors = 1; weights = uniform,f1 and cv,0.598 +- 0.286
45,knn,_scale = True; algorithm = kd_tree; leaf_size = 31; metric = chebyshev; n_neighbors = 4; weights = uniform,f1 and cv,0.626 +- 0.322
46,mlp,_scale = True; activation = relu; alpha = 0.00759969817544; batch_size = auto; beta_1 = 0.805257558344; beta_2 = 0.919639253787; hidden_layer_sizes[0] = 271; hidden_layer_sizes[1] = 156; hidden_layer_sizes[2] = 102; learning_rate_init = 0.538063857136; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.478 +- 0.556
47,mlp,_scale = True; activation = relu; alpha = 0.00183811745554; batch_size = auto; beta_1 = 0.917281907651; beta_2 = 0.909306026217; hidden_layer_sizes[0] = 94; hidden_layer_sizes[1] = 4; hidden_layer_sizes[2] = 41; learning_rate_init = 0.0100400664162; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.698 +- 0.344
48,mlp,_scale = True; activation = relu; alpha = 0.0047834096605; batch_size = auto; hidden_layer_sizes[0] = 238; hidden_layer_sizes[1] = 101; learning_rate = constant; learning_rate_init = 0.488408572136; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.705 +- 0.415
49,mlp,_scale = True; activation = relu; alpha = 0.00710954274516; batch_size = auto; hidden_layer_sizes[0] = 14; hidden_layer_sizes[1] = 230; learning_rate = constant; learning_rate_init = 0.786669723308; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.756 +- 0.196
50,rf,criterion = entropy; max_depth = 6; max_features = 0.232233143849; min_samples_leaf = 1; n_estimators = 100; n_jobs = -1,f1 and cv,0.731 +- 0.292
51,rf,criterion = entropy; max_depth = 5; max_features = 0.59182882637; min_samples_leaf = 2; n_estimators = 100; n_jobs = -1,f1 and cv,0.680 +- 0.412
52,mlp,_scale = True; activation = logistic; alpha = 0.00629100817992; batch_size = auto; beta_1 = 0.809151964451; beta_2 = 0.993608194363; hidden_layer_sizes[0] = 256; learning_rate_init = 0.612522328836; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.513 +- 0.515
53,mlp,_scale = True; activation = logistic; alpha = 0.00102623664838; batch_size = auto; beta_1 = 0.990889730404; beta_2 = 0.873016441774; hidden_layer_sizes[0] = 155; learning_rate_init = 0.468791139345; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.385 +- 0.639
54,mlp,_scale = True; activation = tanh; alpha = 0.0060745624063; batch_size = auto; beta_1 = 0.810455090006; beta_2 = 0.86233389184; hidden_layer_sizes[0] = 166; learning_rate_init = 0.506612828834; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.754 +- 0.015
55,mlp,_scale = True; activation = tanh; alpha = 0.00642222331207; batch_size = auto; beta_1 = 0.950661050707; beta_2 = 0.9228862104; hidden_layer_sizes[0] = 232; learning_rate_init = 0.737945109017; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.623 +- 0.382
56,mlp,_scale = True; activation = logistic; alpha = 0.00221486648488; batch_size = auto; hidden_layer_sizes[0] = 229; hidden_layer_sizes[1] = 24; learning_rate = invscaling; learning_rate_init = 0.725149199927; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.618 +- 0.634
57,mlp,_scale = True; activation = logistic; alpha = 0.00490142271385; batch_size = auto; hidden_layer_sizes[0] = 152; hidden_layer_sizes[1] = 258; learning_rate = invscaling; learning_rate_init = 0.450398857861; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.565 +- 0.566
58,gnb,_scale_minmax = True,f1 and cv,0.734 +- 0.174
59,mlp,_scale = True; activation = relu; alpha = 0.00732790324552; batch_size = auto; beta_1 = 0.913017407091; beta_2 = 0.873413950401; hidden_layer_sizes[0] = 198; hidden_layer_sizes[1] = 119; learning_rate_init = 0.299263258228; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.357 +- 0.588
60,mlp,_scale = True; activation = relu; alpha = 0.00437532628446; batch_size = auto; beta_1 = 0.856779201746; beta_2 = 0.905041477573; hidden_layer_sizes[0] = 254; hidden_layer_sizes[1] = 281; learning_rate_init = 0.317421025284; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.514 +- 0.520
61,mlp,_scale = True; activation = tanh; alpha = 0.00808218337356; batch_size = auto; beta_1 = 0.813646861773; beta_2 = 0.926051015685; hidden_layer_sizes[0] = 223; hidden_layer_sizes[1] = 79; learning_rate_init = 0.681626093158; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.645 +- 0.153
62,mlp,_scale = True; activation = tanh; alpha = 0.00275617054662; batch_size = auto; beta_1 = 0.977371192532; beta_2 = 0.950555116359; hidden_layer_sizes[0] = 246; hidden_layer_sizes[1] = 147; learning_rate_init = 0.41924593034; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.697 +- 0.307
63,mlp,_scale = True; activation = identity; alpha = 0.00197176250179; batch_size = auto; hidden_layer_sizes[0] = 120; learning_rate = invscaling; learning_rate_init = 0.785634918376; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.704 +- 0.399
64,mlp,_scale = True; activation = identity; alpha = 0.00180158765636; batch_size = auto; hidden_layer_sizes[0] = 145; learning_rate = invscaling; learning_rate_init = 0.552198889363; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.765 +- 0.300
65,logreg,C = 47093.7014376; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 15.325529229,f1 and cv,0.702 +- 0.278
66,logreg,C = 0.443155342509; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 0.000101144238412,f1 and cv,0.677 +- 0.489
67,mlp,_scale = True; activation = identity; alpha = 0.00367494681335; batch_size = auto; hidden_layer_sizes[0] = 142; hidden_layer_sizes[1] = 237; hidden_layer_sizes[2] = 158; learning_rate = adaptive; learning_rate_init = 0.311947801595; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.705 +- 0.211
68,mlp,_scale = True; activation = identity; alpha = 0.00010321608489; batch_size = auto; hidden_layer_sizes[0] = 159; hidden_layer_sizes[1] = 75; hidden_layer_sizes[2] = 73; learning_rate = adaptive; learning_rate_init = 0.704449459858; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.000 +- 0.000
69,mlp,_scale = True; activation = logistic; alpha = 0.00658761698968; batch_size = auto; hidden_layer_sizes[0] = 99; hidden_layer_sizes[1] = 152; hidden_layer_sizes[2] = 113; learning_rate = constant; learning_rate_init = 0.658043918401; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.243 +- 0.595
70,mlp,_scale = True; activation = logistic; alpha = 0.00712192156559; batch_size = auto; hidden_layer_sizes[0] = 277; hidden_layer_sizes[1] = 271; hidden_layer_sizes[2] = 158; learning_rate = constant; learning_rate_init = 0.128920390882; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.728 +- 0.043
71,mlp,_scale = True; activation = relu; alpha = 0.00140375540345; batch_size = auto; hidden_layer_sizes[0] = 78; hidden_layer_sizes[1] = 201; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.727 +- 0.364
72,mlp,_scale = True; activation = relu; alpha = 0.00649551585093; batch_size = auto; hidden_layer_sizes[0] = 247; hidden_layer_sizes[1] = 183; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.703 +- 0.339
73,logreg,C = 0.0013761076145; _scale = True; class_weight = balanced; dual = False; fit_intercept = True; penalty = l2; tol = 1.41262043531,f1 and cv,0.758 +- 0.188
74,logreg,C = 9303.15817441; _scale = True; class_weight = balanced; dual = False; fit_intercept = True; penalty = l2; tol = 0.000334662940477,f1 and cv,0.552 +- 0.386
75,mlp,_scale = True; activation = relu; alpha = 0.00311675023279; batch_size = auto; hidden_layer_sizes[0] = 202; learning_rate = adaptive; learning_rate_init = 0.504088851907; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.703 +- 0.321
76,mlp,_scale = True; activation = relu; alpha = 0.00494765202385; batch_size = auto; hidden_layer_sizes[0] = 212; learning_rate = adaptive; learning_rate_init = 0.140358550945; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.804 +- 0.225
77,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 18; weights = distance,f1 and cv,0.686 +- 0.187
78,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 3; weights = distance,f1 and cv,0.875 +- 0.255
79,mlp,_scale = True; activation = logistic; alpha = 0.00631435652909; batch_size = auto; hidden_layer_sizes[0] = 87; hidden_layer_sizes[1] = 262; hidden_layer_sizes[2] = 57; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.749 +- 0.203
80,mlp,_scale = True; activation = logistic; alpha = 0.00491522611475; batch_size = auto; hidden_layer_sizes[0] = 243; hidden_layer_sizes[1] = 84; hidden_layer_sizes[2] = 207; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.690 +- 0.318
81,dt,criterion = gini; max_depth = 5; max_features = 0.26928275228; min_samples_leaf = 2; min_samples_split = 3,f1 and cv,0.782 +- 0.341
82,dt,criterion = gini; max_depth = 4; max_features = 0.350580615226; min_samples_leaf = 2; min_samples_split = 3,f1 and cv,0.743 +- 0.086
83,mlp,_scale = True; activation = logistic; alpha = 0.00555540554099; batch_size = auto; hidden_layer_sizes[0] = 268; hidden_layer_sizes[1] = 88; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.845 +- 0.253
84,mlp,_scale = True; activation = logistic; alpha = 0.00830150899829; batch_size = auto; hidden_layer_sizes[0] = 106; hidden_layer_sizes[1] = 254; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.606 +- 0.358
85,knn,_scale = True; algorithm = ball_tree; leaf_size = 47; metric = chebyshev; n_neighbors = 5; weights = distance,f1 and cv,0.679 +- 0.269
86,knn,_scale = True; algorithm = ball_tree; leaf_size = 49; metric = chebyshev; n_neighbors = 8; weights = distance,f1 and cv,0.566 +- 0.376
87,mlp,_scale = True; activation = relu; alpha = 0.00134657693167; batch_size = auto; beta_1 = 0.901446054733; beta_2 = 0.880896299544; hidden_layer_sizes[0] = 191; learning_rate_init = 0.375569538389; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.632 +- 0.525
88,mlp,_scale = True; activation = relu; alpha = 0.000297485001862; batch_size = auto; beta_1 = 0.941478867427; beta_2 = 0.973162866089; hidden_layer_sizes[0] = 69; learning_rate_init = 0.533560098352; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.860 +- 0.217
89,mlp,_scale = True; activation = tanh; alpha = 0.005287015641; batch_size = auto; hidden_layer_sizes[0] = 182; learning_rate = constant; learning_rate_init = 0.163780650802; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.729 +- 0.065
90,mlp,_scale = True; activation = tanh; alpha = 0.00456308654567; batch_size = auto; hidden_layer_sizes[0] = 127; learning_rate = constant; learning_rate_init = 0.135323349646; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.826 +- 0.249
91,gp,kernel = exp_sine_squared; length_scale = 4.85831821276; periodicity = 0,f1 and cv,0.264 +- 0.657
92,gp,kernel = exp_sine_squared; length_scale = 0.135958947829; periodicity = 0,f1 and cv,0.509 +- 0.491
93,mlp,_scale = True; activation = identity; alpha = 0.00327852610934; batch_size = auto; hidden_layer_sizes[0] = 47; hidden_layer_sizes[1] = 186; learning_rate = adaptive; learning_rate_init = 0.572603383057; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.814 +- 0.184
94,mlp,_scale = True; activation = identity; alpha = 0.00459711119933; batch_size = auto; hidden_layer_sizes[0] = 57; hidden_layer_sizes[1] = 244; learning_rate = adaptive; learning_rate_init = 0.183884865397; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.717 +- 0.082
95,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 15; weights = uniform,f1 and cv,0.748 +- 0.302
96,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 14; weights = uniform,f1 and cv,0.781 +- 0.123
97,mlp,_scale = True; activation = identity; alpha = 0.0074516368895; batch_size = auto; hidden_layer_sizes[0] = 260; hidden_layer_sizes[1] = 272; learning_rate = constant; learning_rate_init = 0.814031098014; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.740 +- 0.568
98,mlp,_scale = True; activation = identity; alpha = 0.000714089726547; batch_size = auto; hidden_layer_sizes[0] = 213; hidden_layer_sizes[1] = 198; learning_rate = constant; learning_rate_init = 0.814268876798; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.705 +- 0.160
99,knn,_scale = True; algorithm = kd_tree; leaf_size = 34; metric = minkowski; n_neighbors = 20; p = 2; weights = distance,f1 and cv,0.636 +- 0.695
100,knn,_scale = True; algorithm = kd_tree; leaf_size = 2; metric = minkowski; n_neighbors = 3; p = 2; weights = distance,f1 and cv,0.695 +- 0.320
101,gp,kernel = exp_sine_squared; length_scale = 0.0418500347903; periodicity = 1,f1 and cv,0.518 +- 0.349
102,gp,kernel = exp_sine_squared; length_scale = 3.2608797321; periodicity = 1,f1 and cv,0.636 +- 0.138
103,knn,_scale = True; algorithm = ball_tree; leaf_size = 39; metric = manhattan; n_neighbors = 14; weights = distance,f1 and cv,0.734 +- 0.174
104,knn,_scale = True; algorithm = ball_tree; leaf_size = 12; metric = manhattan; n_neighbors = 8; weights = distance,f1 and cv,0.735 +- 0.329
105,gp,kernel = rbf,f1 and cv,0.000 +- 0.000
106,logreg,C = 8003.09009751; _scale = True; class_weight = balanced; dual = False; fit_intercept = False; penalty = l2; tol = 158.3290079,f1 and cv,0.000 +- 0.000
107,knn,_scale = True; algorithm = kd_tree; leaf_size = 39; metric = manhattan; n_neighbors = 5; weights = distance,f1 and cv,0.763 +- 0.174
108,knn,_scale = True; algorithm = kd_tree; leaf_size = 19; metric = manhattan; n_neighbors = 1; weights = distance,f1 and cv,0.750 +- 0.390
109,mlp,_scale = True; activation = tanh; alpha = 0.00825577378371; batch_size = auto; beta_1 = 0.985892130227; beta_2 = 0.828672288718; hidden_layer_sizes[0] = 30; hidden_layer_sizes[1] = 97; hidden_layer_sizes[2] = 21; learning_rate_init = 0.681012686376; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.786 +- 0.214
110,mlp,_scale = True; activation = tanh; alpha = 0.00649915984678; batch_size = auto; beta_1 = 0.982349328285; beta_2 = 0.873896270168; hidden_layer_sizes[0] = 180; hidden_layer_sizes[1] = 113; hidden_layer_sizes[2] = 273; learning_rate_init = 0.244111027344; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.647 +- 0.323
111,mlp,_scale = True; activation = tanh; alpha = 0.00629250795; batch_size = auto; hidden_layer_sizes[0] = 153; hidden_layer_sizes[1] = 56; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.906 +- 0.185
112,mlp,_scale = True; activation = tanh; alpha = 0.00814064893138; batch_size = auto; hidden_layer_sizes[0] = 296; hidden_layer_sizes[1] = 246; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.648 +- 0.198
113,mlp,_scale = True; activation = tanh; alpha = 0.0054920617902; batch_size = auto; hidden_layer_sizes[0] = 201; hidden_layer_sizes[1] = 184; learning_rate = adaptive; learning_rate_init = 0.266207557118; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.663 +- 0.331
114,mlp,_scale = True; activation = tanh; alpha = 0.000210532215542; batch_size = auto; hidden_layer_sizes[0] = 84; hidden_layer_sizes[1] = 27; learning_rate = adaptive; learning_rate_init = 0.133819357352; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.758 +- 0.196
115,mlp,_scale = True; activation = logistic; alpha = 0.00749068366409; batch_size = auto; hidden_layer_sizes[0] = 24; learning_rate = adaptive; learning_rate_init = 0.444646403945; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.567 +- 0.267
116,mlp,_scale = True; activation = logistic; alpha = 0.00845358643974; batch_size = auto; hidden_layer_sizes[0] = 142; learning_rate = adaptive; learning_rate_init = 0.925708350382; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.740 +- 0.223
117,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 15; weights = uniform,f1 and cv,0.728 +- 0.191
118,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 18; weights = uniform,f1 and cv,0.660 +- 0.282
119,mlp,_scale = True; activation = identity; alpha = 0.00234311244437; batch_size = auto; hidden_layer_sizes[0] = 145; learning_rate = constant; learning_rate_init = 0.141410353023; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.720 +- 0.160
120,mlp,_scale = True; activation = identity; alpha = 0.00665717297745; batch_size = auto; hidden_layer_sizes[0] = 45; learning_rate = constant; learning_rate_init = 0.25076141269; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.741 +- 0.181
121,mlp,_scale = True; activation = logistic; alpha = 0.00875211514426; batch_size = auto; beta_1 = 0.863778890054; beta_2 = 0.883855308382; hidden_layer_sizes[0] = 209; hidden_layer_sizes[1] = 195; learning_rate_init = 0.0853683569178; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.000 +- 0.000
122,mlp,_scale = True; activation = logistic; alpha = 0.00293197513824; batch_size = auto; hidden_layer_sizes[0] = 43; hidden_layer_sizes[1] = 208; hidden_layer_sizes[2] = 229; learning_rate = adaptive; learning_rate_init = 0.649397583034; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.400 +- 0.653
123,mlp,_scale = True; activation = logistic; alpha = 0.00681985969241; batch_size = auto; hidden_layer_sizes[0] = 14; hidden_layer_sizes[1] = 30; hidden_layer_sizes[2] = 174; learning_rate = adaptive; learning_rate_init = 0.10778253764; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.000 +- 0.000
124,svm,C = 8.16055297671; _scale = True; cache_size = 15000; class_weight = balanced; gamma = 147.068791847; kernel = rbf; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.747 +- 0.054
125,svm,C = 0.0089089646207; _scale = True; cache_size = 15000; class_weight = balanced; gamma = 253.344779981; kernel = rbf; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.646 +- 0.050
126,mnb,_scale_minmax = True; alpha = 0.902316081541; class_prior = None; fit_prior = 0,f1 and cv,0.659 +- 0.427
127,mnb,_scale_minmax = True; alpha = 0.710780560312; class_prior = None; fit_prior = 0,f1 and cv,0.694 +- 0.353
128,mlp,_scale = True; activation = tanh; alpha = 0.00480760064499; batch_size = auto; hidden_layer_sizes[0] = 67; learning_rate = adaptive; learning_rate_init = 0.333529348373; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.695 +- 0.480
129,mlp,_scale = True; activation = tanh; alpha = 0.0085398433589; batch_size = auto; hidden_layer_sizes[0] = 252; learning_rate = adaptive; learning_rate_init = 0.531057244459; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.575 +- 0.498
130,svm,C = 0.488485724028; _scale = True; cache_size = 15000; class_weight = balanced; coef0 = 808; gamma = 1.48944568797e-05; kernel = sigmoid; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.400 +- 0.653
131,svm,C = 12.6587418859; _scale = True; cache_size = 15000; class_weight = balanced; coef0 = -239; gamma = 663.160595527; kernel = sigmoid; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.694 +- 0.253
132,mlp,_scale = True; activation = identity; alpha = 0.00378946917625; batch_size = auto; hidden_layer_sizes[0] = 248; hidden_layer_sizes[1] = 219; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.718 +- 0.176
133,mlp,_scale = True; activation = identity; alpha = 0.00171384081042; batch_size = auto; hidden_layer_sizes[0] = 194; hidden_layer_sizes[1] = 244; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.677 +- 0.204
134,gp,kernel = matern; nu = 0.5,f1 and cv,0.417 +- 0.230
135,mlp,_scale = True; activation = tanh; alpha = 0.00396164402352; batch_size = auto; hidden_layer_sizes[0] = 253; hidden_layer_sizes[1] = 154; hidden_layer_sizes[2] = 216; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.763 +- 0.524
136,mlp,_scale = True; activation = tanh; alpha = 0.00726596359904; batch_size = auto; hidden_layer_sizes[0] = 200; hidden_layer_sizes[1] = 28; hidden_layer_sizes[2] = 205; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.725 +- 0.274
137,knn,_scale = True; algorithm = ball_tree; leaf_size = 7; metric = chebyshev; n_neighbors = 12; weights = uniform,f1 and cv,0.558 +- 0.281
138,knn,_scale = True; algorithm = ball_tree; leaf_size = 33; metric = chebyshev; n_neighbors = 15; weights = uniform,f1 and cv,0.706 +- 0.178
139,mlp,_scale = True; activation = relu; alpha = 0.00267022655552; batch_size = auto; hidden_layer_sizes[0] = 162; learning_rate = constant; learning_rate_init = 0.329554709582; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.741 +- 0.421
140,mlp,_scale = True; activation = relu; alpha = 0.000120285567481; batch_size = auto; hidden_layer_sizes[0] = 94; learning_rate = constant; learning_rate_init = 0.287889175922; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.773 +- 0.157
141,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 13; p = 1; weights = uniform,f1 and cv,0.789 +- 0.280
142,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 19; p = 2; weights = uniform,f1 and cv,0.708 +- 0.161
143,mlp,_scale = True; activation = tanh; alpha = 0.00364601841439; batch_size = auto; hidden_layer_sizes[0] = 172; hidden_layer_sizes[1] = 88; hidden_layer_sizes[2] = 134; learning_rate = constant; learning_rate_init = 0.930415677006; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.558 +- 0.645
144,mlp,_scale = True; activation = tanh; alpha = 0.000723907193495; batch_size = auto; hidden_layer_sizes[0] = 25; hidden_layer_sizes[1] = 248; hidden_layer_sizes[2] = 290; learning_rate = constant; learning_rate_init = 0.449709456131; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.781 +- 0.285
145,mlp,_scale = True; activation = tanh; alpha = 0.00615973608702; batch_size = auto; hidden_layer_sizes[0] = 229; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.707 +- 0.373
146,mlp,_scale = True; activation = tanh; alpha = 0.00342215452488; batch_size = auto; hidden_layer_sizes[0] = 291; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.788 +- 0.371
147,logreg,C = 33236.2651831; _scale = True; class_weight = balanced; fit_intercept = False; penalty = l1; tol = 45143.8756661,f1 and cv,0.000 +- 0.000
148,mlp,_scale = True; activation = relu; alpha = 0.00733763196925; batch_size = auto; hidden_layer_sizes[0] = 84; hidden_layer_sizes[1] = 193; hidden_layer_sizes[2] = 23; learning_rate = constant; learning_rate_init = 0.149237023614; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.689 +- 0.208
149,mlp,_scale = True; activation = relu; alpha = 0.00777580626777; batch_size = auto; hidden_layer_sizes[0] = 108; hidden_layer_sizes[1] = 205; hidden_layer_sizes[2] = 52; learning_rate = constant; learning_rate_init = 0.529978231905; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.728 +- 0.218
150,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 19; weights = distance,f1 and cv,0.619 +- 0.197
151,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 6; weights = distance,f1 and cv,0.532 +- 0.378
152,mlp,_scale = True; activation = logistic; alpha = 0.000567528746084; batch_size = auto; hidden_layer_sizes[0] = 150; hidden_layer_sizes[1] = 264; learning_rate = constant; learning_rate_init = 0.0846532674358; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.133 +- 0.533
153,mlp,_scale = True; activation = logistic; alpha = 0.00239891080963; batch_size = auto; hidden_layer_sizes[0] = 201; hidden_layer_sizes[1] = 46; learning_rate = constant; learning_rate_init = 0.426161179321; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.422 +- 0.690
154,mlp,_scale = True; activation = tanh; alpha = 0.00827701278597; batch_size = auto; hidden_layer_sizes[0] = 197; hidden_layer_sizes[1] = 97; learning_rate = invscaling; learning_rate_init = 0.977395089155; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.798 +- 0.217
155,mlp,_scale = True; activation = tanh; alpha = 0.00235795231137; batch_size = auto; hidden_layer_sizes[0] = 120; hidden_layer_sizes[1] = 166; learning_rate = invscaling; learning_rate_init = 0.194546202206; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.660 +- 0.338
156,mlp,_scale = True; activation = identity; alpha = 0.00781093580274; batch_size = auto; beta_1 = 0.893500772757; beta_2 = 0.847286576572; hidden_layer_sizes[0] = 14; hidden_layer_sizes[1] = 247; learning_rate_init = 0.758696518557; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.762 +- 0.097
157,mlp,_scale = True; activation = identity; alpha = 0.00567418712325; batch_size = auto; beta_1 = 0.96818040483; beta_2 = 0.97953775169; hidden_layer_sizes[0] = 126; hidden_layer_sizes[1] = 258; learning_rate_init = 0.341003076312; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.533 +- 0.573
158,mlp,_scale = True; activation = relu; alpha = 0.00162868600501; batch_size = auto; hidden_layer_sizes[0] = 144; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.754 +- 0.425
159,mlp,_scale = True; activation = relu; alpha = 0.00229409998765; batch_size = auto; hidden_layer_sizes[0] = 298; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.829 +- 0.264
160,mlp,_scale = True; activation = identity; alpha = 0.00864326826731; batch_size = auto; beta_1 = 0.942795630859; beta_2 = 0.947817974492; hidden_layer_sizes[0] = 237; hidden_layer_sizes[1] = 4; hidden_layer_sizes[2] = 135; learning_rate_init = 0.887629148002; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.733 +- 0.164
161,mlp,_scale = True; activation = identity; alpha = 0.00853544959721; batch_size = auto; beta_1 = 0.910177581959; beta_2 = 0.954639833114; hidden_layer_sizes[0] = 60; hidden_layer_sizes[1] = 167; hidden_layer_sizes[2] = 135; learning_rate_init = 0.248072259148; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.631 +- 0.711
162,mlp,_scale = True; activation = logistic; alpha = 0.000935512363926; batch_size = auto; hidden_layer_sizes[0] = 69; hidden_layer_sizes[1] = 257; hidden_layer_sizes[2] = 173; learning_rate = invscaling; learning_rate_init = 0.643384098013; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.562 +- 0.563
163,mlp,_scale = True; activation = logistic; alpha = 0.00613288968392; batch_size = auto; hidden_layer_sizes[0] = 50; hidden_layer_sizes[1] = 14; hidden_layer_sizes[2] = 259; learning_rate = invscaling; learning_rate_init = 0.886220190632; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.133 +- 0.533
164,mlp,_scale = True; activation = relu; alpha = 0.00458676425967; batch_size = auto; hidden_layer_sizes[0] = 29; hidden_layer_sizes[1] = 85; learning_rate = adaptive; learning_rate_init = 0.387754092947; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.771 +- 0.272
165,mlp,_scale = True; activation = relu; alpha = 0.00334926155067; batch_size = auto; hidden_layer_sizes[0] = 42; hidden_layer_sizes[1] = 200; learning_rate = adaptive; learning_rate_init = 0.540543347995; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.707 +- 0.299
166,knn,_scale = True; algorithm = ball_tree; leaf_size = 12; metric = euclidean; n_neighbors = 20; weights = uniform,f1 and cv,0.646 +- 0.371
167,knn,_scale = True; algorithm = ball_tree; leaf_size = 36; metric = euclidean; n_neighbors = 20; weights = uniform,f1 and cv,0.773 +- 0.170
168,mlp,_scale = True; activation = relu; alpha = 0.00719711014803; batch_size = auto; hidden_layer_sizes[0] = 8; hidden_layer_sizes[1] = 221; hidden_layer_sizes[2] = 194; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.692 +- 0.424
169,mlp,_scale = True; activation = relu; alpha = 0.00887638888759; batch_size = auto; hidden_layer_sizes[0] = 12; hidden_layer_sizes[1] = 299; hidden_layer_sizes[2] = 127; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.650 +- 0.380
170,mlp,_scale = True; activation = tanh; alpha = 0.00614178050714; batch_size = auto; hidden_layer_sizes[0] = 61; hidden_layer_sizes[1] = 225; hidden_layer_sizes[2] = 43; learning_rate = adaptive; learning_rate_init = 0.494382591683; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.714 +- 0.199
171,mlp,_scale = True; activation = tanh; alpha = 0.00738872825514; batch_size = auto; hidden_layer_sizes[0] = 11; hidden_layer_sizes[1] = 145; hidden_layer_sizes[2] = 65; learning_rate = adaptive; learning_rate_init = 0.313803037636; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.838 +- 0.191
172,mlp,_scale = True; activation = relu; alpha = 0.00167916081958; batch_size = auto; hidden_layer_sizes[0] = 120; hidden_layer_sizes[1] = 121; learning_rate = invscaling; learning_rate_init = 0.958818724407; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.667 +- 0.183
173,mlp,_scale = True; activation = relu; alpha = 0.0020100221907; batch_size = auto; hidden_layer_sizes[0] = 84; hidden_layer_sizes[1] = 297; learning_rate = invscaling; learning_rate_init = 0.952122922462; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.659 +- 0.327
174,gp,kernel = matern; nu = 2.5,f1 and cv,0.626 +- 0.144
175,knn,_scale = True; algorithm = kd_tree; leaf_size = 49; metric = manhattan; n_neighbors = 20; weights = uniform,f1 and cv,0.821 +- 0.158
176,knn,_scale = True; algorithm = kd_tree; leaf_size = 5; metric = manhattan; n_neighbors = 7; weights = uniform,f1 and cv,0.803 +- 0.179
177,mlp,_scale = True; activation = logistic; alpha = 0.000158711821583; batch_size = auto; beta_1 = 0.840424693096; beta_2 = 0.878668041576; hidden_layer_sizes[0] = 4; hidden_layer_sizes[1] = 50; hidden_layer_sizes[2] = 129; learning_rate_init = 0.00585355090471; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.533 +- 0.533
178,mlp,_scale = True; activation = logistic; alpha = 0.00201291230505; batch_size = auto; beta_1 = 0.822720823925; beta_2 = 0.994438957081; hidden_layer_sizes[0] = 106; hidden_layer_sizes[1] = 288; hidden_layer_sizes[2] = 154; learning_rate_init = 0.97642751098; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.276 +- 0.677
