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27b2712016
* Refactor into automl subpackage Moved some of the packages into an automl subpackage to tidy before the task-based refactor. This is in response to discussions with the group and a comment on the first task-based PR. Only changes here are moving subpackages and modules into the new automl, fixing imports to work with this structure and fixing some dependencies in setup.py. * Fix doc building post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Remove vw from test deps as this is breaking the build * Move default back to the top-level I'd moved this to automl as that's where it's used internally, but had missed that this is actually part of the public interface so makes sense to live where it was. * Re-add top level modules with deprecation warnings flaml.data, flaml.ml and flaml.model are re-added to the top level, being re-exported from flaml.automl for backwards compatability. Adding a deprecation warning so that we can have a planned removal later. * Fix model.py line-endings * WIP * WIP - Notes below Got to the point where the methods from AutoML are pulled to GenericTask. Started removing private markers and removing the passing of automl to these methods. Done with decide_split_type, started on prepare_data. Need to do the others after * Re-add generic_task * Fix tests: add Task.__str__ * Fix tests: test for ray.ObjectRef * Hotwire TS_Sklearn wrapper to fix test fail * Remove unused data size field from Task * Fix import for CLASSIFICATION in notebook * Update flaml/automl/data.py Co-authored-by: Chi Wang <wang.chi@microsoft.com> * Fix review comments * Fix task -> str in custom learner constructor * Remove unused CLASSIFICATION imports * Hotwire TS_Sklearn wrapper to fix test fail by setting optimizer_for_horizon == False * Revert changes to the automl_classification and pin FLAML version * Fix imports in reverted notebook * Fix FLAML version in automl notebooks * Fix ml.py line endings * Fix CLASSIFICATION task import in automl_classification notebook * Uncomment pip install in notebook and revert import Not convinced this will work because of installing an older version of the package into the environment in which we're running the tests, but let's see. * Revert c6a5dd1a0 * Revert "Revert c6a5dd1a0" This reverts commit e55e35adea03993de87b23f092b14c6af623d487. * Black format model.py * Bump version to 1.1.2 in automl_xgboost * Add docstrings to the Task ABC * Fix import in custom_learner * fix 'optimize_for_horizon' for ts_sklearn * remove debugging print statements * Check for is_forecast() before is_classification() in decide_split_type * Attempt to fix formatting fail * Another attempt to fix formatting fail * And another attempt to fix formatting fail * Add type annotations for task arg in signatures and docstrings * Fix formatting * Fix linting --------- Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: EgorKraevTransferwise <egor.kraev@transferwise.com> Co-authored-by: Chi Wang <wang.chi@microsoft.com> Co-authored-by: Kevin Chen <chenkevin.8787@gmail.com>
454 lines
252 KiB
Plaintext
454 lines
252 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"Copyright (c) Microsoft Corporation. All rights reserved. \n",
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"\n",
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"Licensed under the MIT License.\n",
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"\n",
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"# AutoVW: ChaCha for Online AutoML with Vowpal Wabbit\n",
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"\n",
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"\n",
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"## 1. Introduction\n",
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"\n",
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"\n",
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"In this notebook, we use one real data example (regression task) to showcase AutoVW, which is an online AutoML solution based on the following work:\n",
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"\n",
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"*ChaCha for online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021.*\n",
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"\n",
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"AutoVW is implemented in FLAML. FLAML requires `Python>=3.7`. To run this notebook example, please install:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install flaml[notebook,vw]==1.1.2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"## 2. Online regression with AutoVW\n",
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"### Load data from openml and preprocess\n",
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"\n",
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"Download [NewFuelCar](https://www.openml.org/d/41506) from OpenML."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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},
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(36203, 17) (36203,)\n"
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]