179,knn,_scale = True; algorithm = kd_tree; leaf_size = 13; metric = euclidean; n_neighbors = 6; weights = distance,f1 and cv,0.805 +- 0.236
180,knn,_scale = True; algorithm = kd_tree; leaf_size = 25; metric = euclidean; n_neighbors = 15; weights = distance,f1 and cv,0.603 +- 0.371
181,mlp,_scale = True; activation = relu; alpha = 0.00445180469685; batch_size = auto; hidden_layer_sizes[0] = 300; hidden_layer_sizes[1] = 202; hidden_layer_sizes[2] = 109; learning_rate = invscaling; learning_rate_init = 0.737533695192; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.831 +- 0.221
182,mlp,_scale = True; activation = relu; alpha = 0.00221169830469; batch_size = auto; hidden_layer_sizes[0] = 300; hidden_layer_sizes[1] = 82; hidden_layer_sizes[2] = 246; learning_rate = invscaling; learning_rate_init = 0.745125016274; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.691 +- 0.341
183,gp,alpha = 0.939043411847; kernel = rational_quadratic; length_scale = 0.215067015461,f1 and cv,0.607 +- 0.122
184,gp,alpha = 0.674130544672; kernel = rational_quadratic; length_scale = 0.0475666943118,f1 and cv,0.688 +- 0.187
185,mlp,_scale = True; activation = identity; alpha = 0.00644730586978; batch_size = auto; beta_1 = 0.882156870467; beta_2 = 0.802764529928; hidden_layer_sizes[0] = 14; learning_rate_init = 0.931185756048; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.789 +- 0.361
186,mlp,_scale = True; activation = identity; alpha = 0.00582384603658; batch_size = auto; beta_1 = 0.817626145533; beta_2 = 0.868733905202; hidden_layer_sizes[0] = 45; learning_rate_init = 0.424573756977; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.798 +- 0.260
187,knn,_scale = True; algorithm = ball_tree; leaf_size = 7; metric = euclidean; n_neighbors = 16; weights = distance,f1 and cv,0.735 +- 0.402
188,knn,_scale = True; algorithm = ball_tree; leaf_size = 22; metric = euclidean; n_neighbors = 8; weights = distance,f1 and cv,0.849 +- 0.132
189,svm,C = 0.0598810328026; _scale = True; cache_size = 15000; class_weight = balanced; kernel = linear; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.849 +- 0.188
190,svm,C = 0.144754612317; _scale = True; cache_size = 15000; class_weight = balanced; kernel = linear; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.764 +- 0.238
191,mlp,_scale = True; activation = logistic; alpha = 0.00558989678671; batch_size = auto; beta_1 = 0.819286301002; beta_2 = 0.909992491242; hidden_layer_sizes[0] = 240; hidden_layer_sizes[1] = 223; learning_rate_init = 0.330182353029; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.436 +- 0.717
192,dt,criterion = entropy; max_depth = 7; max_features = 0.163511255284; min_samples_leaf = 2; min_samples_split = 4,f1 and cv,0.567 +- 0.653
193,dt,criterion = entropy; max_depth = 6; max_features = 0.30231712356; min_samples_leaf = 1; min_samples_split = 4,f1 and cv,0.737 +- 0.374
194,knn,_scale = True; algorithm = ball_tree; leaf_size = 34; metric = manhattan; n_neighbors = 2; weights = uniform,f1 and cv,0.709 +- 0.313
195,knn,_scale = True; algorithm = ball_tree; leaf_size = 11; metric = manhattan; n_neighbors = 2; weights = uniform,f1 and cv,0.748 +- 0.149
196,mlp,_scale = True; activation = identity; alpha = 0.00121727411285; batch_size = auto; hidden_layer_sizes[0] = 12; hidden_layer_sizes[1] = 64; hidden_layer_sizes[2] = 209; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.790 +- 0.232
197,mlp,_scale = True; activation = identity; alpha = 0.00557906441868; batch_size = auto; hidden_layer_sizes[0] = 102; hidden_layer_sizes[1] = 297; hidden_layer_sizes[2] = 168; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.710 +- 0.247
198,knn,_scale = True; algorithm = kd_tree; leaf_size = 23; metric = chebyshev; n_neighbors = 8; weights = distance,f1 and cv,0.509 +- 0.833
199,knn,_scale = True; algorithm = kd_tree; leaf_size = 38; metric = chebyshev; n_neighbors = 14; weights = distance,f1 and cv,0.716 +- 0.129
================================================
FILE: public/viz/datarun1_grid.csv
================================================
trail ID,method,parameters,metrics,performance
0,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 13; weights = distance,f1 and cv,0.749 +- 0.464
1,knn,_scale = True; algorithm = ball_tree; leaf_size = 38; metric = euclidean; n_neighbors = 10; weights = distance,f1 and cv,0.704 +- 0.265
2,knn,_scale = True; algorithm = ball_tree; leaf_size = 32; metric = minkowski; n_neighbors = 8; p = 3; weights = uniform,f1 and cv,0.817 +- 0.306
3,knn,_scale = True; algorithm = kd_tree; leaf_size = 4; metric = euclidean; n_neighbors = 5; weights = uniform,f1 and cv,0.791 +- 0.079
4,logreg,C = 0.000187053924899; _scale = True; class_weight = balanced; dual = False; fit_intercept = False; penalty = l2; tol = 0.0034814857662,f1 and cv,0.664 +- 0.442
5,logreg,C = 2335.37517726; _scale = True; class_weight = balanced; fit_intercept = True; penalty = l1; tol = 5208.03307363,f1 and cv,0.000 +- 0.000
6,logreg,C = 160.105625083; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 0.0819182525731,f1 and cv,0.745 +- 0.107
7,dt,criterion = entropy; max_depth = 5; max_features = 0.781478174182; min_samples_leaf = 1; min_samples_split = 2,f1 and cv,0.585 +- 0.451
8,knn,_scale = True; algorithm = kd_tree; leaf_size = 49; metric = euclidean; n_neighbors = 18; weights = distance,f1 and cv,0.531 +- 0.717
9,knn,_scale = True; algorithm = kd_tree; leaf_size = 17; metric = manhattan; n_neighbors = 12; weights = distance,f1 and cv,0.686 +- 0.187
10,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 15; weights = uniform,f1 and cv,0.786 +- 0.297
11,logreg,C = 3647.55069827; _scale = True; class_weight = balanced; dual = False; fit_intercept = True; penalty = l2; tol = 0.448237732321,f1 and cv,0.769 +- 0.217
12,dt,criterion = entropy; max_depth = 10; max_features = 0.913382063114; min_samples_leaf = 2; min_samples_split = 4,f1 and cv,0.649 +- 0.182
13,knn,_scale = True; algorithm = ball_tree; leaf_size = 37; metric = minkowski; n_neighbors = 3; p = 3; weights = distance,f1 and cv,0.740 +- 0.527
14,dt,criterion = entropy; max_depth = 5; max_features = 0.325846823079; min_samples_leaf = 1; min_samples_split = 2,f1 and cv,0.736 +- 0.283
15,knn,_scale = True; algorithm = kd_tree; leaf_size = 2; metric = chebyshev; n_neighbors = 3; weights = uniform,f1 and cv,0.691 +- 0.424
16,knn,_scale = True; algorithm = kd_tree; leaf_size = 27; metric = manhattan; n_neighbors = 8; weights = uniform,f1 and cv,0.730 +- 0.496
17,knn,_scale = True; algorithm = ball_tree; leaf_size = 35; metric = minkowski; n_neighbors = 12; p = 1; weights = uniform,f1 and cv,0.793 +- 0.184
18,logreg,C = 2.25465797942e-05; _scale = True; class_weight = balanced; fit_intercept = True; penalty = l1; tol = 98.8424586631,f1 and cv,0.000 +- 0.000
19,logreg,C = 2593.12551929; _scale = True; class_weight = balanced; fit_intercept = True; penalty = l1; tol = 10437.0432791,f1 and cv,0.000 +- 0.000
20,knn,_scale = True; algorithm = kd_tree; leaf_size = 23; metric = chebyshev; n_neighbors = 10; weights = distance,f1 and cv,0.604 +- 0.403
21,logreg,C = 1.35334969088e-05; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 0.000448639710882,f1 and cv,0.729 +- 0.201
22,knn,_scale = True; algorithm = ball_tree; leaf_size = 19; metric = manhattan; n_neighbors = 20; weights = distance,f1 and cv,0.648 +- 0.224
23,knn,_scale = True; algorithm = ball_tree; leaf_size = 1; metric = chebyshev; n_neighbors = 7; weights = distance,f1 and cv,0.671 +- 0.142
24,dt,criterion = entropy; max_depth = 10; max_features = 0.497398625415; min_samples_leaf = 2; min_samples_split = 2,f1 and cv,0.639 +- 0.317
25,logreg,C = 0.00183903257216; _scale = True; class_weight = balanced; dual = False; fit_intercept = True; penalty = l2; tol = 0.000134398031491,f1 and cv,0.647 +- 0.462
26,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 19; weights = distance,f1 and cv,0.816 +- 0.104
27,logreg,C = 0.017394914513; _scale = True; class_weight = balanced; dual = False; fit_intercept = False; penalty = l2; tol = 0.0703048129884,f1 and cv,0.698 +- 0.347
28,knn,_scale = True; algorithm = kd_tree; leaf_size = 27; metric = manhattan; n_neighbors = 12; weights = uniform,f1 and cv,0.567 +- 0.670
29,logreg,C = 720.35958696; _scale = True; class_weight = balanced; dual = False; fit_intercept = True; penalty = l2; tol = 860.875802944,f1 and cv,0.000 +- 0.000
30,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 7; weights = distance,f1 and cv,0.631 +- 0.330
31,knn,_scale = True; algorithm = ball_tree; leaf_size = 45; metric = chebyshev; n_neighbors = 3; weights = distance,f1 and cv,0.611 +- 0.420
32,knn,_scale = True; algorithm = ball_tree; leaf_size = 39; metric = manhattan; n_neighbors = 16; weights = distance,f1 and cv,0.700 +- 0.374
33,knn,_scale = True; algorithm = kd_tree; leaf_size = 27; metric = manhattan; n_neighbors = 5; weights = uniform,f1 and cv,0.770 +- 0.173
34,logreg,C = 2.03125993463; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 0.00113450587983,f1 and cv,0.641 +- 0.271
35,logreg,C = 3.04936416576; _scale = True; class_weight = balanced; fit_intercept = True; penalty = l1; tol = 11.3252664914,f1 and cv,0.000 +- 0.000
36,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 5; p = 3; weights = distance,f1 and cv,0.716 +- 0.235
37,knn,_scale = True; algorithm = kd_tree; leaf_size = 22; metric = euclidean; n_neighbors = 6; weights = uniform,f1 and cv,0.779 +- 0.313
38,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 20; weights = distance,f1 and cv,0.780 +- 0.134
39,knn,_scale = True; algorithm = kd_tree; leaf_size = 4; metric = euclidean; n_neighbors = 5; weights = uniform,f1 and cv,0.655 +- 0.374
40,dt,criterion = gini; max_depth = 3; max_features = 0.410278290867; min_samples_leaf = 3; min_samples_split = 2,f1 and cv,0.684 +- 0.190
41,dt,criterion = entropy; max_depth = 10; max_features = 0.379779506067; min_samples_leaf = 1; min_samples_split = 3,f1 and cv,0.694 +- 0.253
42,knn,_scale = True; algorithm = kd_tree; leaf_size = 9; metric = euclidean; n_neighbors = 7; weights = uniform,f1 and cv,0.709 +- 0.288
43,knn,_scale = True; algorithm = ball_tree; leaf_size = 26; metric = euclidean; n_neighbors = 8; weights = uniform,f1 and cv,0.731 +- 0.163
44,knn,_scale = True; algorithm = ball_tree; leaf_size = 7; metric = chebyshev; n_neighbors = 11; weights = distance,f1 and cv,0.768 +- 0.208
45,knn,_scale = True; algorithm = ball_tree; leaf_size = 13; metric = manhattan; n_neighbors = 5; weights = uniform,f1 and cv,0.698 +- 0.266
46,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 7; weights = distance,f1 and cv,0.727 +- 0.262
47,knn,_scale = True; algorithm = kd_tree; leaf_size = 16; metric = minkowski; n_neighbors = 15; p = 3; weights = distance,f1 and cv,0.621 +- 0.265
48,knn,_scale = True; algorithm = kd_tree; leaf_size = 46; metric = euclidean; n_neighbors = 7; weights = distance,f1 and cv,0.620 +- 0.742
49,knn,_scale = True; algorithm = kd_tree; leaf_size = 47; metric = manhattan; n_neighbors = 6; weights = distance,f1 and cv,0.748 +- 0.365
50,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 16; weights = distance,f1 and cv,0.646 +- 0.138
51,knn,_scale = True; algorithm = kd_tree; leaf_size = 31; metric = chebyshev; n_neighbors = 20; weights = uniform,f1 and cv,0.746 +- 0.254
52,knn,_scale = True; algorithm = ball_tree; leaf_size = 20; metric = chebyshev; n_neighbors = 3; weights = distance,f1 and cv,0.732 +- 0.280
53,knn,_scale = True; algorithm = ball_tree; leaf_size = 5; metric = minkowski; n_neighbors = 16; p = 2; weights = distance,f1 and cv,0.749 +- 0.235
54,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 10; weights = uniform,f1 and cv,0.678 +- 0.174
55,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 18; weights = distance,f1 and cv,0.815 +- 0.243
56,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 16; weights = uniform,f1 and cv,0.586 +- 0.347
57,knn,_scale = True; algorithm = ball_tree; leaf_size = 33; metric = manhattan; n_neighbors = 18; weights = distance,f1 and cv,0.636 +- 0.426
58,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 10; weights = uniform,f1 and cv,0.718 +- 0.317
59,knn,_scale = True; algorithm = kd_tree; leaf_size = 26; metric = minkowski; n_neighbors = 10; p = 2; weights = distance,f1 and cv,0.614 +- 0.635
60,knn,_scale = True; algorithm = ball_tree; leaf_size = 1; metric = minkowski; n_neighbors = 3; p = 1; weights = uniform,f1 and cv,0.756 +- 0.303
61,logreg,C = 3.41536388184e-05; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 0.000706626237088,f1 and cv,0.760 +- 0.261
62,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 18; p = 3; weights = uniform,f1 and cv,0.748 +- 0.302
63,knn,_scale = True; algorithm = ball_tree; leaf_size = 1; metric = manhattan; n_neighbors = 13; weights = distance,f1 and cv,0.758 +- 0.288
64,knn,_scale = True; algorithm = ball_tree; leaf_size = 32; metric = chebyshev; n_neighbors = 9; weights = uniform,f1 and cv,0.527 +- 0.560
65,logreg,C = 0.000881758281515; _scale = True; class_weight = balanced; dual = False; fit_intercept = False; penalty = l2; tol = 1.48094272547e-05,f1 and cv,0.513 +- 0.668
66,dt,criterion = gini; max_depth = 5; max_features = 0.522434815905; min_samples_leaf = 1; min_samples_split = 2,f1 and cv,0.584 +- 0.452
67,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 6; weights = uniform,f1 and cv,0.616 +- 0.444
68,logreg,C = 143.056895445; _scale = True; class_weight = balanced; fit_intercept = False; penalty = l1; tol = 0.00424864328362,f1 and cv,0.805 +- 0.123
69,knn,_scale = True; algorithm = ball_tree; leaf_size = 17; metric = minkowski; n_neighbors = 13; p = 1; weights = uniform,f1 and cv,0.749 +- 0.100
70,logreg,C = 0.0206281857645; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 5.35095733943e-05,f1 and cv,0.757 +- 0.240
71,knn,_scale = True; algorithm = ball_tree; leaf_size = 38; metric = euclidean; n_neighbors = 16; weights = uniform,f1 and cv,0.780 +- 0.321
72,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 8; p = 3; weights = distance,f1 and cv,0.668 +- 0.302
73,knn,_scale = True; algorithm = kd_tree; leaf_size = 40; metric = minkowski; n_neighbors = 3; p = 1; weights = distance,f1 and cv,0.642 +- 0.251
74,knn,_scale = True; algorithm = ball_tree; leaf_size = 31; metric = chebyshev; n_neighbors = 9; weights = uniform,f1 and cv,0.500 +- 0.587
75,knn,_scale = True; algorithm = ball_tree; leaf_size = 43; metric = manhattan; n_neighbors = 1; weights = distance,f1 and cv,0.755 +- 0.121
76,knn,_scale = True; algorithm = ball_tree; leaf_size = 31; metric = manhattan; n_neighbors = 18; weights = distance,f1 and cv,0.671 +- 0.704
77,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 12; weights = uniform,f1 and cv,0.632 +- 0.225
78,logreg,C = 0.000595902947969; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 1.04172722169e-05,f1 and cv,0.718 +- 0.336
79,knn,_scale = True; algorithm = kd_tree; leaf_size = 39; metric = minkowski; n_neighbors = 9; p = 1; weights = distance,f1 and cv,0.717 +- 0.082
80,knn,_scale = True; algorithm = ball_tree; leaf_size = 32; metric = euclidean; n_neighbors = 11; weights = distance,f1 and cv,0.760 +- 0.432
81,knn,_scale = True; algorithm = kd_tree; leaf_size = 12; metric = manhattan; n_neighbors = 14; weights = distance,f1 and cv,0.620 +- 0.482
82,logreg,C = 39737.1240377; _scale = True; class_weight = balanced; fit_intercept = True; penalty = l1; tol = 22796.4475141,f1 and cv,0.000 +- 0.000
83,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 9; weights = distance,f1 and cv,0.680 +- 0.259
84,knn,_scale = True; algorithm = kd_tree; leaf_size = 22; metric = manhattan; n_neighbors = 19; weights = distance,f1 and cv,0.791 +- 0.300
85,knn,_scale = True; algorithm = kd_tree; leaf_size = 25; metric = chebyshev; n_neighbors = 19; weights = uniform,f1 and cv,0.691 +- 0.157
86,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 17; p = 1; weights = uniform,f1 and cv,0.757 +- 0.180
87,knn,_scale = True; algorithm = ball_tree; leaf_size = 15; metric = chebyshev; n_neighbors = 13; weights = uniform,f1 and cv,0.711 +- 0.079
88,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 19; weights = distance,f1 and cv,0.732 +- 0.087
89,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 10; weights = distance,f1 and cv,0.701 +- 0.334
90,logreg,C = 810.212983363; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 0.0254460704707,f1 and cv,0.731 +- 0.194
91,knn,_scale = True; algorithm = kd_tree; leaf_size = 24; metric = minkowski; n_neighbors = 13; p = 3; weights = uniform,f1 and cv,0.541 +- 0.560
92,logreg,C = 4.98491618564; _scale = True; class_weight = balanced; fit_intercept = False; penalty = l1; tol = 2120.19924658,f1 and cv,0.000 +- 0.000
93,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 15; weights = uniform,f1 and cv,0.783 +- 0.080
94,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 15; weights = uniform,f1 and cv,0.576 +- 0.635
95,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 5; weights = distance,f1 and cv,0.713 +- 0.227
96,knn,_scale = True; algorithm = kd_tree; leaf_size = 20; metric = euclidean; n_neighbors = 19; weights = uniform,f1 and cv,0.748 +- 0.121
97,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 2; weights = distance,f1 and cv,0.731 +- 0.271
98,logreg,C = 0.0049368197232; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 0.413574153029,f1 and cv,0.784 +- 0.103
99,logreg,C = 65.2333401319; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 80.4831976789,f1 and cv,0.680 +- 0.412
================================================
FILE: public/viz/datarun2_gp.csv
================================================
trail ID,method,parameters,metrics,performance
0,mlp,_scale = True; activation = logistic; alpha = 0.00799991890745; batch_size = auto; hidden_layer_sizes[0] = 56; hidden_layer_sizes[1] = 24; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.748 +- 0.164
1,mlp,_scale = True; activation = logistic; alpha = 0.00849143103282; batch_size = auto; hidden_layer_sizes[0] = 99; hidden_layer_sizes[1] = 62; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.735 +- 0.184
2,mlp,_scale = True; activation = logistic; alpha = 0.00177045733688; batch_size = auto; hidden_layer_sizes[0] = 55; hidden_layer_sizes[1] = 29; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.691 +- 0.157
3,mlp,_scale = True; activation = logistic; alpha = 0.00723514242738; batch_size = auto; hidden_layer_sizes[0] = 57; hidden_layer_sizes[1] = 21; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.760 +- 0.171
4,mlp,_scale = True; activation = identity; alpha = 0.00547161489348; batch_size = auto; hidden_layer_sizes[0] = 209; learning_rate = invscaling; learning_rate_init = 0.650260689975; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.682 +- 0.105
5,mlp,_scale = True; activation = identity; alpha = 0.00899195208914; batch_size = auto; hidden_layer_sizes[0] = 64; learning_rate = invscaling; learning_rate_init = 0.731561837323; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.609 +- 0.387
6,mlp,_scale = True; activation = identity; alpha = 0.00234238712992; batch_size = auto; hidden_layer_sizes[0] = 209; learning_rate = invscaling; learning_rate_init = 0.465202580375; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.736 +- 0.259
7,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 15; weights = distance,f1 and cv,0.780 +- 0.278
8,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 8; weights = distance,f1 and cv,0.674 +- 0.146
9,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 15; weights = distance,f1 and cv,0.589 +- 0.305
10,mlp,_scale = True; activation = tanh; alpha = 0.00224178522662; batch_size = auto; hidden_layer_sizes[0] = 239; hidden_layer_sizes[1] = 218; learning_rate = invscaling; learning_rate_init = 0.344309618261; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.791 +- 0.079
11,mlp,_scale = True; activation = tanh; alpha = 0.00427765774483; batch_size = auto; hidden_layer_sizes[0] = 246; hidden_layer_sizes[1] = 184; learning_rate = invscaling; learning_rate_init = 0.0273710171825; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.735 +- 0.498
12,mlp,_scale = True; activation = tanh; alpha = 0.00526485272616; batch_size = auto; hidden_layer_sizes[0] = 237; hidden_layer_sizes[1] = 218; learning_rate = invscaling; learning_rate_init = 0.879873731867; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.755 +- 0.121
13,mlp,_scale = True; activation = tanh; alpha = 0.000634821692555; batch_size = auto; hidden_layer_sizes[0] = 149; hidden_layer_sizes[1] = 6; learning_rate = adaptive; learning_rate_init = 0.277926847527; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.785 +- 0.423
14,mlp,_scale = True; activation = tanh; alpha = 0.00660553229005; batch_size = auto; hidden_layer_sizes[0] = 165; hidden_layer_sizes[1] = 112; learning_rate = adaptive; learning_rate_init = 0.867735422342; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.712 +- 0.130
15,mlp,_scale = True; activation = tanh; alpha = 0.00371911388751; batch_size = auto; hidden_layer_sizes[0] = 148; hidden_layer_sizes[1] = 4; learning_rate = adaptive; learning_rate_init = 0.122456112659; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.707 +- 0.166
16,mlp,_scale = True; activation = relu; alpha = 0.00392350082244; batch_size = auto; beta_1 = 0.914223423245; beta_2 = 0.858754257734; hidden_layer_sizes[0] = 237; hidden_layer_sizes[1] = 229; hidden_layer_sizes[2] = 67; learning_rate_init = 0.175696655355; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.594 +- 0.620
17,mlp,_scale = True; activation = relu; alpha = 0.000295211950158; batch_size = auto; beta_1 = 0.917406288989; beta_2 = 0.860898038173; hidden_layer_sizes[0] = 39; hidden_layer_sizes[1] = 287; hidden_layer_sizes[2] = 51; learning_rate_init = 0.559862026703; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.213 +- 0.549
18,gp,alpha = 0.041501904705; kernel = rational_quadratic; length_scale = 14.3432879364,f1 and cv,0.712 +- 0.348
19,gp,alpha = 0.773879362873; kernel = rational_quadratic; length_scale = 0.0330209564233,f1 and cv,0.614 +- 0.267
20,mlp,_scale = True; activation = identity; alpha = 0.00562597823112; batch_size = auto; hidden_layer_sizes[0] = 33; hidden_layer_sizes[1] = 195; hidden_layer_sizes[2] = 98; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.575 +- 0.348
21,mlp,_scale = True; activation = identity; alpha = 0.00806297352457; batch_size = auto; hidden_layer_sizes[0] = 170; hidden_layer_sizes[1] = 114; hidden_layer_sizes[2] = 63; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.673 +- 0.171
22,mlp,_scale = True; activation = logistic; alpha = 0.00160751379346; batch_size = auto; hidden_layer_sizes[0] = 218; hidden_layer_sizes[1] = 27; hidden_layer_sizes[2] = 67; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.694 +- 0.109
23,mlp,_scale = True; activation = logistic; alpha = 0.00837727401381; batch_size = auto; hidden_layer_sizes[0] = 22; hidden_layer_sizes[1] = 245; hidden_layer_sizes[2] = 194; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.797 +- 0.311
24,mlp,_scale = True; activation = logistic; alpha = 0.00879886663304; batch_size = auto; hidden_layer_sizes[0] = 243; hidden_layer_sizes[1] = 221; hidden_layer_sizes[2] = 250; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.766 +- 0.186
25,knn,_scale = True; algorithm = ball_tree; leaf_size = 42; metric = euclidean; n_neighbors = 17; weights = uniform,f1 and cv,0.807 +- 0.284
26,knn,_scale = True; algorithm = ball_tree; leaf_size = 4; metric = euclidean; n_neighbors = 13; weights = uniform,f1 and cv,0.682 +- 0.382
27,mnb,_scale_minmax = True; alpha = 0.822696871672; class_prior = None; fit_prior = 0,f1 and cv,0.780 +- 0.234
28,mnb,_scale_minmax = True; alpha = 0.955370128562; class_prior = None; fit_prior = 0,f1 and cv,0.664 +- 0.437
29,dt,criterion = gini; max_depth = 7; max_features = 0.676782742212; min_samples_leaf = 2; min_samples_split = 2,f1 and cv,0.745 +- 0.277
30,dt,criterion = gini; max_depth = 3; max_features = 0.979277036136; min_samples_leaf = 3; min_samples_split = 2,f1 and cv,0.760 +- 0.348
31,logreg,C = 0.00528614608449; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 7.63521491081,f1 and cv,0.708 +- 0.381
32,logreg,C = 1.87012501354; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 0.038350775541,f1 and cv,0.782 +- 0.149
33,mlp,_scale = True; activation = relu; alpha = 0.00376390194408; batch_size = auto; hidden_layer_sizes[0] = 94; learning_rate = invscaling; learning_rate_init = 0.380382485648; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.719 +- 0.155
34,mlp,_scale = True; activation = relu; alpha = 0.00206755077975; batch_size = auto; hidden_layer_sizes[0] = 70; learning_rate = invscaling; learning_rate_init = 0.4049758173; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.793 +- 0.158
35,mlp,_scale = True; activation = relu; alpha = 0.00664538442617; batch_size = auto; hidden_layer_sizes[0] = 274; hidden_layer_sizes[1] = 118; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.809 +- 0.112
36,mlp,_scale = True; activation = relu; alpha = 0.00718100709273; batch_size = auto; hidden_layer_sizes[0] = 41; hidden_layer_sizes[1] = 165; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.742 +- 0.260
37,mlp,_scale = True; activation = identity; alpha = 0.00871607173955; batch_size = auto; hidden_layer_sizes[0] = 6; hidden_layer_sizes[1] = 290; hidden_layer_sizes[2] = 293; learning_rate = adaptive; learning_rate_init = 0.942419045492; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.000 +- 0.000
38,mlp,_scale = True; activation = relu; alpha = 0.00833505964486; batch_size = auto; hidden_layer_sizes[0] = 9; hidden_layer_sizes[1] = 122; learning_rate = adaptive; learning_rate_init = 0.784859303991; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.638 +- 0.339
39,mlp,_scale = True; activation = relu; alpha = 0.006648665153; batch_size = auto; hidden_layer_sizes[0] = 180; hidden_layer_sizes[1] = 15; learning_rate = adaptive; learning_rate_init = 0.562082568671; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.658 +- 0.221
40,mlp,_scale = True; activation = identity; alpha = 0.00792512283525; batch_size = auto; hidden_layer_sizes[0] = 25; hidden_layer_sizes[1] = 292; hidden_layer_sizes[2] = 22; learning_rate = constant; learning_rate_init = 0.788690042474; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.367 +- 0.442
41,mlp,_scale = True; activation = identity; alpha = 0.00688422258512; batch_size = auto; hidden_layer_sizes[0] = 288; hidden_layer_sizes[1] = 260; hidden_layer_sizes[2] = 238; learning_rate = constant; learning_rate_init = 0.77218536964; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.360 +- 0.406
42,knn,_scale = True; algorithm = kd_tree; leaf_size = 27; metric = chebyshev; n_neighbors = 16; weights = uniform,f1 and cv,0.679 +- 0.154
43,knn,_scale = True; algorithm = kd_tree; leaf_size = 33; metric = chebyshev; n_neighbors = 18; weights = uniform,f1 and cv,0.705 +- 0.228
44,mlp,_scale = True; activation = relu; alpha = 0.00829106098594; batch_size = auto; hidden_layer_sizes[0] = 37; hidden_layer_sizes[1] = 118; learning_rate = constant; learning_rate_init = 0.455241704563; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.716 +- 0.415
45,mlp,_scale = True; activation = relu; alpha = 0.00233838412282; batch_size = auto; hidden_layer_sizes[0] = 12; hidden_layer_sizes[1] = 24; learning_rate = constant; learning_rate_init = 0.58136369656; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.655 +- 0.119
46,gp,kernel = exp_sine_squared; length_scale = 0.286115700759; periodicity = 1,f1 and cv,0.493 +- 0.441
47,gp,kernel = exp_sine_squared; length_scale = 0.0757682044306; periodicity = 1,f1 and cv,0.479 +- 0.506
48,mlp,_scale = True; activation = identity; alpha = 0.00811391645532; batch_size = auto; beta_1 = 0.970126220631; beta_2 = 0.995982659475; hidden_layer_sizes[0] = 277; learning_rate_init = 0.683438765367; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.847 +- 0.059
49,mlp,_scale = True; activation = identity; alpha = 0.00659411144259; batch_size = auto; beta_1 = 0.905504262224; beta_2 = 0.821045183661; hidden_layer_sizes[0] = 85; learning_rate_init = 0.504346221191; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.713 +- 0.466
50,logreg,C = 0.197368621397; _scale = True; class_weight = balanced; fit_intercept = True; penalty = l1; tol = 64.4595613216,f1 and cv,0.000 +- 0.000
51,mlp,_scale = True; activation = relu; alpha = 0.00153881672492; batch_size = auto; hidden_layer_sizes[0] = 115; hidden_layer_sizes[1] = 227; hidden_layer_sizes[2] = 104; learning_rate = invscaling; learning_rate_init = 0.0393098031832; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.806 +- 0.533
52,mlp,_scale = True; activation = relu; alpha = 0.00816849554577; batch_size = auto; hidden_layer_sizes[0] = 250; hidden_layer_sizes[1] = 85; hidden_layer_sizes[2] = 128; learning_rate = invscaling; learning_rate_init = 0.615046869676; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.674 +- 0.293
53,mlp,_scale = True; activation = tanh; alpha = 0.00360979808843; batch_size = auto; beta_1 = 0.855599296801; beta_2 = 0.856429648316; hidden_layer_sizes[0] = 179; hidden_layer_sizes[1] = 53; learning_rate_init = 0.929951980019; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.577 +- 0.292
54,mlp,_scale = True; activation = tanh; alpha = 0.0058900276749; batch_size = auto; beta_1 = 0.89139590446; beta_2 = 0.844801556636; hidden_layer_sizes[0] = 170; hidden_layer_sizes[1] = 150; learning_rate_init = 0.327819358042; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.569 +- 0.418
55,mlp,_scale = True; activation = logistic; alpha = 0.00408815617237; batch_size = auto; hidden_layer_sizes[0] = 149; hidden_layer_sizes[1] = 9; learning_rate = constant; learning_rate_init = 0.609590912397; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.831 +- 0.221
56,mlp,_scale = True; activation = logistic; alpha = 0.000300185642587; batch_size = auto; hidden_layer_sizes[0] = 91; hidden_layer_sizes[1] = 129; learning_rate = constant; learning_rate_init = 0.709215361944; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.379 +- 0.621
57,mlp,_scale = True; activation = relu; alpha = 0.0030438878624; batch_size = auto; hidden_layer_sizes[0] = 81; learning_rate = constant; learning_rate_init = 0.905282464084; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.673 +- 0.232
58,mlp,_scale = True; activation = relu; alpha = 0.00146693288878; batch_size = auto; hidden_layer_sizes[0] = 189; learning_rate = constant; learning_rate_init = 0.898892816545; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.841 +- 0.290
59,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 10; p = 1; weights = uniform,f1 and cv,0.650 +- 0.466
60,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 2; p = 2; weights = uniform,f1 and cv,0.826 +- 0.225
61,knn,_scale = True; algorithm = kd_tree; leaf_size = 10; metric = euclidean; n_neighbors = 6; weights = distance,f1 and cv,0.776 +- 0.187
62,knn,_scale = True; algorithm = kd_tree; leaf_size = 19; metric = euclidean; n_neighbors = 14; weights = distance,f1 and cv,0.733 +- 0.163
63,knn,_scale = True; algorithm = kd_tree; leaf_size = 33; metric = manhattan; n_neighbors = 2; weights = uniform,f1 and cv,0.543 +- 0.732
64,knn,_scale = True; algorithm = kd_tree; leaf_size = 9; metric = manhattan; n_neighbors = 3; weights = uniform,f1 and cv,0.662 +- 0.160
65,logreg,C = 1.44037909643e-05; _scale = True; class_weight = balanced; fit_intercept = False; penalty = l1; tol = 387.70744433,f1 and cv,0.000 +- 0.000
66,mlp,_scale = True; activation = identity; alpha = 0.00398238250451; batch_size = auto; hidden_layer_sizes[0] = 46; learning_rate = constant; learning_rate_init = 0.047364176926; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.730 +- 0.234
67,mlp,_scale = True; activation = identity; alpha = 0.00463099649498; batch_size = auto; hidden_layer_sizes[0] = 107; learning_rate = constant; learning_rate_init = 0.733119060066; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.778 +- 0.281
68,mlp,_scale = True; activation = tanh; alpha = 0.00562018390673; batch_size = auto; hidden_layer_sizes[0] = 87; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.793 +- 0.224
69,mlp,_scale = True; activation = tanh; alpha = 0.00696111712754; batch_size = auto; hidden_layer_sizes[0] = 279; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.594 +- 0.362
70,gp,kernel = constant,f1 and cv,0.707 +- 0.042
71,knn,_scale = True; algorithm = kd_tree; leaf_size = 34; metric = minkowski; n_neighbors = 16; p = 2; weights = distance,f1 and cv,0.715 +- 0.140
72,knn,_scale = True; algorithm = kd_tree; leaf_size = 50; metric = minkowski; n_neighbors = 19; p = 2; weights = distance,f1 and cv,0.688 +- 0.275
73,mlp,_scale = True; activation = relu; alpha = 0.000282349269762; batch_size = auto; beta_1 = 0.852197982746; beta_2 = 0.983418833054; hidden_layer_sizes[0] = 62; learning_rate_init = 0.588627297032; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.819 +- 0.097
74,mlp,_scale = True; activation = relu; alpha = 0.00515144712438; batch_size = auto; beta_1 = 0.994226324379; beta_2 = 0.815806660936; hidden_layer_sizes[0] = 199; learning_rate_init = 0.812129907734; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.661 +- 0.325
75,logreg,C = 2725.12959188; _scale = True; class_weight = balanced; fit_intercept = True; penalty = l1; tol = 5377.22663938,f1 and cv,0.000 +- 0.000
76,mlp,_scale = True; activation = identity; alpha = 0.0062318539897; batch_size = auto; hidden_layer_sizes[0] = 54; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.631 +- 0.319
77,mlp,_scale = True; activation = identity; alpha = 0.00872090939651; batch_size = auto; hidden_layer_sizes[0] = 219; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.623 +- 0.349
78,knn,_scale = True; algorithm = ball_tree; leaf_size = 39; metric = euclidean; n_neighbors = 17; weights = distance,f1 and cv,0.663 +- 0.380
79,knn,_scale = True; algorithm = ball_tree; leaf_size = 45; metric = euclidean; n_neighbors = 10; weights = distance,f1 and cv,0.744 +- 0.193
80,knn,_scale = True; algorithm = kd_tree; leaf_size = 34; metric = manhattan; n_neighbors = 1; weights = distance,f1 and cv,0.498 +- 0.526
81,knn,_scale = True; algorithm = kd_tree; leaf_size = 30; metric = manhattan; n_neighbors = 7; weights = distance,f1 and cv,0.704 +- 0.227
82,gp,kernel = matern; nu = 0.5,f1 and cv,0.514 +- 0.520
83,mlp,_scale = True; activation = tanh; alpha = 0.00883367578485; batch_size = auto; hidden_layer_sizes[0] = 36; hidden_layer_sizes[1] = 177; hidden_layer_sizes[2] = 36; learning_rate = adaptive; learning_rate_init = 0.558670955025; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.785 +- 0.126
84,mlp,_scale = True; activation = tanh; alpha = 0.00583093659719; batch_size = auto; hidden_layer_sizes[0] = 242; hidden_layer_sizes[1] = 206; hidden_layer_sizes[2] = 129; learning_rate = adaptive; learning_rate_init = 0.273588290809; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.595 +- 0.234
85,mlp,_scale = True; activation = identity; alpha = 0.00147185389566; batch_size = auto; hidden_layer_sizes[0] = 15; hidden_layer_sizes[1] = 162; learning_rate = invscaling; learning_rate_init = 0.00589667281794; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.634 +- 0.196
86,mlp,_scale = True; activation = identity; alpha = 0.00146904657447; batch_size = auto; hidden_layer_sizes[0] = 152; hidden_layer_sizes[1] = 39; learning_rate = invscaling; learning_rate_init = 0.501611525757; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.735 +- 0.218
87,knn,_scale = True; algorithm = ball_tree; leaf_size = 18; metric = manhattan; n_neighbors = 16; weights = distance,f1 and cv,0.833 +- 0.230
88,knn,_scale = True; algorithm = ball_tree; leaf_size = 38; metric = manhattan; n_neighbors = 3; weights = distance,f1 and cv,0.719 +- 0.361
89,mlp,_scale = True; activation = relu; alpha = 0.00321551107466; batch_size = auto; beta_1 = 0.806148066701; beta_2 = 0.907200674379; hidden_layer_sizes[0] = 45; hidden_layer_sizes[1] = 195; learning_rate_init = 0.489362033585; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.753 +- 0.316
90,mlp,_scale = True; activation = relu; alpha = 0.00200767805409; batch_size = auto; beta_1 = 0.878324882969; beta_2 = 0.985934115428; hidden_layer_sizes[0] = 266; hidden_layer_sizes[1] = 196; learning_rate_init = 0.109332747323; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.260 +- 0.644
91,mlp,_scale = True; activation = identity; alpha = 0.000627780706672; batch_size = auto; beta_1 = 0.859269935731; beta_2 = 0.857146993073; hidden_layer_sizes[0] = 235; hidden_layer_sizes[1] = 202; hidden_layer_sizes[2] = 173; learning_rate_init = 0.0132779420352; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.735 +- 0.387
92,mlp,_scale = True; activation = identity; alpha = 0.00664294783307; batch_size = auto; beta_1 = 0.895240629973; beta_2 = 0.96121714919; hidden_layer_sizes[0] = 199; hidden_layer_sizes[1] = 2; hidden_layer_sizes[2] = 166; learning_rate_init = 0.184764822357; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.742 +- 0.197
93,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 11; weights = distance,f1 and cv,0.811 +- 0.237
94,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 16; weights = distance,f1 and cv,0.821 +- 0.197
95,mlp,_scale = True; activation = tanh; alpha = 0.00447020989052; batch_size = auto; hidden_layer_sizes[0] = 53; learning_rate = invscaling; learning_rate_init = 0.83218879304; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.843 +- 0.238
96,mlp,_scale = True; activation = tanh; alpha = 0.00534383889414; batch_size = auto; hidden_layer_sizes[0] = 130; learning_rate = invscaling; learning_rate_init = 0.317531602371; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.783 +- 0.405
97,knn,_scale = True; algorithm = ball_tree; leaf_size = 13; metric = manhattan; n_neighbors = 11; weights = uniform,f1 and cv,0.765 +- 0.164
98,knn,_scale = True; algorithm = ball_tree; leaf_size = 13; metric = manhattan; n_neighbors = 17; weights = uniform,f1 and cv,0.713 +- 0.498
99,mlp,_scale = True; activation = logistic; alpha = 0.00647870532925; batch_size = auto; beta_1 = 0.899214542024; beta_2 = 0.953020327964; hidden_layer_sizes[0] = 150; hidden_layer_sizes[1] = 273; hidden_layer_sizes[2] = 24; learning_rate_init = 0.513705713727; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.133 +- 0.533
100,mlp,_scale = True; activation = logistic; alpha = 0.00504175802063; batch_size = auto; beta_1 = 0.965923313126; beta_2 = 0.870884989507; hidden_layer_sizes[0] = 169; hidden_layer_sizes[1] = 269; hidden_layer_sizes[2] = 164; learning_rate_init = 0.671616929214; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.565 +- 0.566
101,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 19; weights = uniform,f1 and cv,0.769 +- 0.298
102,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 4; weights = uniform,f1 and cv,0.641 +- 0.468
103,mlp,_scale = True; activation = logistic; alpha = 0.00535635609759; batch_size = auto; hidden_layer_sizes[0] = 214; learning_rate = constant; learning_rate_init = 0.476065873624; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.145 +- 0.582
104,mlp,_scale = True; activation = logistic; alpha = 0.00155844793069; batch_size = auto; hidden_layer_sizes[0] = 147; learning_rate = constant; learning_rate_init = 0.515921525931; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.506 +- 0.595
105,mlp,_scale = True; activation = tanh; alpha = 0.00657916092724; batch_size = auto; hidden_layer_sizes[0] = 172; hidden_layer_sizes[1] = 78; learning_rate = constant; learning_rate_init = 0.84120659402; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.713 +- 0.307
106,mlp,_scale = True; activation = tanh; alpha = 0.00631981104713; batch_size = auto; hidden_layer_sizes[0] = 278; hidden_layer_sizes[1] = 113; learning_rate = constant; learning_rate_init = 0.59761438975; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.732 +- 0.529
107,gp,kernel = exp_sine_squared; length_scale = 10.4281777388; periodicity = 0,f1 and cv,0.668 +- 0.239
108,gp,kernel = exp_sine_squared; length_scale = 0.0124348843377; periodicity = 0,f1 and cv,0.324 +- 0.367
109,mlp,_scale = True; activation = tanh; alpha = 0.0071096109221; batch_size = auto; hidden_layer_sizes[0] = 239; hidden_layer_sizes[1] = 179; hidden_layer_sizes[2] = 50; learning_rate = invscaling; learning_rate_init = 0.851048113402; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.722 +- 0.102
110,mlp,_scale = True; activation = tanh; alpha = 0.00624666516271; batch_size = auto; hidden_layer_sizes[0] = 84; hidden_layer_sizes[1] = 289; hidden_layer_sizes[2] = 188; learning_rate = invscaling; learning_rate_init = 0.22508895672; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.707 +- 0.331
111,mlp,_scale = True; activation = logistic; alpha = 0.00545160793444; batch_size = auto; beta_1 = 0.921522650053; beta_2 = 0.885952281926; hidden_layer_sizes[0] = 97; hidden_layer_sizes[1] = 293; learning_rate_init = 0.699397950919; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.410 +- 0.670
112,mlp,_scale = True; activation = logistic; alpha = 0.00773926142465; batch_size = auto; beta_1 = 0.962469816726; beta_2 = 0.857047078236; hidden_layer_sizes[0] = 128; hidden_layer_sizes[1] = 16; learning_rate_init = 0.48692840029; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.417 +- 0.683
113,svm,C = 3.41895813938e-05; _scale = True; cache_size = 15000; class_weight = balanced; kernel = linear; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.000 +- 0.000
114,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 15; weights = uniform,f1 and cv,0.737 +- 0.252
115,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 3; weights = uniform,f1 and cv,0.740 +- 0.163
116,mlp,_scale = True; activation = logistic; alpha = 0.00634399261369; batch_size = auto; hidden_layer_sizes[0] = 154; hidden_layer_sizes[1] = 222; hidden_layer_sizes[2] = 251; learning_rate = adaptive; learning_rate_init = 0.650390099442; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.440 +- 0.719
117,mlp,_scale = True; activation = logistic; alpha = 0.0076146680887; batch_size = auto; hidden_layer_sizes[0] = 149; hidden_layer_sizes[1] = 12; hidden_layer_sizes[2] = 255; learning_rate = adaptive; learning_rate_init = 0.404560346859; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.267 +- 0.653
118,svm,C = 0.000620649616089; _scale = True; cache_size = 15000; class_weight = balanced; gamma = 31.6556705129; kernel = rbf; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.400 +- 0.653
119,svm,C = 0.0022628323725; _scale = True; cache_size = 15000; class_weight = balanced; gamma = 0.105226970249; kernel = rbf; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.133 +- 0.533
120,mlp,_scale = True; activation = relu; alpha = 0.00569511782952; batch_size = auto; hidden_layer_sizes[0] = 4; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.701 +- 0.148
121,mlp,_scale = True; activation = relu; alpha = 0.00341068824745; batch_size = auto; hidden_layer_sizes[0] = 68; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.715 +- 0.249
122,mlp,_scale = True; activation = logistic; alpha = 0.00109553986077; batch_size = auto; hidden_layer_sizes[0] = 18; learning_rate = invscaling; learning_rate_init = 0.16996237031; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.648 +- 0.076
123,mlp,_scale = True; activation = logistic; alpha = 0.00410451928098; batch_size = auto; hidden_layer_sizes[0] = 267; learning_rate = invscaling; learning_rate_init = 0.497287105842; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.787 +- 0.167
124,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 15; weights = distance,f1 and cv,0.702 +- 0.269
125,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 14; weights = distance,f1 and cv,0.682 +- 0.139
126,logreg,C = 0.000214886983356; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 0.0606697682009,f1 and cv,0.743 +- 0.086
127,logreg,C = 0.0149215712074; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 0.013252790367,f1 and cv,0.772 +- 0.200
128,mlp,_scale = True; activation = logistic; alpha = 0.00674379437133; batch_size = auto; hidden_layer_sizes[0] = 234; learning_rate = adaptive; learning_rate_init = 0.315733576239; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.256 +- 0.629
129,mlp,_scale = True; activation = logistic; alpha = 0.00225251596382; batch_size = auto; hidden_layer_sizes[0] = 296; learning_rate = adaptive; learning_rate_init = 0.117927780645; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.400 +- 0.653
130,mlp,_scale = True; activation = tanh; alpha = 0.00250989500273; batch_size = auto; hidden_layer_sizes[0] = 261; hidden_layer_sizes[1] = 29; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.765 +- 0.164
131,mlp,_scale = True; activation = tanh; alpha = 0.00717527438859; batch_size = auto; hidden_layer_sizes[0] = 281; hidden_layer_sizes[1] = 21; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.626 +- 0.161
132,mlp,_scale = True; activation = relu; alpha = 0.00627663522928; batch_size = auto; hidden_layer_sizes[0] = 7; hidden_layer_sizes[1] = 57; hidden_layer_sizes[2] = 287; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.737 +- 0.290
133,mlp,_scale = True; activation = relu; alpha = 0.00877694108375; batch_size = auto; hidden_layer_sizes[0] = 188; hidden_layer_sizes[1] = 285; hidden_layer_sizes[2] = 287; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.719 +- 0.477
134,mlp,_scale = True; activation = logistic; alpha = 0.00573644572307; batch_size = auto; hidden_layer_sizes[0] = 263; hidden_layer_sizes[1] = 108; learning_rate = invscaling; learning_rate_init = 0.409766285581; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.240 +- 0.640
135,mlp,_scale = True; activation = logistic; alpha = 0.000438855779034; batch_size = auto; hidden_layer_sizes[0] = 120; hidden_layer_sizes[1] = 262; learning_rate = invscaling; learning_rate_init = 0.595116141461; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.246 +- 0.603
136,mlp,_scale = True; activation = identity; alpha = 0.00498270430181; batch_size = auto; hidden_layer_sizes[0] = 41; hidden_layer_sizes[1] = 145; learning_rate = adaptive; learning_rate_init = 0.258447206553; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.722 +- 0.491
137,mlp,_scale = True; activation = identity; alpha = 0.00843062651055; batch_size = auto; hidden_layer_sizes[0] = 285; hidden_layer_sizes[1] = 250; learning_rate = adaptive; learning_rate_init = 0.200867030817; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.665 +- 0.248
138,mlp,_scale = True; activation = tanh; alpha = 0.00388333311812; batch_size = auto; beta_1 = 0.871947033336; beta_2 = 0.897936169998; hidden_layer_sizes[0] = 78; hidden_layer_sizes[1] = 184; hidden_layer_sizes[2] = 241; learning_rate_init = 0.339093791529; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.394 +- 0.370
139,mlp,_scale = True; activation = tanh; alpha = 0.00254041083504; batch_size = auto; beta_1 = 0.890001067675; beta_2 = 0.995239587889; hidden_layer_sizes[0] = 134; hidden_layer_sizes[1] = 138; hidden_layer_sizes[2] = 298; learning_rate_init = 0.6329508392; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.720 +- 0.249
140,logreg,C = 0.000169227404116; _scale = True; class_weight = balanced; dual = False; fit_intercept = True; penalty = l2; tol = 4.40641336764e-05,f1 and cv,0.679 +- 0.189
141,logreg,C = 8145.17905037; _scale = True; class_weight = balanced; dual = False; fit_intercept = True; penalty = l2; tol = 1.09895176679,f1 and cv,0.805 +- 0.245
142,mlp,_scale = True; activation = tanh; alpha = 0.00254330722049; batch_size = auto; hidden_layer_sizes[0] = 136; learning_rate = constant; learning_rate_init = 0.917409557426; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.894 +- 0.194
143,mlp,_scale = True; activation = tanh; alpha = 0.00258248944698; batch_size = auto; hidden_layer_sizes[0] = 277; learning_rate = constant; learning_rate_init = 0.0852553336674; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.776 +- 0.231
144,knn,_scale = True; algorithm = kd_tree; leaf_size = 38; metric = chebyshev; n_neighbors = 6; weights = distance,f1 and cv,0.741 +- 0.123
145,knn,_scale = True; algorithm = kd_tree; leaf_size = 7; metric = chebyshev; n_neighbors = 12; weights = distance,f1 and cv,0.715 +- 0.189
146,logreg,C = 5936.16635027; _scale = True; class_weight = balanced; dual = False; fit_intercept = False; penalty = l2; tol = 0.000765762014002,f1 and cv,0.714 +- 0.486
147,logreg,C = 106.176591673; _scale = True; class_weight = balanced; dual = False; fit_intercept = False; penalty = l2; tol = 634.086502893,f1 and cv,0.000 +- 0.000
148,knn,_scale = True; algorithm = ball_tree; leaf_size = 36; metric = minkowski; n_neighbors = 19; p = 2; weights = distance,f1 and cv,0.736 +- 0.138
149,knn,_scale = True; algorithm = ball_tree; leaf_size = 27; metric = minkowski; n_neighbors = 17; p = 2; weights = distance,f1 and cv,0.785 +- 0.149
150,mlp,_scale = True; activation = tanh; alpha = 0.00128966913907; batch_size = auto; hidden_layer_sizes[0] = 156; learning_rate = adaptive; learning_rate_init = 0.393032014951; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.838 +- 0.191
151,mlp,_scale = True; activation = tanh; alpha = 0.00846265747264; batch_size = auto; hidden_layer_sizes[0] = 137; learning_rate = adaptive; learning_rate_init = 0.937994496757; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.648 +- 0.198
152,knn,_scale = True; algorithm = kd_tree; leaf_size = 48; metric = euclidean; n_neighbors = 3; weights = uniform,f1 and cv,0.761 +- 0.347
153,knn,_scale = True; algorithm = kd_tree; leaf_size = 11; metric = euclidean; n_neighbors = 4; weights = uniform,f1 and cv,0.646 +- 0.487
154,svm,C = 0.00967371320032; _scale = True; cache_size = 15000; class_weight = balanced; kernel = linear; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.699 +- 0.361
155,knn,_scale = True; algorithm = ball_tree; leaf_size = 3; metric = minkowski; n_neighbors = 3; p = 2; weights = uniform,f1 and cv,0.620 +- 0.388
156,knn,_scale = True; algorithm = ball_tree; leaf_size = 3; metric = minkowski; n_neighbors = 10; p = 2; weights = uniform,f1 and cv,0.693 +- 0.136
157,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 20; p = 3; weights = distance,f1 and cv,0.749 +- 0.297
158,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 10; p = 1; weights = distance,f1 and cv,0.686 +- 0.187
159,svm,C = 3.08767056789e-05; _scale = True; cache_size = 15000; class_weight = balanced; coef0 = -500; gamma = 0.000157995812159; kernel = sigmoid; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.000 +- 0.000
160,gp,kernel = rbf,f1 and cv,0.160 +- 0.392
161,mlp,_scale = True; activation = identity; alpha = 0.00409914752862; batch_size = auto; hidden_layer_sizes[0] = 10; hidden_layer_sizes[1] = 124; hidden_layer_sizes[2] = 198; learning_rate = adaptive; learning_rate_init = 0.803910784878; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.000 +- 0.000
162,mlp,_scale = True; activation = relu; alpha = 0.000722975852489; batch_size = auto; hidden_layer_sizes[0] = 18; learning_rate = adaptive; learning_rate_init = 0.0589195024231; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.881 +- 0.162
163,mlp,_scale = True; activation = relu; alpha = 0.00276069690004; batch_size = auto; hidden_layer_sizes[0] = 98; learning_rate = adaptive; learning_rate_init = 0.582401488601; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.682 +- 0.486
164,mlp,_scale = True; activation = identity; alpha = 0.00290322408132; batch_size = auto; hidden_layer_sizes[0] = 145; hidden_layer_sizes[1] = 56; learning_rate = constant; learning_rate_init = 0.630712740026; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.634 +- 0.678
165,mlp,_scale = True; activation = identity; alpha = 0.0059401822615; batch_size = auto; hidden_layer_sizes[0] = 242; hidden_layer_sizes[1] = 69; learning_rate = constant; learning_rate_init = 0.783875604546; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.414 +- 0.524
166,mlp,_scale = True; activation = logistic; alpha = 0.00734691511846; batch_size = auto; beta_1 = 0.979837055634; beta_2 = 0.855015251651; hidden_layer_sizes[0] = 167; learning_rate_init = 0.0168683883515; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.747 +- 0.236
167,mlp,_scale = True; activation = logistic; alpha = 0.00183632302307; batch_size = auto; beta_1 = 0.906403478475; beta_2 = 0.829474115993; hidden_layer_sizes[0] = 53; learning_rate_init = 0.345052614046; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.571 +- 0.628
168,rf,criterion = gini; max_depth = 9; max_features = 0.804958345337; min_samples_leaf = 3; n_estimators = 100; n_jobs = -1,f1 and cv,0.813 +- 0.389
169,rf,criterion = gini; max_depth = 8; max_features = 0.307667280453; min_samples_leaf = 3; n_estimators = 100; n_jobs = -1,f1 and cv,0.848 +- 0.111
170,mlp,_scale = True; activation = relu; alpha = 0.0076573246351; batch_size = auto; hidden_layer_sizes[0] = 73; hidden_layer_sizes[1] = 151; hidden_layer_sizes[2] = 64; learning_rate = constant; learning_rate_init = 0.0473321470211; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.681 +- 0.295
171,mlp,_scale = True; activation = relu; alpha = 0.000685877164814; batch_size = auto; hidden_layer_sizes[0] = 200; hidden_layer_sizes[1] = 273; hidden_layer_sizes[2] = 93; learning_rate = constant; learning_rate_init = 0.779631789596; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.709 +- 0.213
172,svm,C = 227.602880751; _scale = True; cache_size = 15000; class_weight = balanced; coef0 = 334; degree = 5; gamma = 0.000267654535605; kernel = poly; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.567 +- 0.383
173,svm,C = 7.42631608244; _scale = True; cache_size = 15000; class_weight = balanced; coef0 = 953; degree = 3; gamma = 114.230318384; kernel = poly; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.831 +- 0.336
174,mlp,_scale = True; activation = logistic; alpha = 0.00456440558355; batch_size = auto; hidden_layer_sizes[0] = 56; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.754 +- 0.107
175,mlp,_scale = True; activation = logistic; alpha = 0.00349695731538; batch_size = auto; hidden_layer_sizes[0] = 299; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.718 +- 0.289
176,mlp,_scale = True; activation = identity; alpha = 0.00645683821149; batch_size = auto; hidden_layer_sizes[0] = 179; learning_rate = adaptive; learning_rate_init = 0.21123025271; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.648 +- 0.479
177,mlp,_scale = True; activation = identity; alpha = 0.00219027581371; batch_size = auto; hidden_layer_sizes[0] = 127; learning_rate = adaptive; learning_rate_init = 0.811623805271; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.642 +- 0.248
178,mlp,_scale = True; activation = logistic; alpha = 0.000441180339435; batch_size = auto; hidden_layer_sizes[0] = 281; hidden_layer_sizes[1] = 294; hidden_layer_sizes[2] = 147; learning_rate = constant; learning_rate_init = 0.937291815316; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.533 +- 0.533
179,mlp,_scale = True; activation = logistic; alpha = 0.000843572763542; batch_size = auto; hidden_layer_sizes[0] = 296; hidden_layer_sizes[1] = 169; hidden_layer_sizes[2] = 6; learning_rate = constant; learning_rate_init = 0.596167630544; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.390 +- 0.505
180,mlp,_scale = True; activation = relu; alpha = 0.00198848567947; batch_size = auto; hidden_layer_sizes[0] = 249; hidden_layer_sizes[1] = 238; hidden_layer_sizes[2] = 194; learning_rate = adaptive; learning_rate_init = 0.905428347836; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.835 +- 0.144
181,mlp,_scale = True; activation = relu; alpha = 0.00633666049544; batch_size = auto; hidden_layer_sizes[0] = 264; hidden_layer_sizes[1] = 85; hidden_layer_sizes[2] = 254; learning_rate = adaptive; learning_rate_init = 0.645189174426; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.753 +- 0.305
182,mlp,_scale = True; activation = tanh; alpha = 0.00264250357265; batch_size = auto; beta_1 = 0.888232994177; beta_2 = 0.93918716222; hidden_layer_sizes[0] = 252; learning_rate_init = 0.633018848284; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.766 +- 0.186
183,mlp,_scale = True; activation = tanh; alpha = 0.00352913577746; batch_size = auto; beta_1 = 0.963429454334; beta_2 = 0.815363692365; hidden_layer_sizes[0] = 264; learning_rate_init = 0.7703991828; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.755 +- 0.329
184,mlp,_scale = True; activation = logistic; alpha = 0.00559135751197; batch_size = auto; hidden_layer_sizes[0] = 64; hidden_layer_sizes[1] = 200; learning_rate = adaptive; learning_rate_init = 0.984715966475; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.279 +- 0.684
185,mlp,_scale = True; activation = logistic; alpha = 0.00602061069601; batch_size = auto; hidden_layer_sizes[0] = 74; hidden_layer_sizes[1] = 52; learning_rate = adaptive; learning_rate_init = 0.438185966665; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.267 +- 0.653
186,mlp,_scale = True; activation = tanh; alpha = 0.00473516959304; batch_size = auto; hidden_layer_sizes[0] = 185; hidden_layer_sizes[1] = 23; hidden_layer_sizes[2] = 298; learning_rate = constant; learning_rate_init = 0.345744308056; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.770 +- 0.200
187,mlp,_scale = True; activation = tanh; alpha = 0.00449996604742; batch_size = auto; hidden_layer_sizes[0] = 49; hidden_layer_sizes[1] = 160; hidden_layer_sizes[2] = 255; learning_rate = constant; learning_rate_init = 0.546664881572; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.669 +- 0.398
188,gp,kernel = matern; nu = 1.5,f1 and cv,0.474 +- 0.350
189,dt,criterion = entropy; max_depth = 2; max_features = 0.816003602121; min_samples_leaf = 2; min_samples_split = 2,f1 and cv,0.702 +- 0.228
190,dt,criterion = entropy; max_depth = 4; max_features = 0.175003521757; min_samples_leaf = 2; min_samples_split = 3,f1 and cv,0.631 +- 0.393
191,knn,_scale = True; algorithm = kd_tree; leaf_size = 29; metric = minkowski; n_neighbors = 6; p = 3; weights = uniform,f1 and cv,0.755 +- 0.286
192,knn,_scale = True; algorithm = kd_tree; leaf_size = 20; metric = minkowski; n_neighbors = 14; p = 3; weights = uniform,f1 and cv,0.775 +- 0.227