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}
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],
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"source": [
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"import openml\n",
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"# did = 42183\n",
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"did = 41506\n",
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"ds = openml.datasets.get_dataset(did)\n",
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"target_attribute = ds.default_target_attribute\n",
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"data = ds.get_data(target=target_attribute, dataset_format='array')\n",
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"X, y = data[0], data[1]\n",
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"print(X.shape, y.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Convert the openml dataset into vowpalwabbit examples:\n",
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"Sequentially group features into up to 10 namespaces and convert the original data examples into vowpal wabbit format."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"openml example: 8.170000076293945 [1.0000e+01 7.0000e+00 3.0000e+00 4.0000e+00 nan 6.3300e+00\n",
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" 1.3600e-01 7.3300e+00 7.0100e+00 6.9800e+00 3.0000e-03 7.0000e+00\n",
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" 9.7000e+00 1.2300e+01 1.0217e+03 0.0000e+00 5.8000e+01]\n",
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"vw example: 8.170000076293945 |a 0:10.000000 1:7.000000|b 2:3.000000 3:4.000000|c 4:nan 5:6.330000|d 6:0.136000 7:7.330000|e 8:7.010000 9:6.980000|f 10:0.003000 11:7.000000|g 12:9.700000 13:12.300000|h 14:1021.700012 15:0.000000|i 16:58.000000\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import string\n",
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"NS_LIST = list(string.ascii_lowercase) + list(string.ascii_uppercase)\n",
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"max_ns_num = 10 # the maximum number of namespaces\n",
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"orginal_dim = X.shape[1]\n",
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"max_size_per_group = int(np.ceil(orginal_dim / float(max_ns_num)))\n",
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"# sequential grouping\n",
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"group_indexes = []\n",
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"for i in range(max_ns_num):\n",
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" indexes = [ind for ind in range(i * max_size_per_group,\n",
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" min((i + 1) * max_size_per_group, orginal_dim))]\n",
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" if len(indexes) > 0:\n",
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" group_indexes.append(indexes)\n",
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"\n",
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"vw_examples = []\n",
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"for i in range(X.shape[0]):\n",
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" ns_content = []\n",
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" for zz in range(len(group_indexes)):\n",
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" ns_features = ' '.join('{}:{:.6f}'.format(ind, X[i][ind]) for ind in group_indexes[zz])\n",
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" ns_content.append(ns_features)\n",
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" ns_line = '{} |{}'.format(str(y[i]), '|'.join('{} {}'.format(NS_LIST[j], ns_content[j]) for j in range(len(group_indexes))))\n",
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" vw_examples.append(ns_line)\n",
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"print('openml example:', y[0], X[0])\n",
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"print('vw example:', vw_examples[0])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Set up the online learning loop\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.metrics import mean_squared_error\n",
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"def online_learning_loop(iter_num, vw_examples, vw_alg):\n",
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" \"\"\"Implements the online learning loop.\n",
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" \"\"\"\n",
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" print('Online learning for', iter_num, 'steps...')\n",
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" loss_list = []\n",
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" for i in range(iter_num):\n",
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" vw_x = vw_examples[i]\n",
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" y_true = float(vw_examples[i].split('|')[0])\n",
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" # predict step\n",
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" y_pred = vw_alg.predict(vw_x)\n",
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" # learn step\n",