193,mlp,_scale = True; activation = identity; alpha = 0.00784529159537; batch_size = auto; hidden_layer_sizes[0] = 252; hidden_layer_sizes[1] = 130; hidden_layer_sizes[2] = 46; learning_rate = invscaling; learning_rate_init = 0.629852682622; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.663 +- 0.271
194,mlp,_scale = True; activation = identity; alpha = 0.000435166593065; batch_size = auto; hidden_layer_sizes[0] = 27; hidden_layer_sizes[1] = 213; hidden_layer_sizes[2] = 294; learning_rate = invscaling; learning_rate_init = 0.374984887497; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.652 +- 0.386
195,mlp,_scale = True; activation = logistic; alpha = 0.00876333613234; batch_size = auto; hidden_layer_sizes[0] = 114; hidden_layer_sizes[1] = 250; hidden_layer_sizes[2] = 35; learning_rate = invscaling; learning_rate_init = 0.383422487694; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.728 +- 0.043
196,mlp,_scale = True; activation = logistic; alpha = 0.000302510140249; batch_size = auto; hidden_layer_sizes[0] = 287; hidden_layer_sizes[1] = 109; hidden_layer_sizes[2] = 107; learning_rate = invscaling; learning_rate_init = 0.297023534052; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.686 +- 0.047
197,mlp,_scale = True; activation = identity; alpha = 0.00096205457469; batch_size = auto; hidden_layer_sizes[0] = 91; hidden_layer_sizes[1] = 139; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.826 +- 0.148
198,mlp,_scale = True; activation = identity; alpha = 0.000503623924711; batch_size = auto; hidden_layer_sizes[0] = 104; hidden_layer_sizes[1] = 173; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.761 +- 0.178
199,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 6; weights = uniform,f1 and cv,0.720 +- 0.156
================================================
FILE: public/viz/datarun3_gp.csv
================================================
trail ID,method,parameters,metrics,performance
0,mlp,_scale = True; activation = tanh; alpha = 0.00781487257724; batch_size = auto; hidden_layer_sizes[0] = 272; hidden_layer_sizes[1] = 8; learning_rate = constant; learning_rate_init = 0.0689462938739; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.679 +- 0.214
1,mlp,_scale = True; activation = tanh; alpha = 0.00543619316979; batch_size = auto; hidden_layer_sizes[0] = 259; hidden_layer_sizes[1] = 270; learning_rate = constant; learning_rate_init = 0.687239967578; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.695 +- 0.480
2,mlp,_scale = True; activation = tanh; alpha = 0.00720261760942; batch_size = auto; hidden_layer_sizes[0] = 261; hidden_layer_sizes[1] = 264; learning_rate = constant; learning_rate_init = 0.63223458188; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.808 +- 0.364
3,mlp,_scale = True; activation = tanh; alpha = 0.000188613090589; batch_size = auto; hidden_layer_sizes[0] = 124; hidden_layer_sizes[1] = 62; learning_rate = constant; learning_rate_init = 0.47053941583; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.699 +- 0.418
4,mlp,_scale = True; activation = logistic; alpha = 0.0013054020274; batch_size = auto; hidden_layer_sizes[0] = 282; hidden_layer_sizes[1] = 274; hidden_layer_sizes[2] = 31; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.781 +- 0.378
5,mlp,_scale = True; activation = logistic; alpha = 0.00620897960824; batch_size = auto; hidden_layer_sizes[0] = 129; hidden_layer_sizes[1] = 259; hidden_layer_sizes[2] = 81; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.751 +- 0.414
6,mlp,_scale = True; activation = logistic; alpha = 0.00763163758209; batch_size = auto; hidden_layer_sizes[0] = 152; hidden_layer_sizes[1] = 54; hidden_layer_sizes[2] = 88; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.707 +- 0.480
7,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 1; weights = uniform,f1 and cv,0.616 +- 0.362
8,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 11; weights = uniform,f1 and cv,0.737 +- 0.128
9,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 11; weights = uniform,f1 and cv,0.703 +- 0.453
10,mlp,_scale = True; activation = relu; alpha = 0.00646205227618; batch_size = auto; hidden_layer_sizes[0] = 126; learning_rate = constant; learning_rate_init = 0.885586585832; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.806 +- 0.183
11,mlp,_scale = True; activation = relu; alpha = 0.00131287408101; batch_size = auto; hidden_layer_sizes[0] = 255; learning_rate = constant; learning_rate_init = 0.251704996158; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.655 +- 0.201
12,mlp,_scale = True; activation = relu; alpha = 0.00325404130072; batch_size = auto; hidden_layer_sizes[0] = 126; learning_rate = constant; learning_rate_init = 0.644087243875; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.748 +- 0.318
13,svm,C = 6.75579369313e-05; _scale = True; cache_size = 15000; class_weight = balanced; gamma = 1.39351729562; kernel = rbf; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.646 +- 0.050
14,svm,C = 3043.0257582; _scale = True; cache_size = 15000; class_weight = balanced; gamma = 54298.6093465; kernel = rbf; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.000 +- 0.000
15,gp,kernel = matern; nu = 2.5,f1 and cv,0.584 +- 0.164
16,mlp,_scale = True; activation = relu; alpha = 0.00159248380823; batch_size = auto; hidden_layer_sizes[0] = 291; learning_rate = invscaling; learning_rate_init = 0.829468493812; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.785 +- 0.306
17,mlp,_scale = True; activation = relu; alpha = 0.000701749141837; batch_size = auto; hidden_layer_sizes[0] = 26; learning_rate = invscaling; learning_rate_init = 0.322870349042; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.817 +- 0.287
18,mlp,_scale = True; activation = relu; alpha = 0.00588022555101; batch_size = auto; hidden_layer_sizes[0] = 26; learning_rate = invscaling; learning_rate_init = 0.298954761221; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.681 +- 0.210
19,knn,_scale = True; algorithm = ball_tree; leaf_size = 12; metric = chebyshev; n_neighbors = 18; weights = uniform,f1 and cv,0.713 +- 0.350
20,knn,_scale = True; algorithm = ball_tree; leaf_size = 49; metric = chebyshev; n_neighbors = 18; weights = uniform,f1 and cv,0.684 +- 0.338
21,mlp,_scale = True; activation = identity; alpha = 0.000748664694229; batch_size = auto; hidden_layer_sizes[0] = 105; hidden_layer_sizes[1] = 195; hidden_layer_sizes[2] = 136; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.647 +- 0.196
22,mlp,_scale = True; activation = identity; alpha = 0.000452395637246; batch_size = auto; hidden_layer_sizes[0] = 68; hidden_layer_sizes[1] = 23; hidden_layer_sizes[2] = 281; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.728 +- 0.174
23,mlp,_scale = True; activation = identity; alpha = 0.00161792293576; batch_size = auto; hidden_layer_sizes[0] = 195; learning_rate = constant; learning_rate_init = 0.365688011884; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.639 +- 0.167
24,mlp,_scale = True; activation = identity; alpha = 0.00349736877457; batch_size = auto; hidden_layer_sizes[0] = 12; learning_rate = constant; learning_rate_init = 0.770069409043; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.716 +- 0.285
25,svm,C = 0.0144774687611; _scale = True; cache_size = 15000; class_weight = balanced; coef0 = -897; degree = 5; gamma = 9.63953019022e-05; kernel = poly; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.654 +- 0.328
26,svm,C = 68361.5583244; _scale = True; cache_size = 15000; class_weight = balanced; coef0 = -681; degree = 2; gamma = 6976.80395927; kernel = poly; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.699 +- 0.243
27,mlp,_scale = True; activation = logistic; alpha = 0.00521335944684; batch_size = auto; beta_1 = 0.939521502808; beta_2 = 0.879544826331; hidden_layer_sizes[0] = 37; hidden_layer_sizes[1] = 145; learning_rate_init = 0.824208604962; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.646 +- 0.050
28,mlp,_scale = True; activation = logistic; alpha = 0.00227190507883; batch_size = auto; beta_1 = 0.898338191366; beta_2 = 0.910334662878; hidden_layer_sizes[0] = 154; hidden_layer_sizes[1] = 98; learning_rate_init = 0.0333173604139; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.482 +- 0.802
29,rf,criterion = gini; max_depth = 3; max_features = 0.872569897951; min_samples_leaf = 1; n_estimators = 100; n_jobs = -1,f1 and cv,0.698 +- 0.248
30,rf,criterion = gini; max_depth = 3; max_features = 0.412701118037; min_samples_leaf = 3; n_estimators = 100; n_jobs = -1,f1 and cv,0.802 +- 0.247
31,logreg,C = 5.5359579201; _scale = True; class_weight = balanced; fit_intercept = False; penalty = l1; tol = 1.95315997637e-05,f1 and cv,0.700 +- 0.448
32,logreg,C = 0.0392440215315; _scale = True; class_weight = balanced; fit_intercept = False; penalty = l1; tol = 16758.1398184,f1 and cv,0.000 +- 0.000
33,gnb,_scale_minmax = True,f1 and cv,0.746 +- 0.162
34,mlp,_scale = True; activation = relu; alpha = 0.00105918357866; batch_size = auto; hidden_layer_sizes[0] = 137; hidden_layer_sizes[1] = 271; learning_rate = constant; learning_rate_init = 0.603980751833; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.758 +- 0.169
35,mlp,_scale = True; activation = relu; alpha = 0.00893820258848; batch_size = auto; hidden_layer_sizes[0] = 4; hidden_layer_sizes[1] = 285; learning_rate = constant; learning_rate_init = 0.622789740096; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.574 +- 0.396
36,knn,_scale = True; algorithm = ball_tree; leaf_size = 22; metric = minkowski; n_neighbors = 11; p = 1; weights = distance,f1 and cv,0.661 +- 0.558
37,knn,_scale = True; algorithm = ball_tree; leaf_size = 10; metric = minkowski; n_neighbors = 10; p = 1; weights = distance,f1 and cv,0.787 +- 0.198
38,logreg,C = 1672.72436359; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 27100.381445,f1 and cv,0.850 +- 0.400
39,logreg,C = 0.00630214753888; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 72.2322909218,f1 and cv,0.690 +- 0.399
40,mlp,_scale = True; activation = relu; alpha = 0.00671090557438; batch_size = auto; hidden_layer_sizes[0] = 169; hidden_layer_sizes[1] = 245; learning_rate = invscaling; learning_rate_init = 0.320680427153; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.712 +- 0.428
41,mlp,_scale = True; activation = relu; alpha = 0.00166052683936; batch_size = auto; hidden_layer_sizes[0] = 57; hidden_layer_sizes[1] = 21; learning_rate = invscaling; learning_rate_init = 0.3787697594; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.715 +- 0.249
42,mlp,_scale = True; activation = identity; alpha = 0.00127587777598; batch_size = auto; hidden_layer_sizes[0] = 55; hidden_layer_sizes[1] = 261; hidden_layer_sizes[2] = 15; learning_rate = invscaling; learning_rate_init = 0.979302563433; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.379 +- 0.414
43,mlp,_scale = True; activation = identity; alpha = 0.00836452785254; batch_size = auto; hidden_layer_sizes[0] = 10; hidden_layer_sizes[1] = 294; hidden_layer_sizes[2] = 24; learning_rate = invscaling; learning_rate_init = 0.0867527859754; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.683 +- 0.395
44,mlp,_scale = True; activation = tanh; alpha = 0.00566483744806; batch_size = auto; beta_1 = 0.941060527496; beta_2 = 0.886333999219; hidden_layer_sizes[0] = 179; hidden_layer_sizes[1] = 163; learning_rate_init = 0.104313563407; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.737 +- 0.128
45,mlp,_scale = True; activation = tanh; alpha = 0.0014836698397; batch_size = auto; beta_1 = 0.866515711814; beta_2 = 0.963674603878; hidden_layer_sizes[0] = 162; hidden_layer_sizes[1] = 229; learning_rate_init = 0.239838223712; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.548 +- 0.255
46,logreg,C = 99.9229727841; _scale = True; class_weight = balanced; dual = False; fit_intercept = False; penalty = l2; tol = 244.185565265,f1 and cv,0.000 +- 0.000
47,mlp,_scale = True; activation = tanh; alpha = 0.00393734628798; batch_size = auto; hidden_layer_sizes[0] = 218; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.597 +- 0.255
48,mlp,_scale = True; activation = tanh; alpha = 0.00466952886902; batch_size = auto; hidden_layer_sizes[0] = 119; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.740 +- 0.502
49,mlp,_scale = True; activation = relu; alpha = 0.00369484939849; batch_size = auto; hidden_layer_sizes[0] = 172; hidden_layer_sizes[1] = 5; hidden_layer_sizes[2] = 117; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.760 +- 0.292
50,mlp,_scale = True; activation = relu; alpha = 0.00148965686068; batch_size = auto; hidden_layer_sizes[0] = 228; hidden_layer_sizes[1] = 258; hidden_layer_sizes[2] = 128; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.718 +- 0.239
51,mlp,_scale = True; activation = tanh; alpha = 0.00463285433881; batch_size = auto; hidden_layer_sizes[0] = 17; hidden_layer_sizes[1] = 60; learning_rate = invscaling; learning_rate_init = 0.677560559236; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.713 +- 0.142
52,mlp,_scale = True; activation = tanh; alpha = 0.00798742244388; batch_size = auto; hidden_layer_sizes[0] = 200; hidden_layer_sizes[1] = 130; learning_rate = invscaling; learning_rate_init = 0.980172261286; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.796 +- 0.289
53,mlp,_scale = True; activation = identity; alpha = 0.00561226799357; batch_size = auto; hidden_layer_sizes[0] = 36; hidden_layer_sizes[1] = 113; hidden_layer_sizes[2] = 51; learning_rate = constant; learning_rate_init = 0.359050106477; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.704 +- 0.227
54,mlp,_scale = True; activation = identity; alpha = 0.00522574484514; batch_size = auto; hidden_layer_sizes[0] = 32; hidden_layer_sizes[1] = 232; hidden_layer_sizes[2] = 184; learning_rate = constant; learning_rate_init = 0.390107009433; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.371 +- 0.732
55,knn,_scale = True; algorithm = kd_tree; leaf_size = 42; metric = minkowski; n_neighbors = 9; p = 2; weights = distance,f1 and cv,0.601 +- 0.126
56,knn,_scale = True; algorithm = kd_tree; leaf_size = 49; metric = minkowski; n_neighbors = 10; p = 2; weights = distance,f1 and cv,0.755 +- 0.339
57,mlp,_scale = True; activation = logistic; alpha = 0.00493108097032; batch_size = auto; hidden_layer_sizes[0] = 67; hidden_layer_sizes[1] = 135; learning_rate = invscaling; learning_rate_init = 0.0413872867992; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.707 +- 0.042
58,mlp,_scale = True; activation = logistic; alpha = 0.00749174273334; batch_size = auto; hidden_layer_sizes[0] = 244; hidden_layer_sizes[1] = 44; learning_rate = invscaling; learning_rate_init = 0.0678221650796; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.400 +- 0.653
59,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 4; weights = distance,f1 and cv,0.728 +- 0.421
60,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 10; weights = distance,f1 and cv,0.630 +- 0.485
61,svm,C = 108.321856002; _scale = True; cache_size = 15000; class_weight = balanced; kernel = linear; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.676 +- 0.306
62,svm,C = 0.22058306113; _scale = True; cache_size = 15000; class_weight = balanced; kernel = linear; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.522 +- 0.548
63,logreg,C = 402.075650797; _scale = True; class_weight = balanced; dual = False; fit_intercept = False; penalty = l2; tol = 422.081243442,f1 and cv,0.000 +- 0.000
64,mlp,_scale = True; activation = relu; alpha = 0.00447154493894; batch_size = auto; hidden_layer_sizes[0] = 133; hidden_layer_sizes[1] = 227; hidden_layer_sizes[2] = 48; learning_rate = adaptive; learning_rate_init = 0.435971730209; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.665 +- 0.459
65,mlp,_scale = True; activation = relu; alpha = 0.00877916277485; batch_size = auto; hidden_layer_sizes[0] = 150; hidden_layer_sizes[1] = 37; hidden_layer_sizes[2] = 274; learning_rate = adaptive; learning_rate_init = 0.0572993617058; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.672 +- 0.466
66,rf,criterion = entropy; max_depth = 5; max_features = 0.556456563127; min_samples_leaf = 2; n_estimators = 100; n_jobs = -1,f1 and cv,0.669 +- 0.443
67,rf,criterion = entropy; max_depth = 2; max_features = 0.98545646594; min_samples_leaf = 2; n_estimators = 100; n_jobs = -1,f1 and cv,0.726 +- 0.415
68,dt,criterion = entropy; max_depth = 5; max_features = 0.900550608963; min_samples_leaf = 1; min_samples_split = 3,f1 and cv,0.640 +- 0.491
69,dt,criterion = entropy; max_depth = 10; max_features = 0.910163275881; min_samples_leaf = 1; min_samples_split = 2,f1 and cv,0.632 +- 0.387
70,mlp,_scale = True; activation = identity; alpha = 0.00682078560952; batch_size = auto; beta_1 = 0.969193004536; beta_2 = 0.958225007994; hidden_layer_sizes[0] = 93; learning_rate_init = 0.913931202199; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.868 +- 0.177
71,mlp,_scale = True; activation = identity; alpha = 0.00645779541489; batch_size = auto; beta_1 = 0.913476793807; beta_2 = 0.949688737801; hidden_layer_sizes[0] = 102; learning_rate_init = 0.133057916229; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.680 +- 0.422
72,gp,kernel = rbf,f1 and cv,0.000 +- 0.000
73,mlp,_scale = True; activation = logistic; alpha = 0.00446415534919; batch_size = auto; hidden_layer_sizes[0] = 270; learning_rate = adaptive; learning_rate_init = 0.595166464296; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.565 +- 0.566
74,mlp,_scale = True; activation = logistic; alpha = 0.00047361483565; batch_size = auto; hidden_layer_sizes[0] = 199; learning_rate = adaptive; learning_rate_init = 0.115392078419; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.742 +- 0.323
75,mlp,_scale = True; activation = identity; alpha = 0.00334901377344; batch_size = auto; hidden_layer_sizes[0] = 191; hidden_layer_sizes[1] = 196; learning_rate = invscaling; learning_rate_init = 0.385631095821; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.695 +- 0.422
76,mlp,_scale = True; activation = identity; alpha = 0.000729809390053; batch_size = auto; hidden_layer_sizes[0] = 191; hidden_layer_sizes[1] = 9; learning_rate = invscaling; learning_rate_init = 0.376426628893; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.771 +- 0.272
77,mlp,_scale = True; activation = relu; alpha = 0.00677952040327; batch_size = auto; hidden_layer_sizes[0] = 78; hidden_layer_sizes[1] = 203; learning_rate = adaptive; learning_rate_init = 0.552285906706; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.705 +- 0.236
78,mlp,_scale = True; activation = relu; alpha = 0.00484606282802; batch_size = auto; hidden_layer_sizes[0] = 121; hidden_layer_sizes[1] = 32; learning_rate = adaptive; learning_rate_init = 0.525993295773; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.669 +- 0.394
79,knn,_scale = True; algorithm = kd_tree; leaf_size = 38; metric = minkowski; n_neighbors = 1; p = 2; weights = uniform,f1 and cv,0.624 +- 0.254
80,knn,_scale = True; algorithm = kd_tree; leaf_size = 36; metric = minkowski; n_neighbors = 6; p = 3; weights = uniform,f1 and cv,0.736 +- 0.408
81,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 9; weights = distance,f1 and cv,0.710 +- 0.346
82,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 20; weights = distance,f1 and cv,0.875 +- 0.077
83,mlp,_scale = True; activation = logistic; alpha = 0.00455678708933; batch_size = auto; hidden_layer_sizes[0] = 226; hidden_layer_sizes[1] = 289; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.557 +- 0.301
84,mlp,_scale = True; activation = logistic; alpha = 0.00770313808357; batch_size = auto; hidden_layer_sizes[0] = 114; hidden_layer_sizes[1] = 131; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.703 +- 0.434
85,gp,kernel = exp_sine_squared; length_scale = 88.5811912348; periodicity = 1,f1 and cv,0.000 +- 0.000
86,mlp,_scale = True; activation = identity; alpha = 0.00563614677265; batch_size = auto; hidden_layer_sizes[0] = 225; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.638 +- 0.284
87,mlp,_scale = True; activation = identity; alpha = 0.00530367936218; batch_size = auto; hidden_layer_sizes[0] = 239; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.772 +- 0.212
88,mlp,_scale = True; activation = logistic; alpha = 0.00135750262202; batch_size = auto; hidden_layer_sizes[0] = 201; hidden_layer_sizes[1] = 173; learning_rate = adaptive; learning_rate_init = 0.704545467478; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.267 +- 0.653
89,mlp,_scale = True; activation = logistic; alpha = 0.00781371196677; batch_size = auto; hidden_layer_sizes[0] = 226; hidden_layer_sizes[1] = 129; learning_rate = adaptive; learning_rate_init = 0.38252192812; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.543 +- 0.544
90,mlp,_scale = True; activation = tanh; alpha = 0.000225589838368; batch_size = auto; hidden_layer_sizes[0] = 70; learning_rate = invscaling; learning_rate_init = 0.24047934953; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.796 +- 0.169
91,mlp,_scale = True; activation = tanh; alpha = 0.0014621770276; batch_size = auto; hidden_layer_sizes[0] = 278; learning_rate = invscaling; learning_rate_init = 0.526678709592; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.849 +- 0.222
92,mlp,_scale = True; activation = logistic; alpha = 0.000395881669625; batch_size = auto; beta_1 = 0.977413026071; beta_2 = 0.824084664119; hidden_layer_sizes[0] = 160; hidden_layer_sizes[1] = 153; hidden_layer_sizes[2] = 63; learning_rate_init = 0.908710334663; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.543 +- 0.544
93,mlp,_scale = True; activation = logistic; alpha = 0.000926257945602; batch_size = auto; beta_1 = 0.909017076342; beta_2 = 0.904584995376; hidden_layer_sizes[0] = 208; hidden_layer_sizes[1] = 36; hidden_layer_sizes[2] = 2; learning_rate_init = 0.894332904907; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.279 +- 0.684
94,mlp,_scale = True; activation = tanh; alpha = 0.00752796608981; batch_size = auto; beta_1 = 0.843095206821; beta_2 = 0.954782682046; hidden_layer_sizes[0] = 43; hidden_layer_sizes[1] = 38; hidden_layer_sizes[2] = 272; learning_rate_init = 0.678536955782; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.654 +- 0.538
95,mlp,_scale = True; activation = tanh; alpha = 0.00104497384797; batch_size = auto; beta_1 = 0.946565507377; beta_2 = 0.809265708154; hidden_layer_sizes[0] = 39; hidden_layer_sizes[1] = 238; hidden_layer_sizes[2] = 134; learning_rate_init = 0.176380045981; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.759 +- 0.276
96,knn,_scale = True; algorithm = kd_tree; leaf_size = 35; metric = manhattan; n_neighbors = 7; weights = distance,f1 and cv,0.633 +- 0.311
97,knn,_scale = True; algorithm = kd_tree; leaf_size = 39; metric = manhattan; n_neighbors = 18; weights = distance,f1 and cv,0.895 +- 0.256
98,knn,_scale = True; algorithm = kd_tree; leaf_size = 4; metric = euclidean; n_neighbors = 19; weights = distance,f1 and cv,0.635 +- 0.676
99,knn,_scale = True; algorithm = kd_tree; leaf_size = 18; metric = euclidean; n_neighbors = 5; weights = distance,f1 and cv,0.599 +- 0.519
100,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 6; weights = uniform,f1 and cv,0.653 +- 0.272
101,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 5; weights = uniform,f1 and cv,0.763 +- 0.198
102,logreg,C = 4.97088705431e-05; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 222.17699589,f1 and cv,0.744 +- 0.266
103,logreg,C = 0.330721375771; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 0.0847406446969,f1 and cv,0.756 +- 0.218
104,mlp,_scale = True; activation = identity; alpha = 0.00388200759279; batch_size = auto; beta_1 = 0.803310010916; beta_2 = 0.847518868014; hidden_layer_sizes[0] = 3; hidden_layer_sizes[1] = 253; hidden_layer_sizes[2] = 240; learning_rate_init = 0.779752504983; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.665 +- 0.304
105,mlp,_scale = True; activation = identity; alpha = 0.00405917657162; batch_size = auto; beta_1 = 0.948791716906; beta_2 = 0.877991922699; hidden_layer_sizes[0] = 211; hidden_layer_sizes[1] = 268; hidden_layer_sizes[2] = 148; learning_rate_init = 0.206409734509; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.621 +- 0.646
106,mlp,_scale = True; activation = logistic; alpha = 0.00107775400387; batch_size = auto; hidden_layer_sizes[0] = 38; hidden_layer_sizes[1] = 260; hidden_layer_sizes[2] = 117; learning_rate = invscaling; learning_rate_init = 0.879719962022; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.585 +- 0.586
107,mlp,_scale = True; activation = logistic; alpha = 0.00746351178429; batch_size = auto; hidden_layer_sizes[0] = 235; hidden_layer_sizes[1] = 209; hidden_layer_sizes[2] = 139; learning_rate = invscaling; learning_rate_init = 0.739358703115; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.565 +- 0.566
108,mlp,_scale = True; activation = relu; alpha = 0.00755618547266; batch_size = auto; beta_1 = 0.818251038404; beta_2 = 0.804836838455; hidden_layer_sizes[0] = 156; hidden_layer_sizes[1] = 292; hidden_layer_sizes[2] = 229; learning_rate_init = 0.231899738619; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.499 +- 0.551
109,mlp,_scale = True; activation = relu; alpha = 0.000890173654937; batch_size = auto; beta_1 = 0.888752607999; beta_2 = 0.905503364471; hidden_layer_sizes[0] = 115; hidden_layer_sizes[1] = 116; hidden_layer_sizes[2] = 74; learning_rate_init = 0.496824150454; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.463 +- 0.593
110,mlp,_scale = True; activation = tanh; alpha = 0.00430648251732; batch_size = auto; hidden_layer_sizes[0] = 57; hidden_layer_sizes[1] = 200; hidden_layer_sizes[2] = 224; learning_rate = invscaling; learning_rate_init = 0.263792190615; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.683 +- 0.377
111,mlp,_scale = True; activation = tanh; alpha = 0.00792514853509; batch_size = auto; hidden_layer_sizes[0] = 58; hidden_layer_sizes[1] = 298; hidden_layer_sizes[2] = 209; learning_rate = invscaling; learning_rate_init = 0.504780015822; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.798 +- 0.417
112,mlp,_scale = True; activation = identity; alpha = 0.000574869927736; batch_size = auto; hidden_layer_sizes[0] = 58; hidden_layer_sizes[1] = 200; learning_rate = constant; learning_rate_init = 0.0847381122613; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.734 +- 0.139
113,mlp,_scale = True; activation = identity; alpha = 0.00748872282397; batch_size = auto; hidden_layer_sizes[0] = 243; hidden_layer_sizes[1] = 215; learning_rate = constant; learning_rate_init = 0.864295574622; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.567 +- 0.265
114,gp,kernel = exp_sine_squared; length_scale = 1.23258725702; periodicity = 1,f1 and cv,0.534 +- 0.554
115,mlp,_scale = True; activation = identity; alpha = 0.00146328031265; batch_size = auto; hidden_layer_sizes[0] = 273; hidden_layer_sizes[1] = 261; hidden_layer_sizes[2] = 17; learning_rate = adaptive; learning_rate_init = 0.662340589203; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.000 +- 0.000
116,mlp,_scale = True; activation = logistic; alpha = 0.00168729100821; batch_size = auto; beta_1 = 0.862405862008; beta_2 = 0.863444265573; hidden_layer_sizes[0] = 90; learning_rate_init = 0.921266595151; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.640 +- 0.676
117,mlp,_scale = True; activation = logistic; alpha = 0.00216221770935; batch_size = auto; beta_1 = 0.854074335995; beta_2 = 0.915921532413; hidden_layer_sizes[0] = 20; learning_rate_init = 0.734002657964; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.718 +- 0.209
118,mlp,_scale = True; activation = identity; alpha = 0.00520025456826; batch_size = auto; hidden_layer_sizes[0] = 165; hidden_layer_sizes[1] = 154; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.716 +- 0.441
119,mlp,_scale = True; activation = identity; alpha = 0.00547849454578; batch_size = auto; hidden_layer_sizes[0] = 162; hidden_layer_sizes[1] = 291; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.758 +- 0.409
120,mlp,_scale = True; activation = relu; alpha = 0.00468474485894; batch_size = auto; hidden_layer_sizes[0] = 160; hidden_layer_sizes[1] = 208; hidden_layer_sizes[2] = 237; learning_rate = constant; learning_rate_init = 0.124026768608; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.775 +- 0.306
121,mlp,_scale = True; activation = relu; alpha = 0.00585513262572; batch_size = auto; hidden_layer_sizes[0] = 216; hidden_layer_sizes[1] = 191; hidden_layer_sizes[2] = 225; learning_rate = constant; learning_rate_init = 0.316155619041; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.742 +- 0.244
122,knn,_scale = True; algorithm = ball_tree; leaf_size = 39; metric = euclidean; n_neighbors = 13; weights = distance,f1 and cv,0.782 +- 0.323
123,knn,_scale = True; algorithm = ball_tree; leaf_size = 29; metric = euclidean; n_neighbors = 20; weights = distance,f1 and cv,0.741 +- 0.164
124,svm,C = 4418.48406248; _scale = True; cache_size = 15000; class_weight = balanced; coef0 = -410; gamma = 0.0128200591314; kernel = sigmoid; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.000 +- 0.000
125,knn,_scale = True; algorithm = ball_tree; leaf_size = 16; metric = chebyshev; n_neighbors = 15; weights = distance,f1 and cv,0.660 +- 0.393