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" vw_alg.learn(vw_x)\n",
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" # calculate one step loss\n",
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" loss = mean_squared_error([y_pred], [y_true])\n",
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" loss_list.append(loss)\n",
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" return loss_list\n",
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"\n",
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"max_iter_num = 10000 # or len(vw_examples)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Vanilla Vowpal Wabbit (VW)\n",
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"Create and run a vanilla vowpal wabbit learner."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Online learning for 10000 steps...\n",
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"Final progressive validation loss of vanilla vw: 15.18087237487917\n"
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]
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}
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],
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"source": [
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"from vowpalwabbit import pyvw\n",
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"''' create a vanilla vw instance '''\n",
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"vanilla_vw = pyvw.vw('--quiet')\n",
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"\n",
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"# online learning with vanilla VW\n",
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"loss_list_vanilla = online_learning_loop(max_iter_num, vw_examples, vanilla_vw)\n",
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"print('Final progressive validation loss of vanilla vw:', sum(loss_list_vanilla)/len(loss_list_vanilla))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### AutoVW which tunes namespace interactions \n",
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"Create and run an AutoVW instance which tunes namespace interactions. Each AutoVW instance allows ```max_live_model_num``` of VW models (each associated with its own hyperaparameter configurations that are tuned online) to run concurrently in each step of the online learning loop."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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},
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Seed namespaces (singletons and interactions): ['g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
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"Created challengers from champion ||\n",
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"New challenger size 37, ['|ah|', '|eg|', '|gi|', '|ag|', '|de|', '|ei|', '|eh|', '|fg|', '|cf|', '|hi|', '|bf|', '|cd|', '|ai|', '|ef|', '|cg|', '|ch|', '|ad|', '|bc|', '|gh|', '|bh|', '|ci|', '|fh|', '|bg|', '|be|', '|bd|', '|fi|', '|bi|', '|df|', '|ac|', '|ae|', '|dg|', '|af|', '|di|', '|ce|', '|dh|', '|ab|', '||']\n",
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"Online learning for 10000 steps...\n",
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"Seed namespaces (singletons and interactions): ['ce', 'g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
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"Created challengers from champion |ce|\n",
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"New challenger size 43, ['|be_ce|', '|bce_ce|', '|ce_ei|', '|ce_ceg|', '|ce_fh|', '|ce_gh|', '|ce_cef|', '|cd_ce|', '|ce_cg|', '|cde_ce|', '|ce_cf|', '|bd_ce|', '|ae_ce|', '|ce_gi|', '|ce_ci|', '|ab_ce|', '|ce_fg|', '|ce_di|', '|bi_ce|', '|ce_de|', '|ce_eg|', '|ce_dg|', '|ce_hi|', '|ai_ce|', '|ag_ce|', '|ac_ce|', '|bh_ce|', '|ce_ch|', '|ce|', '|ace_ce|', '|ah_ce|', '|af_ce|', '|bc_ce|', '|ce_dh|', '|ce_ef|', '|ad_ce|', '|ce_df|', '|ce_cei|', '|ce_eh|', '|bg_ce|', '|ce_ceh|', '|bf_ce|', '|ce_fi|']\n",
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"Final progressive validation loss of autovw: 8.718817421944529\n"
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]
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}
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],
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"source": [
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"''' import AutoVW class from flaml package '''\n",
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"from flaml import AutoVW\n",
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"\n",
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"'''create an AutoVW instance for tuning namespace interactions'''\n",
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"# configure both hyperparamters to tune, e.g., 'interactions', and fixed arguments about the online learner,\n",
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"# e.g., 'quiet' in the search_space argument.\n",
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"autovw_ni = AutoVW(max_live_model_num=5, search_space={'interactions': AutoVW.AUTOMATIC, 'quiet': ''})\n",