126,knn,_scale = True; algorithm = ball_tree; leaf_size = 14; metric = chebyshev; n_neighbors = 20; weights = distance,f1 and cv,0.585 +- 0.366
127,knn,_scale = True; algorithm = ball_tree; leaf_size = 35; metric = minkowski; n_neighbors = 7; p = 3; weights = uniform,f1 and cv,0.667 +- 0.146
128,knn,_scale = True; algorithm = ball_tree; leaf_size = 1; metric = minkowski; n_neighbors = 19; p = 3; weights = uniform,f1 and cv,0.623 +- 0.441
129,gp,kernel = matern; nu = 0.5,f1 and cv,0.451 +- 0.198
130,mlp,_scale = True; activation = logistic; alpha = 0.000492057617733; batch_size = auto; hidden_layer_sizes[0] = 183; hidden_layer_sizes[1] = 44; hidden_layer_sizes[2] = 22; learning_rate = adaptive; learning_rate_init = 0.0803546188134; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.747 +- 0.054
131,mlp,_scale = True; activation = logistic; alpha = 0.0061092024869; batch_size = auto; hidden_layer_sizes[0] = 245; hidden_layer_sizes[1] = 225; hidden_layer_sizes[2] = 115; learning_rate = adaptive; learning_rate_init = 0.261554783012; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.246 +- 0.603
132,dt,criterion = gini; max_depth = 9; max_features = 0.20530013294; min_samples_leaf = 3; min_samples_split = 4,f1 and cv,0.722 +- 0.431
133,dt,criterion = gini; max_depth = 5; max_features = 0.895429193079; min_samples_leaf = 2; min_samples_split = 4,f1 and cv,0.671 +- 0.226
134,mlp,_scale = True; activation = logistic; alpha = 0.00493337414245; batch_size = auto; hidden_layer_sizes[0] = 225; hidden_layer_sizes[1] = 249; learning_rate = constant; learning_rate_init = 0.388841600493; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.143 +- 0.571
135,mlp,_scale = True; activation = logistic; alpha = 0.00107313907428; batch_size = auto; hidden_layer_sizes[0] = 125; hidden_layer_sizes[1] = 79; learning_rate = constant; learning_rate_init = 0.913790170325; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.747 +- 0.054
136,svm,C = 0.000699346092196; _scale = True; cache_size = 15000; class_weight = balanced; coef0 = -356; gamma = 2904.70602162; kernel = sigmoid; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.646 +- 0.050
137,mlp,_scale = True; activation = identity; alpha = 0.00734266673854; batch_size = auto; beta_1 = 0.961171981627; beta_2 = 0.888975972278; hidden_layer_sizes[0] = 257; hidden_layer_sizes[1] = 298; learning_rate_init = 0.414234249407; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.714 +- 0.328
138,mlp,_scale = True; activation = identity; alpha = 0.00161530133551; batch_size = auto; beta_1 = 0.936825982064; beta_2 = 0.95893046097; hidden_layer_sizes[0] = 164; hidden_layer_sizes[1] = 125; learning_rate_init = 0.453433432399; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.659 +- 0.427
139,mlp,_scale = True; activation = relu; alpha = 0.00163845695715; batch_size = auto; hidden_layer_sizes[0] = 284; learning_rate = adaptive; learning_rate_init = 0.429799911569; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.768 +- 0.312
140,mlp,_scale = True; activation = relu; alpha = 0.00657294944483; batch_size = auto; hidden_layer_sizes[0] = 203; learning_rate = adaptive; learning_rate_init = 0.451312921609; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.827 +- 0.195
141,knn,_scale = True; algorithm = ball_tree; leaf_size = 28; metric = manhattan; n_neighbors = 4; weights = uniform,f1 and cv,0.758 +- 0.155
142,knn,_scale = True; algorithm = ball_tree; leaf_size = 46; metric = manhattan; n_neighbors = 12; weights = uniform,f1 and cv,0.562 +- 0.382
143,mlp,_scale = True; activation = tanh; alpha = 0.00476005930825; batch_size = auto; beta_1 = 0.837603101953; beta_2 = 0.955572625664; hidden_layer_sizes[0] = 62; learning_rate_init = 0.836221756772; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.749 +- 0.283
144,mlp,_scale = True; activation = tanh; alpha = 0.00686387329794; batch_size = auto; beta_1 = 0.893992260512; beta_2 = 0.831867746086; hidden_layer_sizes[0] = 206; learning_rate_init = 0.453787250521; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.677 +- 0.361
145,mlp,_scale = True; activation = tanh; alpha = 0.00288630618482; batch_size = auto; hidden_layer_sizes[0] = 291; hidden_layer_sizes[1] = 85; learning_rate = adaptive; learning_rate_init = 0.456429072586; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.549 +- 0.559
146,mlp,_scale = True; activation = tanh; alpha = 0.00213734631388; batch_size = auto; hidden_layer_sizes[0] = 264; hidden_layer_sizes[1] = 39; learning_rate = adaptive; learning_rate_init = 0.137660603614; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.819 +- 0.282
147,mlp,_scale = True; activation = relu; alpha = 0.000527020841096; batch_size = auto; hidden_layer_sizes[0] = 270; hidden_layer_sizes[1] = 119; hidden_layer_sizes[2] = 191; learning_rate = invscaling; learning_rate_init = 0.132056020654; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.704 +- 0.265
148,mlp,_scale = True; activation = relu; alpha = 0.000167336640832; batch_size = auto; hidden_layer_sizes[0] = 119; hidden_layer_sizes[1] = 138; hidden_layer_sizes[2] = 79; learning_rate = invscaling; learning_rate_init = 0.579541176656; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.783 +- 0.389
149,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 2; p = 2; weights = distance,f1 and cv,0.577 +- 0.397
150,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 5; p = 1; weights = distance,f1 and cv,0.758 +- 0.297
151,mlp,_scale = True; activation = tanh; alpha = 0.00830371973064; batch_size = auto; hidden_layer_sizes[0] = 215; learning_rate = adaptive; learning_rate_init = 0.217830033624; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.735 +- 0.218
152,mlp,_scale = True; activation = tanh; alpha = 0.00593473166394; batch_size = auto; hidden_layer_sizes[0] = 156; learning_rate = adaptive; learning_rate_init = 0.0917559046633; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.592 +- 0.701
153,knn,_scale = True; algorithm = kd_tree; leaf_size = 22; metric = manhattan; n_neighbors = 17; weights = uniform,f1 and cv,0.617 +- 0.663
154,knn,_scale = True; algorithm = kd_tree; leaf_size = 31; metric = manhattan; n_neighbors = 20; weights = uniform,f1 and cv,0.656 +- 0.478
155,logreg,C = 0.00306769302685; _scale = True; class_weight = balanced; dual = False; fit_intercept = True; penalty = l2; tol = 0.012801448798,f1 and cv,0.788 +- 0.379
156,logreg,C = 177.566822646; _scale = True; class_weight = balanced; dual = False; fit_intercept = True; penalty = l2; tol = 36300.246269,f1 and cv,0.000 +- 0.000
157,mlp,_scale = True; activation = logistic; alpha = 0.00292235765101; batch_size = auto; hidden_layer_sizes[0] = 219; hidden_layer_sizes[1] = 215; hidden_layer_sizes[2] = 261; learning_rate = constant; learning_rate_init = 0.463754226406; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.562 +- 0.563
158,mlp,_scale = True; activation = logistic; alpha = 0.00336265552639; batch_size = auto; hidden_layer_sizes[0] = 134; hidden_layer_sizes[1] = 288; hidden_layer_sizes[2] = 113; learning_rate = constant; learning_rate_init = 0.106379752839; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.000 +- 0.000
159,knn,_scale = True; algorithm = kd_tree; leaf_size = 20; metric = chebyshev; n_neighbors = 3; weights = distance,f1 and cv,0.660 +- 0.350
160,knn,_scale = True; algorithm = kd_tree; leaf_size = 15; metric = chebyshev; n_neighbors = 11; weights = distance,f1 and cv,0.651 +- 0.217
161,mlp,_scale = True; activation = identity; alpha = 0.000333265318374; batch_size = auto; hidden_layer_sizes[0] = 13; hidden_layer_sizes[1] = 227; learning_rate = adaptive; learning_rate_init = 0.121745354886; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.770 +- 0.286
162,mlp,_scale = True; activation = identity; alpha = 0.00294752057394; batch_size = auto; hidden_layer_sizes[0] = 128; hidden_layer_sizes[1] = 205; learning_rate = adaptive; learning_rate_init = 0.598308980669; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.617 +- 0.347
163,mlp,_scale = True; activation = identity; alpha = 0.00516176907568; batch_size = auto; hidden_layer_sizes[0] = 194; hidden_layer_sizes[1] = 79; hidden_layer_sizes[2] = 153; learning_rate = adaptive; learning_rate_init = 0.353882854198; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.584 +- 0.372
164,knn,_scale = True; algorithm = ball_tree; leaf_size = 38; metric = manhattan; n_neighbors = 9; weights = distance,f1 and cv,0.739 +- 0.230
165,knn,_scale = True; algorithm = ball_tree; leaf_size = 39; metric = manhattan; n_neighbors = 8; weights = distance,f1 and cv,0.686 +- 0.401
166,mlp,_scale = True; activation = tanh; alpha = 0.00335381389702; batch_size = auto; hidden_layer_sizes[0] = 221; learning_rate = constant; learning_rate_init = 0.0543684388061; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.729 +- 0.266
167,mlp,_scale = True; activation = tanh; alpha = 0.000718678014138; batch_size = auto; hidden_layer_sizes[0] = 194; learning_rate = constant; learning_rate_init = 0.817123258156; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.699 +- 0.285
168,mlp,_scale = True; activation = identity; alpha = 0.00742865324697; batch_size = auto; hidden_layer_sizes[0] = 7; learning_rate = adaptive; learning_rate_init = 0.104806834524; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.691 +- 0.059
169,mlp,_scale = True; activation = identity; alpha = 0.00842933174699; batch_size = auto; hidden_layer_sizes[0] = 145; learning_rate = adaptive; learning_rate_init = 0.47682224929; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.675 +- 0.272
170,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 10; weights = distance,f1 and cv,0.760 +- 0.113
171,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 4; weights = distance,f1 and cv,0.636 +- 0.180
172,gp,kernel = exp_sine_squared; length_scale = 6.13840945438; periodicity = 0,f1 and cv,0.574 +- 0.574
173,gp,kernel = exp_sine_squared; length_scale = 27.2598804204; periodicity = 0,f1 and cv,0.000 +- 0.000
174,mlp,_scale = True; activation = tanh; alpha = 0.00389901170727; batch_size = auto; hidden_layer_sizes[0] = 31; hidden_layer_sizes[1] = 12; hidden_layer_sizes[2] = 292; learning_rate = constant; learning_rate_init = 0.352909877432; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.681 +- 0.343
175,mlp,_scale = True; activation = tanh; alpha = 0.00861460532622; batch_size = auto; hidden_layer_sizes[0] = 2; hidden_layer_sizes[1] = 251; hidden_layer_sizes[2] = 239; learning_rate = constant; learning_rate_init = 0.802964762108; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.715 +- 0.249
176,mlp,_scale = True; activation = tanh; alpha = 0.00796704569567; batch_size = auto; hidden_layer_sizes[0] = 179; hidden_layer_sizes[1] = 7; hidden_layer_sizes[2] = 157; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.752 +- 0.207
177,mlp,_scale = True; activation = tanh; alpha = 0.00108419022247; batch_size = auto; hidden_layer_sizes[0] = 283; hidden_layer_sizes[1] = 73; hidden_layer_sizes[2] = 51; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.641 +- 0.121
178,knn,_scale = True; algorithm = ball_tree; leaf_size = 21; metric = euclidean; n_neighbors = 7; weights = uniform,f1 and cv,0.695 +- 0.235
179,knn,_scale = True; algorithm = ball_tree; leaf_size = 42; metric = euclidean; n_neighbors = 13; weights = uniform,f1 and cv,0.705 +- 0.435
180,mlp,_scale = True; activation = tanh; alpha = 0.00354606375968; batch_size = auto; hidden_layer_sizes[0] = 10; hidden_layer_sizes[1] = 27; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.775 +- 0.128
181,mlp,_scale = True; activation = tanh; alpha = 0.00774998611852; batch_size = auto; hidden_layer_sizes[0] = 206; hidden_layer_sizes[1] = 140; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.756 +- 0.242
182,gp,kernel = constant,f1 and cv,0.728 +- 0.043
183,logreg,C = 1250.42827557; _scale = True; class_weight = balanced; fit_intercept = True; penalty = l1; tol = 0.00118418554271,f1 and cv,0.703 +- 0.157
184,logreg,C = 0.000256996185277; _scale = True; class_weight = balanced; fit_intercept = True; penalty = l1; tol = 10261.6124623,f1 and cv,0.000 +- 0.000
185,mlp,_scale = True; activation = logistic; alpha = 0.00014692611716; batch_size = auto; hidden_layer_sizes[0] = 42; learning_rate = constant; learning_rate_init = 0.974463794961; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.527 +- 0.560
186,mlp,_scale = True; activation = logistic; alpha = 0.0044263395726; batch_size = auto; hidden_layer_sizes[0] = 173; learning_rate = constant; learning_rate_init = 0.696057688579; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.475 +- 0.478
187,mlp,_scale = True; activation = tanh; alpha = 0.00241873404932; batch_size = auto; hidden_layer_sizes[0] = 219; hidden_layer_sizes[1] = 187; hidden_layer_sizes[2] = 300; learning_rate = adaptive; learning_rate_init = 0.687763327145; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.566 +- 0.376
188,mlp,_scale = True; activation = tanh; alpha = 0.0038955923106; batch_size = auto; hidden_layer_sizes[0] = 244; hidden_layer_sizes[1] = 124; hidden_layer_sizes[2] = 211; learning_rate = adaptive; learning_rate_init = 0.881892229762; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.693 +- 0.136
189,gp,kernel = matern; nu = 1.5,f1 and cv,0.643 +- 0.403
190,knn,_scale = True; algorithm = kd_tree; leaf_size = 1; metric = euclidean; n_neighbors = 17; weights = uniform,f1 and cv,0.667 +- 0.183
191,knn,_scale = True; algorithm = kd_tree; leaf_size = 29; metric = euclidean; n_neighbors = 16; weights = uniform,f1 and cv,0.706 +- 0.264
192,mnb,_scale_minmax = True; alpha = 0.876309586449; class_prior = None; fit_prior = 1,f1 and cv,0.788 +- 0.051
193,mnb,_scale_minmax = True; alpha = 0.688666400872; class_prior = None; fit_prior = 0,f1 and cv,0.737 +- 0.120
194,knn,_scale = True; algorithm = kd_tree; leaf_size = 38; metric = chebyshev; n_neighbors = 14; weights = uniform,f1 and cv,0.777 +- 0.225
195,knn,_scale = True; algorithm = kd_tree; leaf_size = 27; metric = chebyshev; n_neighbors = 19; weights = uniform,f1 and cv,0.742 +- 0.112
196,mlp,_scale = True; activation = relu; alpha = 0.00118456803489; batch_size = auto; hidden_layer_sizes[0] = 29; hidden_layer_sizes[1] = 97; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.655 +- 0.337
197,mlp,_scale = True; activation = relu; alpha = 0.00716166598187; batch_size = auto; hidden_layer_sizes[0] = 5; hidden_layer_sizes[1] = 171; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.683 +- 0.427
198,mlp,_scale = True; activation = relu; alpha = 0.00498911153908; batch_size = auto; beta_1 = 0.896843350434; beta_2 = 0.9872568151; hidden_layer_sizes[0] = 57; hidden_layer_sizes[1] = 104; learning_rate_init = 0.647266236782; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.100 +- 0.400
199,mlp,_scale = True; activation = relu; alpha = 0.0086748781932; batch_size = auto; beta_1 = 0.808846389859; beta_2 = 0.918761804441; hidden_layer_sizes[0] = 5; hidden_layer_sizes[1] = 116; learning_rate_init = 0.839328146746; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.516 +- 0.581
200,mlp,_scale = True; activation = logistic; alpha = 0.00791651056937; batch_size = auto; hidden_layer_sizes[0] = 96; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.702 +- 0.363
201,mlp,_scale = True; activation = logistic; alpha = 0.00761269673425; batch_size = auto; hidden_layer_sizes[0] = 195; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.741 +- 0.281
202,mlp,_scale = True; activation = logistic; alpha = 0.00599579013339; batch_size = auto; hidden_layer_sizes[0] = 132; learning_rate = invscaling; learning_rate_init = 0.878033337806; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.739 +- 0.178
203,mlp,_scale = True; activation = logistic; alpha = 0.00819682558214; batch_size = auto; hidden_layer_sizes[0] = 243; learning_rate = invscaling; learning_rate_init = 0.807274141581; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.567 +- 0.653
204,mlp,_scale = True; activation = relu; alpha = 0.0014727938631; batch_size = auto; hidden_layer_sizes[0] = 237; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.800 +- 0.116
205,mlp,_scale = True; activation = relu; alpha = 0.00554646313568; batch_size = auto; hidden_layer_sizes[0] = 124; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.743 +- 0.264
206,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 20; weights = uniform,f1 and cv,0.619 +- 0.687
207,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 9; weights = uniform,f1 and cv,0.732 +- 0.216
208,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 6; p = 1; weights = uniform,f1 and cv,0.819 +- 0.152
209,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 14; p = 3; weights = uniform,f1 and cv,0.664 +- 0.262
210,gp,alpha = 0.441179556454; kernel = rational_quadratic; length_scale = 0.0590781492897,f1 and cv,0.577 +- 0.378
211,gp,alpha = 0.394747125512; kernel = rational_quadratic; length_scale = 0.454814209874,f1 and cv,0.620 +- 0.131
212,mlp,_scale = True; activation = relu; alpha = 0.00384623541841; batch_size = auto; beta_1 = 0.937909544848; beta_2 = 0.889828852962; hidden_layer_sizes[0] = 260; learning_rate_init = 0.981211355904; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.682 +- 0.311
213,mlp,_scale = True; activation = relu; alpha = 0.00365876945699; batch_size = auto; beta_1 = 0.894011255549; beta_2 = 0.802233668538; hidden_layer_sizes[0] = 27; learning_rate_init = 0.406507847967; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.745 +- 0.274
214,mlp,_scale = True; activation = identity; alpha = 0.00845645787211; batch_size = auto; hidden_layer_sizes[0] = 273; learning_rate = invscaling; learning_rate_init = 0.445585302825; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.767 +- 0.149
215,mlp,_scale = True; activation = identity; alpha = 0.00108570440386; batch_size = auto; hidden_layer_sizes[0] = 121; learning_rate = invscaling; learning_rate_init = 0.895340433661; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.864 +- 0.219
216,mlp,_scale = True; activation = tanh; alpha = 0.000728993751142; batch_size = auto; hidden_layer_sizes[0] = 278; learning_rate = invscaling; learning_rate_init = 0.670393489284; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.657 +- 0.276
217,mlp,_scale = True; activation = identity; alpha = 0.00590165715418; batch_size = auto; hidden_layer_sizes[0] = 121; learning_rate = invscaling; learning_rate_init = 0.86357115334; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.777 +- 0.245
218,mlp,_scale = True; activation = relu; alpha = 0.00458576632242; batch_size = auto; hidden_layer_sizes[0] = 203; learning_rate = adaptive; learning_rate_init = 0.303130900389; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.622 +- 0.207
219,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 20; weights = distance,f1 and cv,0.754 +- 0.393
220,mlp,_scale = True; activation = identity; alpha = 0.0061414985785; batch_size = auto; beta_1 = 0.850813944282; beta_2 = 0.819958129268; hidden_layer_sizes[0] = 93; learning_rate_init = 0.743269273633; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.761 +- 0.209
221,mlp,_scale = True; activation = relu; alpha = 0.00301544594515; batch_size = auto; hidden_layer_sizes[0] = 237; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.734 +- 0.086
222,logreg,C = 316.540829698; _scale = True; class_weight = balanced; dual = True; fit_intercept = False; penalty = l2; tol = 113.651538457,f1 and cv,0.749 +- 0.297
223,mlp,_scale = True; activation = tanh; alpha = 0.00748704668471; batch_size = auto; hidden_layer_sizes[0] = 10; hidden_layer_sizes[1] = 20; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.602 +- 0.637
224,knn,_scale = True; algorithm = kd_tree; leaf_size = 38; metric = manhattan; n_neighbors = 18; weights = distance,f1 and cv,0.672 +- 0.463
225,mnb,_scale_minmax = True; alpha = 0.998765544364; class_prior = None; fit_prior = 1,f1 and cv,0.670 +- 0.229
226,knn,_scale = True; algorithm = ball_tree; leaf_size = 39; metric = euclidean; n_neighbors = 13; weights = distance,f1 and cv,0.703 +- 0.324
227,knn,_scale = True; algorithm = kd_tree; leaf_size = 38; metric = chebyshev; n_neighbors = 14; weights = uniform,f1 and cv,0.762 +- 0.128
228,mlp,_scale = True; activation = relu; alpha = 0.000284386971655; batch_size = auto; hidden_layer_sizes[0] = 151; hidden_layer_sizes[1] = 3; hidden_layer_sizes[2] = 103; learning_rate = constant; learning_rate_init = 0.695437589557; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.696 +- 0.192
229,mlp,_scale = True; activation = tanh; alpha = 0.00280894987658; batch_size = auto; hidden_layer_sizes[0] = 198; hidden_layer_sizes[1] = 135; learning_rate = invscaling; learning_rate_init = 0.317156548818; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.865 +- 0.253
230,logreg,C = 0.379743154494; _scale = True; class_weight = balanced; dual = True; fit_intercept = True; penalty = l2; tol = 0.069387906243,f1 and cv,0.784 +- 0.242
231,rf,criterion = gini; max_depth = 3; max_features = 0.414843892414; min_samples_leaf = 3; n_estimators = 100; n_jobs = -1,f1 and cv,0.802 +- 0.307
232,mlp,_scale = True; activation = relu; alpha = 0.00546833979991; batch_size = auto; hidden_layer_sizes[0] = 108; hidden_layer_sizes[1] = 193; hidden_layer_sizes[2] = 34; learning_rate = invscaling; learning_rate_init = 0.189587204661; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.781 +- 0.163
233,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 6; p = 1; weights = uniform,f1 and cv,0.812 +- 0.379
234,mlp,_scale = True; activation = tanh; alpha = 0.00823992812488; batch_size = auto; hidden_layer_sizes[0] = 295; hidden_layer_sizes[1] = 294; hidden_layer_sizes[2] = 44; learning_rate = invscaling; learning_rate_init = 0.481404118046; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.794 +- 0.209
235,mlp,_scale = True; activation = relu; alpha = 0.00399410990265; batch_size = auto; hidden_layer_sizes[0] = 233; hidden_layer_sizes[1] = 17; hidden_layer_sizes[2] = 9; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.783 +- 0.259
236,mlp,_scale = True; activation = identity; alpha = 0.00630579760633; batch_size = auto; hidden_layer_sizes[0] = 162; hidden_layer_sizes[1] = 289; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.715 +- 0.157
237,mlp,_scale = True; activation = identity; alpha = 0.00612355292055; batch_size = auto; hidden_layer_sizes[0] = 189; hidden_layer_sizes[1] = 10; learning_rate = invscaling; learning_rate_init = 0.316571358391; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.714 +- 0.404
238,knn,_scale = True; algorithm = ball_tree; leaf_size = 10; metric = minkowski; n_neighbors = 10; p = 1; weights = distance,f1 and cv,0.673 +- 0.232
239,mlp,_scale = True; activation = logistic; alpha = 0.00242931276589; batch_size = auto; hidden_layer_sizes[0] = 195; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.707 +- 0.291
240,mlp,_scale = True; activation = tanh; alpha = 0.00266562382463; batch_size = auto; hidden_layer_sizes[0] = 221; learning_rate = constant; learning_rate_init = 0.0228795034676; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.776 +- 0.187
241,mlp,_scale = True; activation = relu; alpha = 0.00205598323924; batch_size = auto; hidden_layer_sizes[0] = 55; hidden_layer_sizes[1] = 20; learning_rate = invscaling; learning_rate_init = 0.925931788017; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.734 +- 0.255
242,mlp,_scale = True; activation = relu; alpha = 0.000649033900464; batch_size = auto; beta_1 = 0.876033262692; beta_2 = 0.895049586659; hidden_layer_sizes[0] = 27; learning_rate_init = 0.143105264242; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.780 +- 0.375
243,knn,_scale = True; algorithm = ball_tree; leaf_size = 38; metric = manhattan; n_neighbors = 9; weights = distance,f1 and cv,0.751 +- 0.171
244,mlp,_scale = True; activation = tanh; alpha = 0.0013265392979; batch_size = auto; beta_1 = 0.943844591381; beta_2 = 0.947204916232; hidden_layer_sizes[0] = 62; learning_rate_init = 0.437697852476; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.718 +- 0.176
245,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 4; weights = uniform,f1 and cv,0.645 +- 0.318
246,mlp,_scale = True; activation = tanh; alpha = 0.00614748743749; batch_size = auto; beta_1 = 0.809114945577; beta_2 = 0.990605182173; hidden_layer_sizes[0] = 218; hidden_layer_sizes[1] = 50; hidden_layer_sizes[2] = 214; learning_rate_init = 0.158953806756; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.611 +- 0.631
247,mlp,_scale = True; activation = identity; alpha = 0.00467152728732; batch_size = auto; hidden_layer_sizes[0] = 239; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.693 +- 0.136
248,knn,_scale = True; algorithm = ball_tree; leaf_size = 42; metric = euclidean; n_neighbors = 13; weights = uniform,f1 and cv,0.599 +- 0.405
249,knn,_scale = True; algorithm = ball_tree; leaf_size = 12; metric = chebyshev; n_neighbors = 18; weights = uniform,f1 and cv,0.783 +- 0.234
250,knn,_scale = True; algorithm = brute; metric = euclidean; n_neighbors = 10; weights = distance,f1 and cv,0.791 +- 0.242
251,mlp,_scale = True; activation = tanh; alpha = 0.00818619325238; batch_size = auto; hidden_layer_sizes[0] = 64; hidden_layer_sizes[1] = 247; hidden_layer_sizes[2] = 181; learning_rate = constant; learning_rate_init = 0.937549184535; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.551 +- 0.388
252,rf,criterion = entropy; max_depth = 2; max_features = 0.908161834155; min_samples_leaf = 2; n_estimators = 100; n_jobs = -1,f1 and cv,0.771 +- 0.324
253,dt,criterion = gini; max_depth = 9; max_features = 0.206621728603; min_samples_leaf = 3; min_samples_split = 4,f1 and cv,0.609 +- 0.414
254,mlp,_scale = True; activation = tanh; alpha = 0.00439204791263; batch_size = auto; hidden_layer_sizes[0] = 152; hidden_layer_sizes[1] = 140; hidden_layer_sizes[2] = 137; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.426 +- 0.496
255,mlp,_scale = True; activation = identity; alpha = 0.00499907212969; batch_size = auto; hidden_layer_sizes[0] = 13; hidden_layer_sizes[1] = 225; learning_rate = adaptive; learning_rate_init = 0.95876802125; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.563 +- 0.731
256,mlp,_scale = True; activation = identity; alpha = 0.00834453755326; batch_size = auto; hidden_layer_sizes[0] = 124; hidden_layer_sizes[1] = 116; hidden_layer_sizes[2] = 242; len(hidden_layer_sizes) = 3; solver = lbfgs,f1 and cv,0.770 +- 0.257
257,mlp,_scale = True; activation = relu; alpha = 0.00659089491028; batch_size = auto; hidden_layer_sizes[0] = 78; hidden_layer_sizes[1] = 205; learning_rate = adaptive; learning_rate_init = 0.200996550946; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.725 +- 0.274
258,knn,_scale = True; algorithm = kd_tree; leaf_size = 29; metric = euclidean; n_neighbors = 16; weights = uniform,f1 and cv,0.757 +- 0.379
259,mlp,_scale = True; activation = identity; alpha = 0.000532662580324; batch_size = auto; beta_1 = 0.883000256325; beta_2 = 0.985679904944; hidden_layer_sizes[0] = 264; hidden_layer_sizes[1] = 299; learning_rate_init = 0.788356525758; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.709 +- 0.299
260,mlp,_scale = True; activation = tanh; alpha = 0.00181218575585; batch_size = auto; hidden_layer_sizes[0] = 262; hidden_layer_sizes[1] = 37; learning_rate = adaptive; learning_rate_init = 0.513613365916; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.650 +- 0.452
261,mlp,_scale = True; activation = identity; alpha = 0.00425512975095; batch_size = auto; hidden_layer_sizes[0] = 7; learning_rate = adaptive; learning_rate_init = 0.0618833847181; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.752 +- 0.272
262,knn,_scale = True; algorithm = kd_tree; leaf_size = 36; metric = minkowski; n_neighbors = 6; p = 2; weights = uniform,f1 and cv,0.733 +- 0.323
263,knn,_scale = True; algorithm = brute; metric = chebyshev; n_neighbors = 4; weights = distance,f1 and cv,0.710 +- 0.186
264,mlp,_scale = True; activation = logistic; alpha = 0.00354360494334; batch_size = auto; beta_1 = 0.82693224625; beta_2 = 0.817798909972; hidden_layer_sizes[0] = 20; learning_rate_init = 0.452164272742; len(hidden_layer_sizes) = 1; solver = adam,f1 and cv,0.816 +- 0.496
265,knn,_scale = True; algorithm = kd_tree; leaf_size = 48; metric = minkowski; n_neighbors = 10; p = 2; weights = distance,f1 and cv,0.626 +- 0.642
266,mlp,_scale = True; activation = identity; alpha = 0.00543802988332; batch_size = auto; hidden_layer_sizes[0] = 12; learning_rate = constant; learning_rate_init = 0.877546109; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.709 +- 0.405
267,svm,C = 1.62680590415e-05; _scale = True; cache_size = 15000; class_weight = balanced; coef0 = 692; degree = 5; gamma = 0.412453389278; kernel = poly; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.661 +- 0.228
268,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors = 9; weights = uniform,f1 and cv,0.774 +- 0.233
269,mlp,_scale = True; activation = relu; alpha = 0.00101355958991; batch_size = auto; hidden_layer_sizes[0] = 2; hidden_layer_sizes[1] = 167; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.734 +- 0.304
270,mlp,_scale = True; activation = relu; alpha = 0.00262922227938; batch_size = auto; hidden_layer_sizes[0] = 26; hidden_layer_sizes[1] = 40; hidden_layer_sizes[2] = 113; learning_rate = adaptive; learning_rate_init = 0.299887409962; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.777 +- 0.245
271,mlp,_scale = True; activation = tanh; alpha = 0.00271773637471; batch_size = auto; hidden_layer_sizes[0] = 119; len(hidden_layer_sizes) = 1; solver = lbfgs,f1 and cv,0.749 +- 0.345
272,knn,_scale = True; algorithm = brute; metric = minkowski; n_neighbors = 5; p = 1; weights = distance,f1 and cv,0.743 +- 0.445
273,mlp,_scale = True; activation = relu; alpha = 0.000626343626388; batch_size = auto; hidden_layer_sizes[0] = 145; hidden_layer_sizes[1] = 271; learning_rate = constant; learning_rate_init = 0.738766298476; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.771 +- 0.146
274,mlp,_scale = True; activation = tanh; alpha = 0.00385332691371; batch_size = auto; hidden_layer_sizes[0] = 215; learning_rate = adaptive; learning_rate_init = 0.483603069058; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.648 +- 0.076
275,knn,_scale = True; algorithm = ball_tree; leaf_size = 28; metric = manhattan; n_neighbors = 4; weights = uniform,f1 and cv,0.760 +- 0.271
276,knn,_scale = True; algorithm = kd_tree; leaf_size = 20; metric = chebyshev; n_neighbors = 3; weights = distance,f1 and cv,0.655 +- 0.280
277,mlp,_scale = True; activation = logistic; alpha = 0.000848257026879; batch_size = auto; hidden_layer_sizes[0] = 199; learning_rate = adaptive; learning_rate_init = 0.653372020353; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.703 +- 0.104
278,mlp,_scale = True; activation = logistic; alpha = 0.00419892412904; batch_size = auto; hidden_layer_sizes[0] = 132; learning_rate = invscaling; learning_rate_init = 0.675419025257; len(hidden_layer_sizes) = 1; solver = sgd,f1 and cv,0.808 +- 0.160
279,mlp,_scale = True; activation = identity; alpha = 0.00338450371429; batch_size = auto; hidden_layer_sizes[0] = 61; hidden_layer_sizes[1] = 198; learning_rate = constant; learning_rate_init = 0.633056212575; len(hidden_layer_sizes) = 2; solver = sgd,f1 and cv,0.688 +- 0.708
280,knn,_scale = True; algorithm = ball_tree; leaf_size = 35; metric = minkowski; n_neighbors = 7; p = 3; weights = uniform,f1 and cv,0.712 +- 0.205
281,mlp,_scale = True; activation = identity; alpha = 0.000989861481974; batch_size = auto; beta_1 = 0.826792157264; beta_2 = 0.929603696055; hidden_layer_sizes[0] = 258; hidden_layer_sizes[1] = 233; hidden_layer_sizes[2] = 101; learning_rate_init = 0.36232540548; len(hidden_layer_sizes) = 3; solver = adam,f1 and cv,0.689 +- 0.493
282,mlp,_scale = True; activation = tanh; alpha = 0.00620264986437; batch_size = auto; beta_1 = 0.851090764705; beta_2 = 0.895041241823; hidden_layer_sizes[0] = 180; hidden_layer_sizes[1] = 163; learning_rate_init = 0.480899000813; len(hidden_layer_sizes) = 2; solver = adam,f1 and cv,0.798 +- 0.278
283,knn,_scale = True; algorithm = kd_tree; leaf_size = 30; metric = manhattan; n_neighbors = 20; weights = uniform,f1 and cv,0.868 +- 0.069
284,dt,criterion = entropy; max_depth = 5; max_features = 0.849892428315; min_samples_leaf = 1; min_samples_split = 3,f1 and cv,0.699 +- 0.329
285,mlp,_scale = True; activation = logistic; alpha = 0.00292615838825; batch_size = auto; hidden_layer_sizes[0] = 111; hidden_layer_sizes[1] = 133; len(hidden_layer_sizes) = 2; solver = lbfgs,f1 and cv,0.741 +- 0.297
286,mlp,_scale = True; activation = tanh; alpha = 0.00192212524063; batch_size = auto; hidden_layer_sizes[0] = 175; hidden_layer_sizes[1] = 173; hidden_layer_sizes[2] = 62; learning_rate = adaptive; learning_rate_init = 0.928033125073; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.591 +- 0.614
287,knn,_scale = True; algorithm = ball_tree; leaf_size = 16; metric = chebyshev; n_neighbors = 15; weights = distance,f1 and cv,0.680 +- 0.196
288,knn,_scale = True; algorithm = kd_tree; leaf_size = 4; metric = euclidean; n_neighbors = 19; weights = distance,f1 and cv,0.643 +- 0.651
289,svm,C = 106.323374676; _scale = True; cache_size = 15000; class_weight = balanced; kernel = linear; max_iter = 50000; probability = True; shrinking = True,f1 and cv,0.821 +- 0.158
290,gp,alpha = 0.340932352507; kernel = rational_quadratic; length_scale = 1.23158862963,f1 and cv,0.623 +- 0.402
291,mlp,_scale = True; activation = logistic; alpha = 0.000565210040334; batch_size = auto; hidden_layer_sizes[0] = 201; hidden_layer_sizes[1] = 125; hidden_layer_sizes[2] = 51; learning_rate = invscaling; learning_rate_init = 0.233137263443; len(hidden_layer_sizes) = 3; solver = sgd,f1 and cv,0.267 +- 0.653
292,mlp,_scale = True; activation = logistic; alpha = 0.00017320409652; ba
gitextract_l1u8z3d0/ ├── .dockerignore ├── .editorconfig ├── .github/ │ └── ISSUE_TEMPLATE.md ├── .gitignore ├── .gitmodules ├── Dockerfile ├── README.md ├── Vagrantfile ├── cleanup.sh ├── docker-compose.yml ├── install-docker.sh ├── install.sh ├── kill.sh ├── log.txt ├── package.json ├── public/ │ ├── index.html │ ├── manifest.json │ ├── morley.csv │ └── viz/ │ ├── blood.csv │ ├── datarun1_gp.csv │ ├── datarun1_grid.csv │ ├── datarun2_gp.csv │ ├── datarun3_gp.csv │ ├── dataset_31_credit-g.csv │ ├── pollution_1.csv │ └── pollution_1_test.csv ├── scripts/ │ ├── build_with_docker.sh │ └── start_docker.sh ├── server/ │ ├── .gitignore │ ├── atm_server/ │ │ ├── __init__.py │ │ ├── api.py │ │ ├── atm_helper/ │ │ │ ├── __init__.py │ │ │ ├── btb_wireup.py │ │ │ ├── datarun_config.py │ │ │ ├── helpers.py │ │ │ └── worker.py │ │ ├── atmvis.py │ │ ├── cache.py │ │ ├── cli.py │ │ ├── config.py │ │ ├── db.py │ │ ├── error.py │ │ ├── recommender/ │ │ │ ├── OneHotEncoder.py │ │ │ ├── __init__.py │ │ │ ├── encoder.py │ │ │ ├── logging_.py │ │ │ ├── metafeature.py │ │ │ ├── metafeatures.py │ │ │ └── predict_dataset.py │ │ ├── server.py │ │ └── utils.py │ ├── config/ │ │ ├── run.1.yaml │ │ ├── run.yaml │ │ └── sql.yaml │ ├── recommender_dataset/ │ │ ├── data_meta_first.csv │ │ └── dataset_method_PMA.csv │ ├── requirements.txt │ └── setup.py ├── src/ │ ├── Const.tsx │ ├── assets/ │ │ ├── methodsDef.1.json │ │ └── methodsDef.json │ ├── components/ │ │ ├── App.css │ │ ├── App.test.tsx │ │ ├── App.tsx │ │ ├── DataRuns/ │ │ │ ├── AskModal.tsx │ │ │ ├── BarChart.tsx │ │ │ ├── DataRuns.tsx │ │ │ ├── Histogram.tsx │ │ │ ├── HyperPartitions.tsx │ │ │ ├── Methods.css │ │ │ ├── Methods.tsx │ │ │ ├── MethodsDotBarChart.css │ │ │ ├── MethodsDotBarChart.tsx │ │ │ ├── MethodsLineChart.css │ │ │ ├── MethodsLineChart.tsx │ │ │ ├── MethodsLineChart_backup.css │ │ │ ├── MethodsLineChart_backup.tsx │ │ │ ├── MethodsSearchSpace.css │ │ │ ├── MethodsSearchSpace.tsx │ │ │ ├── ThreeLevel/ │ │ │ │ ├── HyperParameters.tsx │ │ │ │ ├── HyperPartitions.css │ │ │ │ ├── HyperPartitions.tsx │ │ │ │ ├── Methods.css │ │ │ │ ├── Methods.tsx │ │ │ │ ├── ThreeLevel.css │ │ │ │ ├── ThreeLevel.tsx │ │ │ │ └── index.tsx │ │ │ ├── ThreeLevel_Horizontal/ │ │ │ │ ├── HyperParameters.tsx │ │ │ │ ├── HyperPartitions.css │ │ │ │ ├── HyperPartitions.tsx │ │ │ │ ├── Methods.css │ │ │ │ ├── Methods.tsx │ │ │ │ ├── ThreeLevel.tsx │ │ │ │ └── index.tsx │ │ │ └── index.tsx │ │ ├── DataView.css │ │ ├── DataView.tsx │ │ ├── SidePanel/ │ │ │ ├── DataSelector.css │ │ │ ├── DataSelector.tsx │ │ │ ├── DataView.css │ │ │ ├── DataView.tsx │ │ │ ├── FeatureChart.tsx │ │ │ ├── LeaderBoard.css │ │ │ ├── LeaderBoard.tsx │ │ │ ├── LineChart.tsx │ │ │ ├── OverallHistogram.tsx │ │ │ ├── SettingsModal.tsx │ │ │ ├── SidePanel.tsx │ │ │ ├── index.css │ │ │ └── index.tsx │ │ ├── UploadModal.tsx │ │ └── Utils/ │ │ └── GenerateSvg.tsx │ ├── helper/ │ │ └── index.tsx │ ├── images.d.ts │ ├── index.css │ ├── index.tsx │ ├── registerServiceWorker.ts │ ├── service/ │ │ └── dataService.ts │ └── types/ │ └── index.ts ├── start.sh ├── startserver.sh ├── tsconfig.json ├── tsconfig.prod.json ├── tsconfig.test.json ├── tslint.json ├── vagrantProvision.sh └── vagrantStart.sh
SYMBOL INDEX (693 symbols across 54 files)
FILE: server/atm_server/api.py
function allowed_file (line 30) | def allowed_file(filename):
function handle_invalid_usage (line 40) | def handle_invalid_usage(error):
function handle_db_request_error (line 49) | def handle_db_request_error(error):
function fetch_entity_as_json (line 62) | def fetch_entity_as_json(*args, **kwargs):
function get_datasets (line 75) | def get_datasets():
function get_dataset (line 81) | def get_dataset(dataset_id):
function get_dataset_file (line 87) | def get_dataset_file(dataset_id):
function get_dataruns (line 104) | def get_dataruns():
function get_datarun (line 114) | def get_datarun(datarun_id):
function get_datarun_summary (line 120) | def get_datarun_summary(datarun_id):
function get_hyperpartitions (line 128) | def get_hyperpartitions():
function get_hyperpartition (line 140) | def get_hyperpartition(hyperpartition_id):
function get_classifiers (line 147) | def get_classifiers():
function get_classifier (line 165) | def get_classifier(classifier_id):
function get_classifier_summary (line 173) | def get_classifier_summary():
function get_datarun_steps_scores (line 192) | def get_datarun_steps_scores(datarun_id):
function post_enter_data (line 227) | def post_enter_data():
function post_new_dataset (line 279) | def post_new_dataset():
function post_new_datarun (line 327) | def post_new_datarun(dataset_id):
function dispatch_simple_worker (line 354) | def dispatch_simple_worker():
function dispatch_single_worker (line 376) | def dispatch_single_worker(datarun_id):
function stop_single_worker (line 389) | def stop_single_worker(datarun_id):
function configs_info (line 401) | def configs_info():
function update_hyperparameters (line 438) | def update_hyperparameters(datarun_id):
function post_disable_hyperpartition (line 464) | def post_disable_hyperpartition():
function post_enable_hyperpartition (line 477) | def post_enable_hyperpartition():
function post_update_datarun_config (line 491) | def post_update_datarun_config(datarun_id):
function post_click_event (line 551) | def post_click_event():
function getRecommendation (line 579) | def getRecommendation(dataset_id):
FILE: server/atm_server/atm_helper/btb_wireup.py
function _selector_scores2rewards (line 11) | def _selector_scores2rewards(selector, choice_scores):
function ucb_bandit_scores (line 37) | def ucb_bandit_scores(choice_rewards):
function selector_bandit_scores (line 57) | def selector_bandit_scores(selector, choice_scores):
FILE: server/atm_server/atm_helper/datarun_config.py
class NewMethod (line 24) | class NewMethod(Method):
method __init__ (line 26) | def __init__(self, method, method_path):
function get_datarun_config_path (line 80) | def get_datarun_config_path(datarun_id, method=None):
function maybe_create_datarun_configs (line 90) | def maybe_create_datarun_configs(datarun_id):
function load_datarun_config_dict (line 99) | def load_datarun_config_dict(datarun_id=None):
function load_datarun_config (line 116) | def load_datarun_config(datarun_id=None):
function update_datarun_config (line 134) | def update_datarun_config(datarun_id, config):
function load_datarun_method_config (line 204) | def load_datarun_method_config(datarun_id, method=None):
function save_datarun_method_config (line 223) | def save_datarun_method_config(datarun_id, method, config):
function update_datarun_method_config (line 230) | def update_datarun_method_config(datarun_id, method, hyperparameter_conf...
class datarun_config (line 276) | class datarun_config:
method __init__ (line 277) | def __init__(self, datarun_id):
method __enter__ (line 284) | def __enter__(self):
method run_config (line 293) | def run_config(self):
method update_run_config (line 299) | def update_run_config(self, config):
method load_method_config (line 304) | def load_method_config(self, method=None):
method update_method_config (line 307) | def update_method_config(self, method, hyperparameters):
method __exit__ (line 310) | def __exit__(self, type, error, traceback):
FILE: server/atm_server/atm_helper/helpers.py
function get_datarun_steps_info (line 18) | def get_datarun_steps_info(datarun_id, classifier_start=None, classifier...
function new_datarun (line 88) | def new_datarun(db, run_config, run_per_partition=False):
FILE: server/atm_server/atm_helper/worker.py
function return_stdout_stderr (line 29) | def return_stdout_stderr(f):
function work (line 58) | def work(datarun_id, args=None):
function dispatch_worker (line 119) | def dispatch_worker(datarun_id):
function monitor_dispatch_worker (line 137) | def monitor_dispatch_worker(datarun_id):
function start_worker (line 185) | def start_worker(datarun_id):
function stop_worker (line 216) | def stop_worker(datarun_id):
function mark_running_datarun_pending (line 261) | def mark_running_datarun_pending(db, datarun_id):
function datarun_id2key (line 272) | def datarun_id2key(datarun_id):
function register_worker_process (line 276) | def register_worker_process(process, datarun_id):
function should_worker_stop (line 286) | def should_worker_stop(datarun_id):
function signal_worker_stop (line 294) | def signal_worker_stop(datarun_id):
function clean_worker_cache (line 310) | def clean_worker_cache(datarun_id):
FILE: server/atm_server/atmvis.py
function send_js (line 7) | def send_js(path):
function send_css (line 12) | def send_css(path):
function send_media (line 17) | def send_media(path):
function index (line 22) | def index():
function send_index (line 27) | def send_index(model):
FILE: server/atm_server/cache.py
class Cache (line 11) | class Cache:
method __init__ (line 12) | def __init__(self):
method set (line 15) | def set(self, key, value):
method get (line 18) | def get(self, key):
method has (line 21) | def has(self, key):
method delete (line 24) | def delete(self, *keys):
function get_cache (line 42) | def get_cache():
FILE: server/atm_server/cli.py
function cli (line 9) | def cli():
FILE: server/atm_server/config.py
class Config (line 5) | class Config(object):
class ProductionConfig (line 16) | class ProductionConfig(Config):
class DevelopmentConfig (line 20) | class DevelopmentConfig(Config):
class TestingConfig (line 24) | class TestingConfig(Config):
FILE: server/atm_server/db.py
function check_db_mappers (line 12) | def check_db_mappers(db):
function get_db (line 33) | def get_db():
function teardown_db (line 49) | def teardown_db(e=None):
function init_app (line 57) | def init_app(app):
function object_as_dict (line 63) | def object_as_dict(obj):
function fetch_entity (line 67) | def fetch_entity(entity_name, filters=None, one=False):
function table_fetcher (line 99) | def table_fetcher(table):
function metric_string (line 111) | def metric_string(model, target):
function params_string (line 120) | def params_string(params):
function hyperpartition_string (line 124) | def hyperpartition_string(hp):
function summarize_classifiers (line 136) | def summarize_classifiers(dataset_id=None, datarun_id=None, hyperpartiti...
function fetch_dataset_path (line 185) | def fetch_dataset_path(dataset_id, train=True):
function summarize_datarun (line 196) | def summarize_datarun(datarun_id, classifier_start=None, classifier_end=...
function fetch_classifiers (line 233) | def fetch_classifiers(classifier_id=None, dataset_id=None, datarun_id=No...
function fetch_hyperpartitions (line 265) | def fetch_hyperpartitions(hyperpartition_id=None, dataset_id=None, datar...
FILE: server/atm_server/error.py
class ApiError (line 1) | class ApiError(Exception):
method __init__ (line 8) | def __init__(self, message, status_code=None, payload=None):
method to_dict (line 15) | def to_dict(self):
FILE: server/atm_server/recommender/OneHotEncoder.py
function _transform_selected (line 7) | def _transform_selected(X, transform, selected="all", copy=True):
class OneHotEncoder (line 61) | class OneHotEncoder(BaseEstimator, TransformerMixin):
method __init__ (line 133) | def __init__(self, categorical_features="all", dtype=np.float,
method fit (line 140) | def fit(self, X, y=None):
method _fit_transform (line 155) | def _fit_transform(self, X):
method fit_transform (line 248) | def fit_transform(self, X, y=None):
method _transform (line 257) | def _transform(self, X):
method transform (line 344) | def transform(self, X):
FILE: server/atm_server/recommender/encoder.py
class MetaData (line 11) | class MetaData(object):
method __init__ (line 12) | def __init__(self, class_column, train_path, test_path=None):
class DataEncoder (line 38) | class DataEncoder(object):
method __init__ (line 39) | def __init__(self, class_column='class', feature_columns=None):
method fit (line 48) | def fit(self, data):
method transform (line 89) | def transform(self, data):
method fit_transform (line 113) | def fit_transform(self, data):
FILE: server/atm_server/recommender/logging_.py
function setup_logger (line 10) | def setup_logger(output_file=None):
function _create_logger (line 19) | def _create_logger(name):
function get_logger (line 25) | def get_logger(name):
class PickableLoggerAdapter (line 30) | class PickableLoggerAdapter(object):
method __init__ (line 32) | def __init__(self, name):
method __getstate__ (line 36) | def __getstate__(self):
method __setstate__ (line 47) | def __setstate__(self, state):
method debug (line 60) | def debug(self, msg, *args, **kwargs):
method info (line 63) | def info(self, msg, *args, **kwargs):
method warning (line 66) | def warning(self, msg, *args, **kwargs):
method error (line 69) | def error(self, msg, *args, **kwargs):
method exception (line 72) | def exception(self, msg, *args, **kwargs):
method critical (line 75) | def critical(self, msg, *args, **kwargs):
method log (line 78) | def log(self, level, msg, *args, **kwargs):
method isEnabledFor (line 81) | def isEnabledFor(self, level):
FILE: server/atm_server/recommender/metafeature.py
class AbstractMetaFeature (line 13) | class AbstractMetaFeature(object):
method __init__ (line 17) | def __init__(self):
method _calculate (line 21) | def _calculate(cls, X, y, categorical):
method __call__ (line 24) | def __call__(self, X, y, categorical=None):
class MetaFeature (line 44) | class MetaFeature(AbstractMetaFeature):
method __init__ (line 45) | def __init__(self):
class HelperFunction (line 50) | class HelperFunction(AbstractMetaFeature):
method __init__ (line 51) | def __init__(self):
class MetaFeatureValue (line 56) | class MetaFeatureValue(object):
method __init__ (line 57) | def __init__(self, name, type_, fold, repeat, value, time, comment=""):
method to_arff_row (line 66) | def to_arff_row(self):
method __repr__ (line 75) | def __repr__(self):
class DatasetMetafeatures (line 83) | class DatasetMetafeatures(object):
method __init__ (line 84) | def __init__(self, dataset_name, metafeature_values):
method _get_arff (line 88) | def _get_arff(self):
method dumps (line 105) | def dumps(self):
method dump (line 108) | def dump(self, path_or_filehandle):
method load (line 118) | def load(cls, path_or_filehandle):
method __repr__ (line 134) | def __repr__(self, verbosity=0):
method keys (line 155) | def keys(self):
method __getitem__ (line 158) | def __getitem__(self, item):
FILE: server/atm_server/recommender/metafeatures.py
class HelperFunctions (line 27) | class HelperFunctions(object):
method __init__ (line 28) | def __init__(self):
method clear (line 32) | def clear(self):
method __iter__ (line 36) | def __iter__(self):
method __getitem__ (line 39) | def __getitem__(self, item):
method __setitem__ (line 42) | def __setitem__(self, key, value):
method __delitem__ (line 45) | def __delitem__(self, key):
method __contains__ (line 48) | def __contains__(self, item):
method is_calculated (line 51) | def is_calculated(self, key):
method get_value (line 58) | def get_value(self, key):
method set_value (line 61) | def set_value(self, key, item):
method define (line 64) | def define(self, name):
class MetafeatureFunctions (line 75) | class MetafeatureFunctions(object):
method __init__ (line 76) | def __init__(self):
method clear (line 81) | def clear(self):
method __iter__ (line 84) | def __iter__(self):
method __getitem__ (line 87) | def __getitem__(self, item):
method __setitem__ (line 90) | def __setitem__(self, key, value):
method __delitem__ (line 93) | def __delitem__(self, key):
method __contains__ (line 96) | def __contains__(self, item):
method get_value (line 99) | def get_value(self, key):
method set_value (line 102) | def set_value(self, key, item):
method is_calculated (line 105) | def is_calculated(self, key):
method get_dependency (line 112) | def get_dependency(self, name):
method define (line 117) | def define(self, name, dependency=None):
class NumberOfInstances (line 135) | class NumberOfInstances(MetaFeature):
method _calculate (line 136) | def _calculate(self, X, y, categorical):
class LogNumberOfInstances (line 141) | class LogNumberOfInstances(MetaFeature):
method _calculate (line 142) | def _calculate(self, X, y, categorical):
class NumberOfClasses (line 146) | class NumberOfClasses(MetaFeature):
method _calculate (line 153) | def _calculate(self, X, y, categorical):
class NumberOfFeatures (line 160) | class NumberOfFeatures(MetaFeature):
method _calculate (line 161) | def _calculate(self, X, y, categorical):
class LogNumberOfFeatures (line 166) | class LogNumberOfFeatures(MetaFeature):
method _calculate (line 167) | def _calculate(self, X, y, categorical):
class MissingValues (line 171) | class MissingValues(HelperFunction):
method _calculate (line 172) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 176) | def _calculate_sparse(self, X, y, categorical):
class NumberOfInstancesWithMissingValues (line 184) | class NumberOfInstancesWithMissingValues(MetaFeature):
method _calculate (line 185) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 190) | def _calculate_sparse(self, X, y, categorical):
class PercentageOfInstancesWithMissingValues (line 201) | class PercentageOfInstancesWithMissingValues(MetaFeature):
method _calculate (line 202) | def _calculate(self, X, y, categorical):
class NumberOfFeaturesWithMissingValues (line 208) | class NumberOfFeaturesWithMissingValues(MetaFeature):
method _calculate (line 209) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 214) | def _calculate_sparse(self, X, y, categorical):
class PercentageOfFeaturesWithMissingValues (line 225) | class PercentageOfFeaturesWithMissingValues(MetaFeature):
method _calculate (line 226) | def _calculate(self, X, y, categorical):
class NumberOfMissingValues (line 231) | class NumberOfMissingValues(MetaFeature):
method _calculate (line 232) | def _calculate(self, X, y, categorical):
class PercentageOfMissingValues (line 237) | class PercentageOfMissingValues(MetaFeature):
method _calculate (line 238) | def _calculate(self, X, y, categorical):
class NumberOfNumericFeatures (line 244) | class NumberOfNumericFeatures(MetaFeature):
method _calculate (line 245) | def _calculate(self, X, y, categorical):
class NumberOfCategoricalFeatures (line 249) | class NumberOfCategoricalFeatures(MetaFeature):
method _calculate (line 250) | def _calculate(self, X, y, categorical):
class RatioNumericalToNominal (line 254) | class RatioNumericalToNominal(MetaFeature):
method _calculate (line 255) | def _calculate(self, X, y, categorical):
class RatioNominalToNumerical (line 265) | class RatioNominalToNumerical(MetaFeature):
method _calculate (line 266) | def _calculate(self, X, y, categorical):
class DatasetRatio (line 278) | class DatasetRatio(MetaFeature):
method _calculate (line 279) | def _calculate(self, X, y, categorical):
class LogDatasetRatio (line 284) | class LogDatasetRatio(MetaFeature):
method _calculate (line 285) | def _calculate(self, X, y, categorical):
class InverseDatasetRatio (line 289) | class InverseDatasetRatio(MetaFeature):
method _calculate (line 290) | def _calculate(self, X, y, categorical):
class LogInverseDatasetRatio (line 296) | class LogInverseDatasetRatio(MetaFeature):
method _calculate (line 297) | def _calculate(self, X, y, categorical):
class ClassOccurences (line 301) | class ClassOccurences(HelperFunction):
method _calculate (line 302) | def _calculate(self, X, y, categorical):
class ClassProbabilityMin (line 315) | class ClassProbabilityMin(MetaFeature):
method _calculate (line 316) | def _calculate(self, X, y, categorical):
class ClassProbabilityMax (line 333) | class ClassProbabilityMax(MetaFeature):
method _calculate (line 334) | def _calculate(self, X, y, categorical):
class ClassProbabilityMean (line 350) | class ClassProbabilityMean(MetaFeature):
method _calculate (line 351) | def _calculate(self, X, y, categorical):
class ClassProbabilitySTD (line 367) | class ClassProbabilitySTD(MetaFeature):
method _calculate (line 368) | def _calculate(self, X, y, categorical):
class NumSymbols (line 391) | class NumSymbols(HelperFunction):
method _calculate (line 392) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 401) | def _calculate_sparse(self, X, y, categorical):
class SymbolsMin (line 412) | class SymbolsMin(MetaFeature):
method _calculate (line 413) | def _calculate(self, X, y, categorical):
class SymbolsMax (line 424) | class SymbolsMax(MetaFeature):
method _calculate (line 425) | def _calculate(self, X, y, categorical):
class SymbolsMean (line 432) | class SymbolsMean(MetaFeature):
method _calculate (line 433) | def _calculate(self, X, y, categorical):
class SymbolsSTD (line 441) | class SymbolsSTD(MetaFeature):
method _calculate (line 442) | def _calculate(self, X, y, categorical):
class SymbolsSum (line 448) | class SymbolsSum(MetaFeature):
method _calculate (line 449) | def _calculate(self, X, y, categorical):
class Kurtosisses (line 460) | class Kurtosisses(HelperFunction):
method _calculate (line 461) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 468) | def _calculate_sparse(self, X, y, categorical):
class KurtosisMin (line 479) | class KurtosisMin(MetaFeature):
method _calculate (line 480) | def _calculate(self, X, y, categorical):
class KurtosisMax (line 486) | class KurtosisMax(MetaFeature):
method _calculate (line 487) | def _calculate(self, X, y, categorical):
class KurtosisMean (line 493) | class KurtosisMean(MetaFeature):
method _calculate (line 494) | def _calculate(self, X, y, categorical):
class KurtosisSTD (line 500) | class KurtosisSTD(MetaFeature):
method _calculate (line 501) | def _calculate(self, X, y, categorical):
class Skewnesses (line 507) | class Skewnesses(HelperFunction):
method _calculate (line 508) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 515) | def _calculate_sparse(self, X, y, categorical):
class SkewnessMin (line 526) | class SkewnessMin(MetaFeature):
method _calculate (line 527) | def _calculate(self, X, y, categorical):
class SkewnessMax (line 533) | class SkewnessMax(MetaFeature):
method _calculate (line 534) | def _calculate(self, X, y, categorical):
class SkewnessMean (line 540) | class SkewnessMean(MetaFeature):
method _calculate (line 541) | def _calculate(self, X, y, categorical):
class SkewnessSTD (line 547) | class SkewnessSTD(MetaFeature):
method _calculate (line 548) | def _calculate(self, X, y, categorical):
class ClassEntropy (line 564) | class ClassEntropy(MetaFeature):
method _calculate (line 565) | def _calculate(self, X, y, categorical):
class LandmarkLDA (line 608) | class LandmarkLDA(MetaFeature):
method _calculate (line 609) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 637) | def _calculate_sparse(self, X, y, categorical):
class LandmarkNaiveBayes (line 642) | class LandmarkNaiveBayes(MetaFeature):
method _calculate (line 643) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 665) | def _calculate_sparse(self, X, y, categorical):
class LandmarkDecisionTree (line 670) | class LandmarkDecisionTree(MetaFeature):
method _calculate (line 671) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 694) | def _calculate_sparse(self, X, y, categorical):
class LandmarkDecisionNodeLearner (line 704) | class LandmarkDecisionNodeLearner(MetaFeature):
method _calculate (line 705) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 728) | def _calculate_sparse(self, X, y, categorical):
class LandmarkRandomNodeLearner (line 732) | class LandmarkRandomNodeLearner(MetaFeature):
method _calculate (line 733) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 752) | def _calculate_sparse(self, X, y, categorical):
class Landmark1NN (line 782) | class Landmark1NN(MetaFeature):
method _calculate (line 783) | def _calculate(self, X, y, categorical):
class PCA (line 814) | class PCA(HelperFunction):
method _calculate (line 815) | def _calculate(self, X, y, categorical):
method _calculate_sparse (line 830) | def _calculate_sparse(self, X, y, categorical):
class PCAFractionOfComponentsFor95PercentVariance (line 852) | class PCAFractionOfComponentsFor95PercentVariance(MetaFeature):
method _calculate (line 853) | def _calculate(self, X, y, categorical):
class PCAKurtosisFirstPC (line 866) | class PCAKurtosisFirstPC(MetaFeature):
method _calculate (line 867) | def _calculate(self, X, y, categorical):
class PCASkewnessFirstPC (line 881) | class PCASkewnessFirstPC(MetaFeature):
method _calculate (line 882) | def _calculate(self, X, y, categorical):
function calculate_all_metafeatures_encoded_labels (line 895) | def calculate_all_metafeatures_encoded_labels(X, y, categorical, dataset...
function calculate_all_metafeatures_with_labels (line 908) | def calculate_all_metafeatures_with_labels(X, y, categorical, dataset_name,
function calculate_all_metafeatures (line 920) | def calculate_all_metafeatures(X, y, categorical, dataset_name,
FILE: server/atm_server/recommender/predict_dataset.py
class Recommender (line 7) | class Recommender:
method __init__ (line 9) | def __init__(self,saved_meta_path):
method getSavedDataMetaPath (line 28) | def getSavedDataMetaPath(self,dataset_name):
method getSavedResultPath (line 31) | def getSavedResultPath(self,dataset_name):
method calculate_dataset (line 34) | def calculate_dataset(self,path_to_dataset,dataset_name):
method calculate_l1_dataset (line 67) | def calculate_l1_dataset(self,dataset_name):
method predict_dataset (line 126) | def predict_dataset(self,predict_dataset_path,datasetID):
FILE: server/atm_server/server.py
function create_app (line 22) | def create_app(config=None):
function add_arguments_server (line 63) | def add_arguments_server(parser):
function start_server (line 74) | def start_server():
FILE: server/atm_server/utils.py
function nice_json_encoder (line 14) | def nice_json_encoder(base_encoder):
FILE: src/Const.tsx
constant URL (line 1) | const URL = process.env.NODE_ENV === 'development'
constant UPDATE_INTERVAL_MS (line 5) | const UPDATE_INTERVAL_MS = 5000;
constant USER_STUDY (line 6) | const USER_STUDY = false;
constant THRESHOLD_STEP (line 10) | const THRESHOLD_STEP : number = 20;
FILE: src/components/App.tsx
type IState (line 16) | interface IState {
class App (line 24) | class App extends React.Component<{}, IState> {
method constructor (line 28) | constructor(props: {}) {
method setDatarunID (line 45) | public setDatarunID(id: number): void {
method setDatasetID (line 49) | public setDatasetID(datasetID: number): void {
method setDatarunStatus (line 52) | public setDatarunStatus(datarunStatus: IDatarunStatusTypes): void {
method updateDatarunStatus (line 56) | public updateDatarunStatus() {
method setTopK (line 64) | public setTopK(topK:number){
method startOrStopUpdateCycle (line 81) | public startOrStopUpdateCycle(datarunStatus: IDatarunStatusTypes) {
method componentDidUpdate (line 104) | componentDidUpdate(prevProps: {}, prevState: IState) {
method render (line 112) | public render() {
FILE: src/components/DataRuns/AskModal.tsx
type AskModalProps (line 4) | interface AskModalProps {
type AskModalState (line 9) | interface AskModalState {
class AskModal (line 16) | class AskModal extends React.Component<AskModalProps, AskModalState> {
method constructor (line 17) | constructor(props: AskModalProps) {
method initModal (line 30) | public initModal() {
method render (line 54) | render() {
FILE: src/components/DataRuns/BarChart.tsx
type IProps (line 12) | interface IProps {
type IState (line 16) | interface IState {
class BarChart (line 19) | class BarChart extends React.Component<IProps, IState>{
method constructor (line 20) | constructor(props: IProps) {
method getOption (line 28) | public getOption() {
method changeSort (line 129) | public changeSort() {
method render (line 134) | public render() {
FILE: src/components/DataRuns/DataRuns.tsx
type IProps (line 37) | interface IProps {
type IState (line 46) | interface IState {
type IDatarunSummary (line 54) | interface IDatarunSummary {
class DataRuns (line 60) | class DataRuns extends React.Component<IProps, IState>{
method constructor (line 62) | constructor(props: IProps) {
method getData (line 76) | public async getData() {
method startOrStopUpdateCycle (line 132) | public startOrStopUpdateCycle() {
method componentDidMount (line 140) | public componentDidMount() {
method componentDidUpdate (line 146) | componentDidUpdate(prevProps: IProps) {
method componentWillUnmount (line 157) | public componentWillUnmount() {
method render (line 188) | public render() {
FILE: src/components/DataRuns/Histogram.tsx
type IProps (line 6) | interface IProps{
type IState (line 10) | interface IState{
class Histogram (line 15) | class Histogram extends React.Component<IProps, IState>{
method constructor (line 16) | constructor(props:IProps){
method getOption (line 23) | public getOption(){
method render (line 89) | public render(){
FILE: src/components/DataRuns/HyperPartitions.tsx
type IProps (line 6) | interface IProps{
type IState (line 10) | interface IState{
method render (line 15) | render(){
class GridHistogram (line 28) | class GridHistogram extends React.Component<IProps, IState>{
method constructor (line 29) | constructor(props:IProps){
method getOption (line 35) | public getOption(){
method render (line 114) | public render(){
FILE: src/components/DataRuns/Methods.tsx
type IState (line 9) | interface IState {
type IProps (line 11) | interface IProps {
class Methods (line 15) | class Methods extends React.Component<IProps, IState>{
method render (line 17) | public render() {
class Method (line 40) | class Method extends React.Component<{ methodDef: IMethod, classifiers: ...
method getOption (line 41) | getOption() {
method render (line 287) | render() {
FILE: src/components/DataRuns/MethodsDotBarChart.tsx
type IState (line 11) | interface IState {
type IProps (line 13) | interface IProps {
type ChartProps (line 17) | interface ChartProps {
class MethodsDotBarChart (line 27) | class MethodsDotBarChart extends React.Component<IProps, IState>{
method render (line 28) | public render() {
class SingleBoxPlot (line 55) | class SingleBoxPlot {
method setdomain (line 78) | public setdomain(x:any) {
method renderBoxPlot (line 82) | renderBoxPlot(data:number[],obj:any){
class BoxPlot (line 194) | class BoxPlot extends React.Component<ChartProps, {}>{
method componentDidMount (line 196) | componentDidMount() {
method renderBoxPlot (line 200) | renderBoxPlot(){
method render (line 264) | render() {
class DotBarChart (line 271) | class DotBarChart extends React.Component<ChartProps, {}>{
method componentDidMount (line 273) | componentDidMount() {
method renderD3 (line 276) | renderD3() {
method render (line 364) | render() {
FILE: src/components/DataRuns/MethodsLineChart.tsx
type IState (line 17) | interface IState {
type IProps (line 30) | interface IProps {
type ChartProps (line 43) | interface ChartProps {
type DetailChartProps (line 58) | interface DetailChartProps{
type HyperpartitionHeatmapProps (line 75) | interface HyperpartitionHeatmapProps{
class MethodsLineChart (line 91) | class MethodsLineChart extends React.Component<IProps, IState>{
method constructor (line 92) | constructor(props:IProps){
method getbestperformance (line 151) | public getbestperformance(list:IClassifier[]){
method getmaxnum (line 166) | public getmaxnum(classifiers:IClassifier[]){
method componentDidMount (line 190) | componentDidMount(){
method componentWillReceiveProps (line 219) | componentWillReceiveProps(nextProps : IProps) {
method render (line 403) | public render() {
class LineChart (line 1004) | class LineChart extends React.Component<ChartProps, {}>{
method componentDidMount (line 1006) | componentDidMount() {
method renderD3 (line 1009) | renderD3() {
method render (line 1144) | render() {
class HyperpartitionHeatmap (line 1151) | class HyperpartitionHeatmap extends React.Component<HyperpartitionHeatma...
method componentDidMount (line 1153) | componentDidMount() {
method renderD3 (line 1156) | renderD3() {
method render (line 1254) | render() {
class DotBarChart (line 1261) | class DotBarChart extends React.Component<DetailChartProps, {}>{
method componentDidMount (line 1263) | componentDidMount() {
method renderD3 (line 1266) | renderD3() {
method render (line 1399) | render() {
FILE: src/components/DataRuns/MethodsLineChart_backup.tsx
type IState (line 17) | interface IState {
type IProps (line 25) | interface IProps {
type ChartProps (line 31) | interface ChartProps {
type DetailChartProps (line 46) | interface DetailChartProps{
type HyperpartitionHeatmapProps (line 58) | interface HyperpartitionHeatmapProps{
class MethodsLineChart (line 73) | class MethodsLineChart extends React.Component<IProps, IState>{
method constructor (line 74) | constructor(props:IProps){
method getbestperformance (line 125) | public getbestperformance(list:IClassifier[]){
method getmaxnum (line 140) | public getmaxnum(classifiers:IClassifier[]){
method componentDidMount (line 164) | componentDidMount(){
method componentWillReceiveProps (line 178) | componentWillReceiveProps(nextProps : IProps) {
method render (line 258) | public render() {
class LineChart (line 714) | class LineChart extends React.Component<ChartProps, {}>{
method componentDidMount (line 716) | componentDidMount() {
method renderD3 (line 719) | renderD3() {
method render (line 854) | render() {
class HyperpartitionHeatmap (line 861) | class HyperpartitionHeatmap extends React.Component<HyperpartitionHeatma...
method componentDidMount (line 863) | componentDidMount() {
method renderD3 (line 866) | renderD3() {
method render (line 964) | render() {
class DotBarChart (line 971) | class DotBarChart extends React.Component<DetailChartProps, {}>{
method componentDidMount (line 973) | componentDidMount() {
method renderD3 (line 976) | renderD3() {
method render (line 1139) | render() {
FILE: src/components/DataRuns/MethodsSearchSpace.tsx
type IState (line 9) | interface IState {
type IProps (line 11) | interface IProps {
class MethodsSearchSpace (line 15) | class MethodsSearchSpace extends React.Component<IProps, IState>{
method render (line 34) | public render() {
class HyperpatitionSearchSpace (line 222) | class HyperpatitionSearchSpace extends React.Component<{ methodDef: IMet...
method getOption (line 225) | getOption() {
method render (line 451) | render() {
class HyperpatitionBarChart (line 460) | class HyperpatitionBarChart extends React.Component<{ methodDef: IMethod...
method getBarOption (line 463) | getBarOption() {
method render (line 519) | render() {
class HyperpatitionOverViewBarChart (line 527) | class HyperpatitionOverViewBarChart extends React.Component<{ methodDef:...
method getBarOption (line 529) | getBarOption() {
method render (line 647) | render() {
FILE: src/components/DataRuns/ThreeLevel/HyperParameters.tsx
type IProps (line 7) | interface IProps {
type HyperParameterInfo (line 17) | interface HyperParameterInfo{
type IState (line 25) | interface IState {
class HyperParameters (line 38) | class HyperParameters extends React.Component<IProps, IState>{
method componentDidMount (line 83) | componentDidMount(){
method prepareData (line 86) | prepareData(nextProps:IProps,nextStates:IState){
method shouldComponentUpdate (line 219) | shouldComponentUpdate(nextProps:IProps,nextStates:IState){
method render (line 226) | render() {
type HyProps (line 287) | interface HyProps {
class HyperParameter (line 325) | class HyperParameter extends React.Component<HyProps, {}>{
method componentDidMount (line 327) | componentDidMount() {
method componentDidUpdate (line 350) | componentDidUpdate(){
method renderD3 (line 374) | renderD3() {
method render (line 525) | render() {
FILE: src/components/DataRuns/ThreeLevel/HyperPartitions.tsx
type IProps (line 10) | interface IProps {
type IState (line 25) | interface IState {
class HyperPartitions (line 31) | class HyperPartitions extends React.Component<IProps, IState>{
method renderD3 (line 64) | public renderD3(hpsInfo: Array<any>, maxLen: number, selectedMethod: s...
method sortHpByperformance (line 470) | public sortHpByperformance(props: IProps) {
method componentDidMount (line 496) | componentDidMount() {
method shouldComponentUpdate (line 528) | shouldComponentUpdate(nextProps: IProps, nextStates: IState) {
method render (line 574) | render() {
FILE: src/components/DataRuns/ThreeLevel/Methods.tsx
type IProps (line 11) | interface IProps {
type IState (line 28) | interface IState {
class methods (line 33) | class methods extends React.Component<IProps, IState>{
method getbestperformance (line 49) | public getbestperformance(list: IClassifier[]) {
method getmaxnum (line 64) | public getmaxnum(classifiers: IClassifierInfo[]) {
method componentDidUpdate (line 88) | componentDidUpdate(){
method render (line 108) | render() {
type LineChartProps (line 395) | interface LineChartProps {
class LineChart (line 412) | class LineChart extends React.Component<LineChartProps, {}>{
method componentDidMount (line 414) | componentDidMount() {
method componentDidUpdate (line 417) | componentDidUpdate(){
method renderD3 (line 423) | renderD3() {
method render (line 669) | render() {
FILE: src/components/DataRuns/ThreeLevel/ThreeLevel.tsx
type IProps (line 17) | interface IProps {
type IState (line 30) | interface IState {
class ThreeLevel (line 44) | class ThreeLevel extends React.Component<IProps, IState>{
method constructor (line 46) | constructor(props: IProps) {
method onSelectMethod (line 78) | onSelectMethod(methodName: string) {
method componentDidMount (line 96) | componentDidMount() {
method componentWillReceiveProps (line 363) | componentWillReceiveProps(nextProps: IProps) {
method setMethodHeight (line 509) | setMethodHeight() {
method setHyperPartitionsHeight (line 529) | setHyperPartitionsHeight() {
method generateTag (line 546) | generateTag(box: any, name: string) {
method generateRect (line 575) | generateRect(box: any, mode: number, eventCallback: () => void) {
method generateHyperpartitionText (line 607) | generateHyperpartitionText() {
method getClassifiers (line 656) | getClassifiers() {
method generateHyperpartition (line 664) | generateHyperpartition() {
method generateHyperparameterText (line 733) | generateHyperparameterText() {
method generateHyperparameter (line 783) | generateHyperparameter() {
method render (line 855) | render() {
FILE: src/components/DataRuns/ThreeLevel_Horizontal/HyperParameters.tsx
type IProps (line 7) | interface IProps {
type HyperParameterInfo (line 16) | interface HyperParameterInfo{
type IState (line 24) | interface IState {
class HyperParameters (line 37) | class HyperParameters extends React.Component<IProps, IState>{
method componentDidMount (line 82) | componentDidMount(){
method prepareData (line 85) | prepareData(nextProps:IProps,nextStates:IState){
method shouldComponentUpdate (line 217) | shouldComponentUpdate(nextProps:IProps,nextStates:IState){
method render (line 224) | render() {
type HyProps (line 285) | interface HyProps {
class HyperParameter (line 323) | class HyperParameter extends React.Component<HyProps, {}>{
method componentDidMount (line 325) | componentDidMount() {
method componentDidUpdate (line 348) | componentDidUpdate(){
method renderD3 (line 372) | renderD3() {
method render (line 524) | render() {
FILE: src/components/DataRuns/ThreeLevel_Horizontal/HyperPartitions.tsx
type IProps (line 10) | interface IProps {
type IState (line 25) | interface IState {
class HyperPartitions (line 31) | class HyperPartitions extends React.Component<IProps, IState>{
method renderD3 (line 64) | public renderD3(hpsInfo: Array<any>, maxLen: number, selectedMethod: s...
method sortHpByperformance (line 490) | public sortHpByperformance(props: IProps) {
method componentDidMount (line 516) | componentDidMount() {
method shouldComponentUpdate (line 548) | shouldComponentUpdate(nextProps: IProps, nextStates: IState) {
method render (line 594) | render() {
FILE: src/components/DataRuns/ThreeLevel_Horizontal/Methods.tsx
type IProps (line 11) | interface IProps {
type IState (line 26) | interface IState {
class methods (line 31) | class methods extends React.Component<IProps, IState>{
method getbestperformance (line 47) | public getbestperformance(list: IClassifier[]) {
method getmaxnum (line 62) | public getmaxnum(classifiers: IClassifierInfo[]) {
method componentDidUpdate (line 86) | componentDidUpdate(){
method render (line 106) | render() {
type LineChartProps (line 317) | interface LineChartProps {
class LineChart (line 334) | class LineChart extends React.Component<LineChartProps, {}>{
method componentDidMount (line 336) | componentDidMount() {
method componentDidUpdate (line 339) | componentDidUpdate(){
method renderD3 (line 345) | renderD3() {
method render (line 573) | render() {
FILE: src/components/DataRuns/ThreeLevel_Horizontal/ThreeLevel.tsx
type IProps (line 12) | interface IProps {
type IState (line 25) | interface IState {
class ThreeLevel (line 37) | class ThreeLevel extends React.Component<IProps, IState>{
method constructor (line 39) | constructor(props: IProps){
method onSelectMethod (line 56) | onSelectMethod(methodName:string){
method componentDidMount (line 70) | componentDidMount(){
method componentWillReceiveProps (line 332) | componentWillReceiveProps(nextProps : IProps) {
method render (line 478) | render(){
FILE: src/components/DataView.tsx
type IProps (line 16) | interface IProps{
type IState (line 21) | interface IState {
type IFeature (line 26) | interface IFeature {
class DataView (line 34) | class DataView extends React.Component<IProps, IState>{
method constructor (line 36) | constructor(props: IProps) {
method getData (line 47) | public async getData() {
method parseData (line 55) | public parseData(csv:string){
method componentDidUpdate (line 80) | public componentDidUpdate(prevProps: IProps, provState: IState) {
method startDatarun (line 86) | public startDatarun() {
method beforeUpload (line 106) | public beforeUpload(file:any){
method render (line 136) | public render() {
FILE: src/components/SidePanel/DataSelector.tsx
type DataSelectorProps (line 12) | interface DataSelectorProps {
type DataSelectorState (line 21) | interface DataSelectorState {
class DataSelector (line 28) | class DataSelector extends React.Component<DataSelectorProps, DataSelect...
method constructor (line 29) | constructor(props: DataSelectorProps) {
method getDatasets (line 44) | public async getDatasets() {
method getDataruns (line 50) | public async getDataruns(datasetID: number, datarunID:number = -1) {
method componentDidMount (line 63) | public componentDidMount() {
method onSelectDataset (line 67) | public onSelectDataset(datasetID: number) {
method onSelectDatarun (line 71) | public onSelectDatarun(datarunID: number) {
method beforeUploadDataset (line 75) | public beforeUploadDataset(file: any) {
method onClickDatarun (line 92) | public onClickDatarun() {
method newDatarun (line 119) | public async newDatarun(configs: IConfigsInfo): Promise<undefined | IN...
method componentDidUpdate (line 132) | public componentDidUpdate(prevProps: DataSelectorProps, prevState: Dat...
method render (line 170) | public render() {
FILE: src/components/SidePanel/DataView.tsx
type IProps (line 8) | interface IProps{
type IState (line 13) | interface IState {
type IFeature (line 18) | interface IFeature {
class DataView (line 26) | class DataView extends React.Component<IProps, IState>{
method constructor (line 28) | constructor(props: IProps) {
method getData (line 37) | public async getData() {
method parseData (line 45) | public parseData(csv:string){
method componentDidUpdate (line 70) | public componentDidUpdate(prevProps: IProps, provState: IState) {
method componentDidMount (line 75) | public componentDidMount() {
method render (line 79) | public render() {
FILE: src/components/SidePanel/FeatureChart.tsx
class FeatureChart (line 7) | class FeatureChart extends React.Component<{ feature: IFeature, classes:...
method checkFeatureType (line 8) | checkFeatureType(feature_values:IFeature['data']) {
method getValOption (line 19) | getValOption() {
method getCateOption (line 163) | getCateOption() {
method render (line 232) | render() {
FILE: src/components/SidePanel/LeaderBoard.tsx
function isFloat (line 16) | function isFloat(n: number): boolean {
type IDatarunSummary (line 20) | interface IDatarunSummary {
function computeDatarunSummary (line 27) | function computeDatarunSummary(classifiers: IClassifierInfo[]): IDatarun...
function classifierMetricString (line 47) | function classifierMetricString(c: IClassifierInfo): string {
function HyperParams (line 51) | function HyperParams(params: { [method: string]: any }) {
function MethodHeader (line 69) | function MethodHeader(params: IClassifierInfo) {
type LeaderBoardProps (line 82) | interface LeaderBoardProps {
type LeaderBoardState (line 90) | interface LeaderBoardState {
class LeaderBoard (line 99) | class LeaderBoard extends React.Component<LeaderBoardProps, LeaderBoardS...
method constructor (line 101) | constructor(props: LeaderBoardProps) {
method updateLeaderBoard (line 114) | public updateLeaderBoard(updateDatarunInfo: boolean = false) {
method startOrStopUpdateCycle (line 146) | public startOrStopUpdateCycle() {
method changeTopK (line 154) | public changeTopK(value:number){
method componentDidMount (line 159) | componentDidMount() {
method componentDidUpdate (line 163) | componentDidUpdate(prevProps: LeaderBoardProps) {
method componentWillUnmount (line 197) | public componentWillUnmount() {
method render (line 200) | public render() {
FILE: src/components/SidePanel/LineChart.tsx
type LineChartProps (line 6) | interface LineChartProps {
type LineChartState (line 13) | interface LineChartState {}
class LineChart (line 15) | class LineChart extends React.Component<LineChartProps, LineChartState> {
method constructor (line 16) | constructor(props: LineChartProps) {
method computeOption (line 20) | public computeOption() {
method render (line 90) | public render() {
FILE: src/components/SidePanel/OverallHistogram.tsx
type IProps (line 5) | interface IProps{
type IState (line 9) | interface IState{
class OverallHistogram (line 14) | class OverallHistogram extends React.Component<IProps, IState>{
method constructor (line 15) | constructor(props:IProps){
method getOption (line 22) | public getOption(){
method render (line 92) | public render(){
FILE: src/components/SidePanel/SettingsModal.tsx
type SettingsModalProps (line 8) | interface SettingsModalProps {
type SettingsModalState (line 14) | interface SettingsModalState {
class SettingsModal (line 28) | class SettingsModal extends React.Component<SettingsModalProps, Settings...
method constructor (line 29) | constructor(props: SettingsModalProps) {
method initModal (line 57) | public initModal() {
method onCheckAllChange (line 172) | onCheckAllChange(e:any){
method render (line 181) | render() {
FILE: src/components/SidePanel/SidePanel.tsx
type SidePanelProps (line 11) | interface SidePanelProps {
type SidePanelState (line 22) | interface SidePanelState {}
class SidePanel (line 24) | class SidePanel extends React.Component<SidePanelProps, SidePanelState> {
method constructor (line 25) | constructor(props: SidePanelProps) {
method render (line 31) | public render() {
FILE: src/components/UploadModal.tsx
type UploadModalProps (line 4) | interface UploadModalProps {
type UploadModalState (line 8) | interface UploadModalState {
class UploadModal (line 15) | class UploadModal extends React.Component<UploadModalProps, UploadModalS...
method constructor (line 16) | constructor(props: UploadModalProps) {
method initModal (line 29) | public initModal() {
method render (line 56) | render() {
FILE: src/components/Utils/GenerateSvg.tsx
type IProps (line 2) | interface IProps {
type IState (line 5) | interface IState {
class GenerateSvg (line 9) | class GenerateSvg extends React.Component<IProps, IState> {
method constructor (line 10) | constructor(props: IProps) {
method render (line 13) | render() {
FILE: src/helper/index.tsx
constant COLORS (line 4) | const COLORS: string[] = [
constant GREEN (line 21) | const GREEN: string[] = [
constant BLUE (line 26) | const BLUE: string[] = [
constant ORANGE (line 33) | const ORANGE: string[] = [
constant PINK (line 39) | const PINK: string[] = [
constant RED (line 46) | const RED: string = "#DC143C";
constant YELLOW (line 47) | const YELLOW : string = '#fee08b';
function asc (line 248) | function asc(arr:number[]) {
FILE: src/registerServiceWorker.ts
function register (line 22) | function register() {
function registerValidSW (line 59) | function registerValidSW(swUrl: string) {
function checkValidServiceWorker (line 90) | function checkValidServiceWorker(swUrl: string) {
function unregister (line 117) | function unregister() {
FILE: src/service/dataService.ts
type IDatarunStatus (line 16) | interface IDatarunStatus {
type IDatasetInfo (line 20) | interface IDatasetInfo {
type IDatarunInfo (line 28) | interface IDatarunInfo extends IDatarunStatus {
type IHyperpartitionInfo (line 36) | interface IHyperpartitionInfo {
type IHyperPartitionScores (line 47) | interface IHyperPartitionScores {
type IFileUploadResponse (line 51) | interface IFileUploadResponse {
type IClassifierInfo (line 57) | interface IClassifierInfo {
type IConfigsInfo (line 68) | interface IConfigsInfo {
type ICommonResponse (line 81) | interface ICommonResponse {
type IConfigsUploadResponse (line 85) | interface IConfigsUploadResponse extends ICommonResponse {}
type INewDatarunResponse (line 87) | interface INewDatarunResponse {
type IMethodHyperParameters (line 92) | interface IMethodHyperParameters {
function getDatasets (line 96) | async function getDatasets(): Promise<IDatasetInfo[]> {
function getDataset (line 105) | async function getDataset(id: number): Promise<IDatasetInfo> {
function getDataruns (line 114) | async function getDataruns(params?: { dataset_id: number }): Promise<IDa...
function getDatarun (line 123) | async function getDatarun(id: number): Promise<IDatarunInfo> {
function getClassifiers (line 132) | async function getClassifiers(
function getHyperpartitions (line 145) | async function getHyperpartitions(hp_id?: number, datarun_id?:number): P...
function getDatasetCSV (line 162) | async function getDatasetCSV(id: number): Promise<string> {
function getDatarunStepsScores (line 171) | async function getDatarunStepsScores(
function getDatarunSummary (line 185) | async function getDatarunSummary(
function postEnterData (line 199) | async function postEnterData(file: any): Promise<IFileUploadResponse> {
function postNewDataset (line 209) | async function postNewDataset(file: any): Promise<IFileUploadResponse> {
function postNewDatarun (line 219) | async function postNewDatarun(datasetId: number, configs: any): Promise<...
function getClassifierSummary (line 229) | async function getClassifierSummary(datarun_id: number): Promise<string> {
function startDatarun (line 239) | async function startDatarun(datarun_id: number): Promise<IDatarunStatus> {
function stopDatarun (line 248) | async function stopDatarun(datarun_id: number): Promise<IDatarunStatus> {
function getConfigs (line 257) | async function getConfigs(): Promise<IConfigsInfo> {
function postConfigs (line 268) | async function postConfigs(configs : any): Promise<IConfigsUploadRespons...
function postMethodHyperparameters (line 288) | async function postMethodHyperparameters(
function postHyperparameters (line 300) | async function postHyperparameters(
function getMethodHyperparameter (line 311) | async function getMethodHyperparameter(
function getHyperparameters (line 322) | async function getHyperparameters(
function disableHyperpartitions (line 333) | async function disableHyperpartitions(hyperpartitionIds: number[] | numb...
function enableHyperpartitions (line 343) | async function enableHyperpartitions(hyperpartitionIds: number[] | numbe...
type IUpdateDatarunConfig (line 354) | interface IUpdateDatarunConfig {
function updateDatarunConfigs (line 373) | async function updateDatarunConfigs(datarun_id: number, config: IUpdateD...
function getDatarunConfigs (line 381) | async function getDatarunConfigs(datarun_id : number): Promise<IConfigsI...
type IClickEvent (line 401) | interface IClickEvent {
type IClickBundleEvent (line 406) | interface IClickBundleEvent{
function postBundleClickEvent (line 412) | async function postBundleClickEvent(log:IClickBundleEvent):Promise<IComm...
type IRecommendationResult (line 421) | interface IRecommendationResult {
function getRecommendation (line 424) | async function getRecommendation(dataset_id : number): Promise<IRecommen...
FILE: src/types/index.ts
type IMethod (line 1) | interface IMethod{
type IDatarun (line 9) | interface IDatarun{
type IClassifier (line 13) | interface IClassifier{
type IDatarunStatusTypes (line 20) | enum IDatarunStatusTypes {
type IClassifierStatusTypes (line 26) | enum IClassifierStatusTypes {
type IHyperParameterNumeric (line 32) | interface IHyperParameterNumeric {
type IHyperParameterCategorical (line 37) | interface IHyperParameterCategorical {
type IHyperParameterList (line 42) | interface IHyperParameterList {
type IHyperParameter (line 48) | type IHyperParameter = IHyperParameterNumeric | IHyperParameterCategoric...
type IMethodType (line 50) | type IMethodType = 'logreg' | 'svm' | 'sgd' | 'dt' | 'et' | 'rf' | 'gnb'...
Condensed preview — 127 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,581K chars).
[
{
"path": ".dockerignore",
"chars": 147,
"preview": "node_modules/\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n.idea/\n.vscode\n.atm_server_cache\n\natm/\nlogs"
},
{
"path": ".editorconfig",
"chars": 133,
"preview": "[*]\nindent_style = space\nindent_size = 4\ntrim_trailing_whitespace = true\n\n[{.travis.yml,*.json}]\nindent_style = space\nin"
},
{
"path": ".github/ISSUE_TEMPLATE.md",
"chars": 213,
"preview": "## Description\n\n\n## Expected Behavior\n\n\n## Actual Behavior\n\n\n## Steps to Reproduce the Problem\n\n 1.\n 1.\n 1.\n\n## Speci"
},
{
"path": ".gitignore",
"chars": 532,
"preview": "# See https://help.github.com/ignore-files/ for more about ignoring files.\n\n# dependencies\n/node_modules\n\n# data\npublic/"
},
{
"path": ".gitmodules",
"chars": 83,
"preview": "[submodule \"lib/atm\"]\n\tpath = lib/atm\n\turl = http://github.com/HDI-Project/ATM.git\n"
},
{
"path": "Dockerfile",
"chars": 857,
"preview": "FROM ubuntu:16.04\n\n# install utilities\nRUN apt-get update -yqq \\\n && apt-get install -yqq \\\n unzip \\\n curl \\\n git \\\n ss"
},
{
"path": "README.md",
"chars": 5331,
"preview": "<p align=\"left\">\n<img width=15% src=\"https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png\" alt=“DAI-"
},
{
"path": "Vagrantfile",
"chars": 3418,
"preview": "# -*- mode: ruby -*-\n# vi: set ft=ruby :\n\n# All Vagrant configuration is done below. The \"2\" in Vagrant.configure\n# conf"
},
{
"path": "cleanup.sh",
"chars": 39,
"preview": "rm -rf models/* metrics/* logs/* atm.db"
},
{
"path": "docker-compose.yml",
"chars": 269,
"preview": "version: \"3\"\nservices:\n web:\n build: .\n container_name: atmseer\n ports:\n - \"5000:5000\"\n command: sudo "
},
{
"path": "install-docker.sh",
"chars": 159,
"preview": "git submodule init\ngit submodule update\nmkdir logs && chmod 0777 logs\nmkdir metrics && chmod 0777 metrics\nmkdir models &"
},
{
"path": "install.sh",
"chars": 201,
"preview": "git submodule init\ngit submodule update\nmkdir logs && chmod 0777 logs\nmkdir metrics && chmod 0777 metrics\nmkdir models &"
},
{
"path": "kill.sh",
"chars": 28,
"preview": "#!/bin/bash\npkill -9 python\n"
},
{
"path": "log.txt",
"chars": 7352,
"preview": " * Serving Flask app \"server\" (lazy loading)\n * Environment: production\n WARNING: Do not use the development server in"
},
{
"path": "package.json",
"chars": 806,
"preview": "{\n \"name\": \"vis_atm\",\n \"version\": \"0.1.0\",\n \"author\": \"qianwen Wang\",\n \"private\": true,\n \"dependencies\": {\n \"@ty"
},
{
"path": "public/index.html",
"chars": 1588,
"preview": "<!DOCTYPE html>\n<html lang=\"en\">\n <head>\n <meta charset=\"utf-8\">\n <meta name=\"viewport\" content=\"width=device-wid"
},
{
"path": "public/manifest.json",
"chars": 317,
"preview": "{\n \"short_name\": \"React App\",\n \"name\": \"Create React App Sample\",\n \"icons\": [\n {\n \"src\": \"favicon.ico\",\n "
},
{
"path": "public/morley.csv",
"chars": 873,
"preview": "Expt,Run,Speed\n1,1,850\n1,2,740\n1,3,900\n1,4,1070\n1,5,930\n1,6,850\n1,7,950\n1,8,980\n1,9,980\n1,10,880\n1,11,1000\n1,12,980\n1,13"
},
{
"path": "public/viz/blood.csv",
"chars": 11249,
"preview": "V1,V2,V3,V4,class\r\n2,50,12500,98,2\r\n0,13,3250,28,2\r\n1,16,4000,35,2\r\n2,20,5000,45,2\r\n1,24,6000,77,1\r\n4,4,1000,4,1\r\n2,7,17"
},
{
"path": "public/viz/datarun1_gp.csv",
"chars": 42955,
"preview": "trail ID,method,parameters,metrics,performance\r\n0,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors "
},
{
"path": "public/viz/datarun1_grid.csv",
"chars": 14372,
"preview": "trail ID,method,parameters,metrics,performance\r\n0,knn,_scale = True; algorithm = brute; metric = manhattan; n_neighbors "
},
{
"path": "public/viz/datarun2_gp.csv",
"chars": 43573,
"preview": "trail ID,method,parameters,metrics,performance\r\n0,mlp,_scale = True; activation = logistic; alpha = 0.00799991890745; ba"
},
{
"path": "public/viz/datarun3_gp.csv",
"chars": 108185,
"preview": "trail ID,method,parameters,metrics,performance\r\n0,mlp,_scale = True; activation = tanh; alpha = 0.00781487257724; batch_"
},
{
"path": "public/viz/dataset_31_credit-g.csv",
"chars": 151964,
"preview": "checking_status,duration,credit_history,purpose,credit_amount,savings_status,employment,installment_commitment,personal_"
},
{
"path": "public/viz/pollution_1.csv",
"chars": 4852,
"preview": ",PREC,JANT,JULT,OVR65,POPN,EDUC,HOUS,DENS,NONW,WWDRK,POOR,HC,NOX,SO@,HUMID,class\n0,36.0,27.0,71.0,8.1,3.34,11.4,81.5,324"
},
{
"path": "public/viz/pollution_1_test.csv",
"chars": 4503,
"preview": ",PREC,JANT,JULT,OVR65,POPN,EDUC,HOUS,DENS,NONW,WWDRK,POOR,HC,NOX,SO@,HUMID,purpose,class\n0,36,27,71,8.1,3.34,11.4,81.5,3"
},
{
"path": "scripts/build_with_docker.sh",
"chars": 60,
"preview": "#!/usr/bin/env bash\n\nnpm run build\ndocker build . -t atm-vis"
},
{
"path": "scripts/start_docker.sh",
"chars": 298,
"preview": "#!/usr/bin/env bash\n\ndocker run -p 5000:5000 -v ~/workspace/atm/ATM-VIS/atm.db:/code/atm.db \\\n -v ~/workspace/atm/ATM"
},
{
"path": "server/.gitignore",
"chars": 1377,
"preview": "# ignore log files and saved models/metrics\nmodels/\nmetrics/\nlogs/\n.atm_server_cache/\n\n# ignore virtualenv and other bin"
},
{
"path": "server/atm_server/__init__.py",
"chars": 119,
"preview": "import os\n\nSERVER_ROOT = os.path.dirname(os.path.abspath(os.path.join(__file__, '..')))\n\nfrom atm_server.cli import cli"
},
{
"path": "server/atm_server/api.py",
"chars": 21896,
"preview": "import os\nimport copy\nimport logging\nimport yaml\nimport json\n\nfrom multiprocessing import Process\nfrom flask import requ"
},
{
"path": "server/atm_server/atm_helper/__init__.py",
"chars": 75,
"preview": "from .datarun_config import *\nfrom .worker import *\nfrom .helpers import *\n"
},
{
"path": "server/atm_server/atm_helper/btb_wireup.py",
"chars": 2524,
"preview": "\"\"\"\nHelper functions that wire up the atm package\n\"\"\"\n\nfrom btb.selection import UCB1, Uniform, RecentKReward, BestKRewa"
},
{
"path": "server/atm_server/atm_helper/datarun_config.py",
"chars": 11806,
"preview": "import os\nimport shutil\nimport json\nimport yaml\nimport copy\nimport warnings\nfrom flask import current_app\nfrom sqlalchem"
},
{
"path": "server/atm_server/atm_helper/helpers.py",
"chars": 6128,
"preview": "from collections import defaultdict\nimport logging\n\nimport numpy as np\nfrom atm.worker import Worker\nfrom atm.constants "
},
{
"path": "server/atm_server/atm_helper/worker.py",
"chars": 10859,
"preview": "import os\nimport sys\nimport time\nimport atexit\nfrom datetime import datetime\nimport argparse\nimport logging\nfrom io impo"
},
{
"path": "server/atm_server/atmvis.py",
"chars": 1222,
"preview": "from flask import Blueprint, send_from_directory, safe_join, current_app\n\nvis = Blueprint('vis', __name__)\n\n\n@vis.route("
},
{
"path": "server/atm_server/cache.py",
"chars": 1009,
"preview": "\"\"\"\nA Cache Interface\nTODO: Add support for redis\n\"\"\"\n\nimport os\nfrom cachelib import SimpleCache, FileSystemCache\nfrom "
},
{
"path": "server/atm_server/cli.py",
"chars": 269,
"preview": "import click\nfrom flask import Flask\nfrom flask.cli import FlaskGroup\n\nfrom atm_server.server import create_app\n\n\n@click"
},
{
"path": "server/atm_server/config.py",
"chars": 597,
"preview": "import os\nfrom atm_server import SERVER_ROOT\n\n\nclass Config(object):\n UPLOAD_FOLDER = 'atm/data'\n DATARUN_CONFIG_D"
},
{
"path": "server/atm_server/db.py",
"chars": 10701,
"preview": "from collections import defaultdict\nimport numpy as np\nfrom flask import current_app, g\nfrom sqlalchemy import inspect\n\n"
},
{
"path": "server/atm_server/error.py",
"chars": 516,
"preview": "class ApiError(Exception):\n \"\"\"\n API error handler Exception\n See: http://flask.pocoo.org/docs/0.12/patterns/ap"
},
{
"path": "server/atm_server/recommender/OneHotEncoder.py",
"chars": 13878,
"preview": "import numpy as np\nfrom scipy import sparse\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.utils "
},
{
"path": "server/atm_server/recommender/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "server/atm_server/recommender/encoder.py",
"chars": 4312,
"preview": "from __future__ import division, unicode_literals\n\nfrom builtins import object\n\nimport numpy as np\nimport pandas as pd\nf"
},
{
"path": "server/atm_server/recommender/logging_.py",
"chars": 2229,
"preview": "# -*- encoding: utf-8 -*-\nimport logging\nimport logging.config\nimport os\nimport sys\n\nimport yaml\n\n\ndef setup_logger(outp"
},
{
"path": "server/atm_server/recommender/metafeature.py",
"chars": 5224,
"preview": "from abc import ABCMeta, abstractmethod\nfrom six import StringIO\nimport time\nimport types\n\nfrom scipy.io import arff\nimp"
},
{
"path": "server/atm_server/recommender/metafeatures.py",
"chars": 42713,
"preview": "from collections import defaultdict, OrderedDict, deque\nimport copy\nimport sys\n\nimport numpy as np\nimport scipy.stats\nfr"
},
{
"path": "server/atm_server/recommender/predict_dataset.py",
"chars": 6380,
"preview": "import atm_server.recommender.metafeatures as metafeatures\nimport atm_server.recommender.encoder as encoder\nimport panda"
},
{
"path": "server/atm_server/server.py",
"chars": 2977,
"preview": "import os\nimport sys\nsys.path.append(os.path.dirname(os.path.dirname(__file__)))\nimport argparse\ntry:\n import simplej"
},
{
"path": "server/atm_server/utils.py",
"chars": 1712,
"preview": "import uuid\nimport decimal\nimport datetime\n\ntry:\n import simplejson as json\nexcept ImportError:\n import json\n\nimpo"
},
{
"path": "server/config/run.1.yaml",
"chars": 1352,
"preview": "## Dataset arguments\ntrain_path: atm/data/test/pollution_1.csv\n# if test_path is not supplied, train_path is assumed to "
},
{
"path": "server/config/run.yaml",
"chars": 359,
"preview": "budget: 1000\nbudget_type: classifier\nclass_column: class\ndata_description: Example dataset description\ndataset_id: null\n"
},
{
"path": "server/config/sql.yaml",
"chars": 336,
"preview": "# SQL dialect\ndialect: sqlite\n# Name of the database\ndatabase: atm.db\n# Username to gain access to the database\nusername"
},
{
"path": "server/recommender_dataset/data_meta_first.csv",
"chars": 168955,
"preview": ",dataset_name,NumberOfInstancesWithMissingValues,ClassProbabilitySTD,NumberOfInstances,LogNumberOfFeatures,RatioNumerica"
},
{
"path": "server/recommender_dataset/dataset_method_PMA.csv",
"chars": 290128,
"preview": "method,dataset_id,name,test_judgment_metric,max_test_judgment_metric,PMA\nclassify_svm,1,2dplanes_1,0.563809835,0.9290722"
},
{
"path": "server/requirements.txt",
"chars": 62,
"preview": "cachelib==0.1\nflask==1.0\nflask-cors==3.0.0\nsqlalchemy==1.1.14\n"
},
{
"path": "server/setup.py",
"chars": 274,
"preview": "from setuptools import setup\n\nsetup(\n name='atm-vis',\n version='0.0.0',\n url='http://flask.pocoo.org/docs/tutor"
},
{
"path": "src/Const.tsx",
"chars": 419,
"preview": "export const URL = process.env.NODE_ENV === 'development'\n ? 'http://localhost:7777'\n : location.origin\n\nexport co"
},
{
"path": "src/assets/methodsDef.1.json",
"chars": 16301,
"preview": "\n{\n \"svm\":{ \n \"name\": \"svm\",\n \"class\": \"sklearn.svm.SVC\",\n \"fullname\":\"support vector machine\""
},
{
"path": "src/assets/methodsDef.json",
"chars": 16002,
"preview": "\n{\n \"svm\":{\n \"name\": \"svm\",\n \"class\": \"sklearn.svm.SVC\",\n \"fullname\":\"support vector machine\",\n "
},
{
"path": "src/components/App.css",
"chars": 1233,
"preview": "@import '~antd/dist/antd.css';\n\n:root{\n --header-height: 6vh;\n}\n.app{\n width:100vw;\n height:100vh;\n}\n.col{\n padding:"
},
{
"path": "src/components/App.test.tsx",
"chars": 258,
"preview": "import * as React from 'react';\nimport * as ReactDOM from 'react-dom';\nimport App from './App';\n\nit('renders without cra"
},
{
"path": "src/components/App.tsx",
"chars": 5715,
"preview": "import { Col, Layout, Row } from 'antd';\nimport * as React from 'react';\nimport * as logo from '../assets/ATM-Logo.png';"
},
{
"path": "src/components/DataRuns/AskModal.tsx",
"chars": 1680,
"preview": "import * as React from 'react';\nimport { Modal, Button } from 'antd';\n\nexport interface AskModalProps {\n AskModalCallB"
},
{
"path": "src/components/DataRuns/BarChart.tsx",
"chars": 4588,
"preview": "import ReactEcharts from \"echarts-for-react\";\nimport { getColor } from \"helper\"\nimport * as React from \"react\";\nimport {"
},
{
"path": "src/components/DataRuns/DataRuns.tsx",
"chars": 12947,
"preview": "// library\n// import axios from \"axios\";\nimport * as React from \"react\";\n//import { Tabs, Row, Col, Progress } from 'ant"
},
{
"path": "src/components/DataRuns/Histogram.tsx",
"chars": 2643,
"preview": "import ReactEcharts from \"echarts-for-react\";\nimport {getColor} from \"helper\"\nimport * as React from \"react\";\nimport { I"
},
{
"path": "src/components/DataRuns/HyperPartitions.tsx",
"chars": 3639,
"preview": "import ReactEcharts from \"echarts-for-react\";\nimport {getColor} from \"helper\"\nimport * as React from \"react\";\nimport {IC"
},
{
"path": "src/components/DataRuns/Methods.css",
"chars": 459,
"preview": ".methods{\n overflow-y: scroll;\n padding: 10px;\n}\n\n.usedMethodContainer{\n padding: 3px 8px;\n float: left;\n}\n\n"
},
{
"path": "src/components/DataRuns/Methods.tsx",
"chars": 10267,
"preview": "import { } from 'antd';\nimport * as React from 'react';\nimport * as methodsDef from \"../../assets/methodsDef.json\";\nimpo"
},
{
"path": "src/components/DataRuns/MethodsDotBarChart.css",
"chars": 671,
"preview": ".methods{\n overflow-y: scroll;\n padding: 10px;\n}\n\n.usedMethodContainer{\n padding: 3px 8px;\n float: left;\n}\n\n"
},
{
"path": "src/components/DataRuns/MethodsDotBarChart.tsx",
"chars": 12496,
"preview": "//import { Button } from 'antd';\nimport * as React from 'react';\nimport * as methodsDef from \"../../assets/methodsDef.js"
},
{
"path": "src/components/DataRuns/MethodsLineChart.css",
"chars": 888,
"preview": ".methods{\n overflow-y: scroll;\n padding: 10px;\n}\n\n.usedMethodContainer{\n padding: 3px 8px;\n float: left;\n}\n\n"
},
{
"path": "src/components/DataRuns/MethodsLineChart.tsx",
"chars": 58460,
"preview": "import { Button,Checkbox,message,InputNumber } from 'antd';\nimport * as React from 'react';\nimport * as methodsDef from "
},
{
"path": "src/components/DataRuns/MethodsLineChart_backup.css",
"chars": 888,
"preview": ".methods{\n overflow-y: scroll;\n padding: 10px;\n}\n\n.usedMethodContainer{\n padding: 3px 8px;\n float: left;\n}\n\n"
},
{
"path": "src/components/DataRuns/MethodsLineChart_backup.tsx",
"chars": 46449,
"preview": "import { Button,Checkbox,message,InputNumber } from 'antd';\nimport * as React from 'react';\nimport * as methodsDef from "
},
{
"path": "src/components/DataRuns/MethodsSearchSpace.css",
"chars": 459,
"preview": ".methods{\n overflow-y: scroll;\n padding: 10px;\n}\n\n.usedMethodContainer{\n padding: 3px 8px;\n float: left;\n}\n\n"
},
{
"path": "src/components/DataRuns/MethodsSearchSpace.tsx",
"chars": 23435,
"preview": "import { Button } from 'antd';\nimport * as React from 'react';\nimport * as methodsDef from \"../../assets/methodsDef.json"
},
{
"path": "src/components/DataRuns/ThreeLevel/HyperParameters.tsx",
"chars": 18749,
"preview": "import * as React from \"react\";\nimport { IClassifierInfo } from \"service/dataService\"\nimport { getColor } from 'helper';"
},
{
"path": "src/components/DataRuns/ThreeLevel/HyperPartitions.css",
"chars": 539,
"preview": ".RadioBox input{vertical-align:middle; margin-bottom:2px; *margin-bottom:2px;}\n\ndiv.tooltip {\n position: absolute;\n "
},
{
"path": "src/components/DataRuns/ThreeLevel/HyperPartitions.tsx",
"chars": 22971,
"preview": "import * as React from \"react\";\nimport { IHyperpartitionInfo, IClassifierInfo } from \"service/dataService\"\nimport { getC"
},
{
"path": "src/components/DataRuns/ThreeLevel/Methods.css",
"chars": 888,
"preview": ".methods{\n overflow-y: scroll;\n padding: 10px;\n}\n\n.usedMethodContainer{\n padding: 3px 8px;\n float: left;\n}\n\n"
},
{
"path": "src/components/DataRuns/ThreeLevel/Methods.tsx",
"chars": 32853,
"preview": "import * as React from \"react\";\nimport { IClassifier, IMethod, } from \"types\";\nimport { getColor } from \"helper\";\nimpor"
},
{
"path": "src/components/DataRuns/ThreeLevel/ThreeLevel.css",
"chars": 75,
"preview": ".svgWrapper{\n width: 100%;\n border-top: 6px solid rgba(0,0,0, 0.4);\n}"
},
{
"path": "src/components/DataRuns/ThreeLevel/ThreeLevel.tsx",
"chars": 32900,
"preview": "import * as React from \"react\";\nimport Methods from './Methods';\nimport HyperPartitions from \"./HyperPartitions\";\nimport"
},
{
"path": "src/components/DataRuns/ThreeLevel/index.tsx",
"chars": 65,
"preview": "import ThreeLevel from './ThreeLevel'\nexport default ThreeLevel;"
},
{
"path": "src/components/DataRuns/ThreeLevel_Horizontal/HyperParameters.tsx",
"chars": 18613,
"preview": "import * as React from \"react\";\nimport { IClassifierInfo } from \"service/dataService\"\nimport { getColor } from 'helper';"
},
{
"path": "src/components/DataRuns/ThreeLevel_Horizontal/HyperPartitions.css",
"chars": 539,
"preview": ".RadioBox input{vertical-align:middle; margin-bottom:2px; *margin-bottom:2px;}\n\ndiv.tooltip {\n position: absolute;\n "
},
{
"path": "src/components/DataRuns/ThreeLevel_Horizontal/HyperPartitions.tsx",
"chars": 23726,
"preview": "import * as React from \"react\";\nimport { IHyperpartitionInfo, IClassifierInfo } from \"service/dataService\"\nimport { getC"
},
{
"path": "src/components/DataRuns/ThreeLevel_Horizontal/Methods.css",
"chars": 888,
"preview": ".methods{\n overflow-y: scroll;\n padding: 10px;\n}\n\n.usedMethodContainer{\n padding: 3px 8px;\n float: left;\n}\n\n"
},
{
"path": "src/components/DataRuns/ThreeLevel_Horizontal/Methods.tsx",
"chars": 29553,
"preview": "import * as React from \"react\";\nimport { IClassifier, IMethod, } from \"types\";\nimport { getColor } from \"helper\";\nimpor"
},
{
"path": "src/components/DataRuns/ThreeLevel_Horizontal/ThreeLevel.tsx",
"chars": 26652,
"preview": "import * as React from \"react\";\nimport Methods from './Methods';\nimport HyperPartitions from \"./HyperPartitions\";\nimport"
},
{
"path": "src/components/DataRuns/ThreeLevel_Horizontal/index.tsx",
"chars": 65,
"preview": "import ThreeLevel from './ThreeLevel'\nexport default ThreeLevel;"
},
{
"path": "src/components/DataRuns/index.tsx",
"chars": 59,
"preview": "import DataRuns from './DataRuns'\nexport default DataRuns;"
},
{
"path": "src/components/DataView.css",
"chars": 789,
"preview": ".datasetInfo{\n padding: 10px;\n width: 100%;\n text-align: center;\n}\n.dataViewColContainer{\n height: 100%;\n "
},
{
"path": "src/components/DataView.tsx",
"chars": 7963,
"preview": "// import axios from \"axios\";\nimport * as React from \"react\";\nimport FeatureChart from \"./SidePanel/FeatureChart\"\nimport"
},
{
"path": "src/components/SidePanel/DataSelector.css",
"chars": 0,
"preview": ""
},
{
"path": "src/components/SidePanel/DataSelector.tsx",
"chars": 10486,
"preview": "import * as React from 'react';\nimport { Select, Row, Col, Upload, Icon, Button, message } from 'antd';\nimport { IDatase"
},
{
"path": "src/components/SidePanel/DataView.css",
"chars": 576,
"preview": ".datasetInfo{\n padding: 10px;\n width: 100%;\n text-align: center;\n}\n\n.dataViewColContainer{\n height: 100%;\n}\n"
},
{
"path": "src/components/SidePanel/DataView.tsx",
"chars": 3979,
"preview": "import * as React from 'react';\nimport { getDatasetCSV } from 'service/dataService';\nimport FeatureChart from './Feature"
},
{
"path": "src/components/SidePanel/FeatureChart.tsx",
"chars": 7048,
"preview": "import ReactEcharts from \"echarts-for-react\";\nimport * as React from \"react\";\n// import { prepareBoxplotData } from '../"
},
{
"path": "src/components/SidePanel/LeaderBoard.css",
"chars": 937,
"preview": ".lb-classifier {\n background-color: #dddddd;\n color: #dddddd;\n position: absolute;\n left: 100px;\n top: 8"
},
{
"path": "src/components/SidePanel/LeaderBoard.tsx",
"chars": 12416,
"preview": "import * as React from 'react';\nimport { Collapse, Tag, InputNumber, Switch, Icon, Progress } from 'antd';\n//import { T"
},
{
"path": "src/components/SidePanel/LineChart.tsx",
"chars": 2792,
"preview": "import * as React from 'react';\nimport ReactEcharts from 'echarts-for-react';\nimport {getColor} from \"helper\";\nimport { "
},
{
"path": "src/components/SidePanel/OverallHistogram.tsx",
"chars": 2879,
"preview": "import ReactEcharts from \"echarts-for-react\";\n//import {getColor} from \"helper\"\nimport * as React from \"react\";\nimport {"
},
{
"path": "src/components/SidePanel/SettingsModal.tsx",
"chars": 8953,
"preview": "import * as React from 'react';\nimport { Collapse,Modal, Button, Checkbox,InputNumber,message,Select } from 'antd';\ni"
},
{
"path": "src/components/SidePanel/SidePanel.tsx",
"chars": 1816,
"preview": "import * as React from 'react';\nimport {Tabs} from 'antd';\nimport DataSelector from './DataSelector';\nimport DataView fr"
},
{
"path": "src/components/SidePanel/index.css",
"chars": 126,
"preview": ".side-panel h4 {\n margin-top: 6px;\n margin-bottom: 2px;\n border-color: #888;\n}\n\n.side-panel hr {\n margin-top"
},
{
"path": "src/components/SidePanel/index.tsx",
"chars": 84,
"preview": "import './index.css';\nimport SidePanel from './SidePanel';\nexport default SidePanel;"
},
{
"path": "src/components/UploadModal.tsx",
"chars": 2045,
"preview": "import * as React from 'react';\nimport { Modal, Button, Input,Alert } from 'antd';\nimport {USER_STUDY} from 'Const';\nex"
},
{
"path": "src/components/Utils/GenerateSvg.tsx",
"chars": 535,
"preview": "import * as React from 'react';\nexport interface IProps {\n id: string\n}\nexport interface IState {\n id: ''\n};\n\nexpo"
},
{
"path": "src/helper/index.tsx",
"chars": 8010,
"preview": "import {IClassifier, IDatarun} from 'types';\n\nconst names: string[] = ['svm', 'knn', 'mlp', 'bnb', 'sgd', 'pa','dt','rf'"
},
{
"path": "src/images.d.ts",
"chars": 93,
"preview": "declare module '*.svg'\ndeclare module '*.png'\ndeclare module '*.jpg'\ndeclare module '*.json'\n"
},
{
"path": "src/index.css",
"chars": 64,
"preview": "body {\n margin: 0;\n padding: 0;\n font-family: sans-serif;\n}\n\n"
},
{
"path": "src/index.tsx",
"chars": 292,
"preview": "import * as React from 'react';\nimport * as ReactDOM from 'react-dom';\nimport App from 'components/App';\nimport './index"
},
{
"path": "src/registerServiceWorker.ts",
"chars": 4534,
"preview": "// tslint:disable:no-console\n// In production, we register a service worker to serve assets from local cache.\n\n// This l"
},
{
"path": "src/service/dataService.ts",
"chars": 12333,
"preview": "import axios from 'axios';\nimport { URL } from 'Const';\nimport { IDatarunStatusTypes, IClassifierStatusTypes } from 'typ"
},
{
"path": "src/types/index.ts",
"chars": 1211,
"preview": "export interface IMethod{\n name: string,\n fullname:string,\n hyperparameters: any,\n root_hyperparameters: str"
},
{
"path": "start.sh",
"chars": 149,
"preview": "#!/bin/sh\n. /vagrant/venv/bin/activate\nexport PYTHONPATH=$PYTHONPATH:`pwd`/server/\nnpm start > node.log &\npython server/"
},
{
"path": "startserver.sh",
"chars": 97,
"preview": "#!/bin/sh\nexport PYTHONPATH=$PYTHONPATH:`pwd`/server/\npython server/atm_server/server.py --debug\n"
},
{
"path": "tsconfig.json",
"chars": 682,
"preview": "{\n \"compilerOptions\": {\n \"baseUrl\": \"./src\",\n \"outDir\": \"build/dist\",\n \"module\": \"esnext\",\n \"target\": \"es5\""
},
{
"path": "tsconfig.prod.json",
"chars": 34,
"preview": "{\n \"extends\": \"./tsconfig.json\"\n}"
},
{
"path": "tsconfig.test.json",
"chars": 87,
"preview": "{\n \"extends\": \"./tsconfig.json\",\n \"compilerOptions\": {\n \"module\": \"commonjs\"\n }\n}"
},
{
"path": "tslint.json",
"chars": 403,
"preview": "{\n \"extends\": [\"tslint-react\", \"tslint-config-prettier\"],\n \"linterOptions\": {\n \"exclude\": [\n \"config/**/*.js\","
},
{
"path": "vagrantProvision.sh",
"chars": 595,
"preview": "sudo apt-get update -y && \\\n apt-get install -y python3 python3-pip && \\\n pip3 install --upgrade pip\n\nsudo apt-get i"
},
{
"path": "vagrantStart.sh",
"chars": 151,
"preview": "cd /vagrant\n. /vagrant/venv/bin/activate\nexport PYTHONPATH=$PYTHONPATH:`pwd`/server/\nnpm start > node.log &\npython serve"
}
]
About this extraction
This page contains the full source code of the HDI-Project/ATMSeer GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 127 files (1.5 MB), approximately 539.3k tokens, and a symbol index with 693 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.