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"\n",
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"# online learning with AutoVW\n",
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"loss_list_autovw_ni = online_learning_loop(max_iter_num, vw_examples, autovw_ni)\n",
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"print('Final progressive validation loss of autovw:', sum(loss_list_autovw_ni)/len(loss_list_autovw_ni))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Online performance comparison between vanilla VW and AutoVW"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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108.309246 209.272048 \nL 108.349877 209.190934 \nL 108.431138 209.263334 \nL 108.512399 208.973635 \nL 108.59366 208.786536 \nL 108.634291 208.842899 \nL 109.121857 209.714479 \nL 109.162488 209.744551 \nL 109.203119 209.670891 \nL 109.528163 209.135649 \nL 109.568794 209.199085 \nL 110.01573 209.996062 \nL 110.137622 209.994993 \nL 110.340775 209.083766 \nL 110.422036 209.162161 \nL 110.543928 209.205534 \nL 111.072125 210.03834 \nL 111.112756 209.998836 \nL 111.234647 209.762004 \nL 111.39717 209.547009 \nL 111.519061 209.376672 \nL 111.559692 209.455887 \nL 112.047259 210.293805 \nL 112.169151 210.092371 \nL 112.372303 209.665813 \nL 112.494195 209.448572 \nL 112.534826 209.517453 \nL 112.981762 210.14666 \nL 113.063023 210.202972 \nL 113.103654 210.170969 \nL 113.469329 209.214838 \nL 113.591221 209.372328 \nL 114.119418 210.153064 \nL 114.28194 209.82369 \nL 114.322571 209.907729 \nL 114.363202 209.875544 \nL 114.485093 209.448661 \nL 114.525724 209.483957 \nL 115.013291 210.302385 \nL 115.053921 210.280487 \nL 115.419597 209.829055 \nL 115.460227 209.867973 \nL 115.988425 210.643269 \nL 116.272839 210.106103 \nL 116.3541 210.024346 \nL 116.39473 210.052091 \nL 116.963558 210.827827 \nL 117.288603 210.257778 \nL 117.369864 210.248585 \nL 117.979323 211.026348 \nL 118.019953 210.993651 \nL 118.223106 210.724278 \nL 118.344998 210.585613 \nL 118.385629 210.658072 \nL 118.873195 211.378672 \nL 118.995087 211.362805 \nL 119.076348 211.198673 \nL 119.157609 211.24954 \nL 119.360762 211.09604 \nL 119.401393 211.170626 \nL 119.92959 211.809784 \nL 120.010852 211.665584 \nL 120.335896 211.054545 \nL 120.945355 211.974438 \nL 121.067246 211.823051 \nL 121.229769 211.730222 \nL 121.31103 211.504558 \nL 121.351661 211.534832 \nL 121.920489 212.348067 \nL 122.286164 211.900157 \nL 122.773731 212.556377 \nL 122.895622 212.376149 \nL 123.180036 212.045587 \nL 123.261298 212.100993 \nL 123.789495 212.887251 \nL 123.952017 212.784737 \nL 124.033278 212.879599 \nL 124.073909 212.854808 \nL 124.195801 212.720663 \nL 124.723998 213.471035 \nL 124.764629 213.435349 \nL 125.170935 213.08652 \nL 125.211565 213.139067 \nL 125.699132 213.789617 \nL 125.861654 213.619903 \nL 126.105438 213.311307 \nL 126.227329 213.407418 \nL 126.755527 214.021774 \nL 126.877419 213.8767 \nL 126.918049 213.813133 \nL 126.95868 213.843549 \nL 126.99931 213.900826 \nL 127.161833 213.335934 \nL 127.243094 213.427283 \nL 127.527508 213.820479 \nL 127.771291 214.077398 \nL 128.177597 213.577655 \nL 128.218228 213.634511 \nL 128.746425 214.184497 \nL 128.868317 213.969145 \nL 128.908947 214.016477 \nL 128.990209 213.915881 \nL 129.274623 213.39097 \nL 129.640298 213.849492 \nL 129.80282 213.628701 \nL 129.884081 213.557716 \nL 129.924712 213.613814 \nL 130.005973 213.557425 \nL 130.127865 213.406697 \nL 130.168495 213.454015 \nL 130.53417 213.956613 \nL 130.656062 213.999998 \nL 131.021737 213.528213 \nL 131.590565 214.207732 \nL 131.712457 214.183261 \nL 132.037502 213.866801 \nL 132.159393 213.996033 \nL 132.60633 214.517875 \nL 132.687591 214.454617 \nL 132.768852 214.372269 \nL 132.809483 214.433615 \nL 132.890744 214.355892 \nL 133.012636 214.271087 \nL 133.540833 214.887905 \nL 133.703355 214.851553 \nL 133.987769 214.51606 \nL 134.06903 214.612613 \nL 134.475336 215.124087 \nL 134.597228 215.2249 \nL 134.637858 215.180884 \nL 134.75975 215.098936 \nL 134.800381 215.149827 \nL 134.841011 215.171006 \nL 135.003534 214.866284 \nL 135.572362 215.444465 \nL 135.897406 215.06065 \nL 136.384973 215.563501 \nL 136.466234 215.60065 \nL 136.750648 215.302895 \nL 136.87254 215.314686 \nL 137.441368 215.899035 \nL 137.644521 215.570932 \nL 137.685152 215.593353 \nL 137.847674 215.448891 \nL 137.888304 215.50104 \nL 138.416502 216.064786 \nL 138.538394 215.928303 \nL 138.700916 215.867802 \nL 138.863438 215.845297 \nL 139.351005 216.362591 \nL 139.472897 216.29659 \nL 139.594789 216.127102 \nL 139.635419 216.146758 \nL 140.001094 216.263934 \nL 140.448031 216.695397 \nL 140.813706 216.328793 \nL 140.854336 216.370039 \nL 141.463795 216.978185 \nL 141.951362 216.734267 \nL 142.398298 217.22717 \nL 142.438929 217.186031 \nL 142.642082 216.863471 \nL 142.723343 216.454891 \nL 142.804604 216.510087 \nL 143.21091 216.872829 \nL 143.332802 216.920652 \nL 143.739107 216.662161 \nL 144.267305 217.129497 \nL 144.795502 216.903566 \nL 145.161177 217.288012 \nL 145.3237 217.385933 \nL 145.567483 217.246418 \nL 145.730005 217.278616 \nL 146.298833 217.79051 \nL 146.420725 217.634581 \nL 146.461356 217.682195 \nL 146.7864 217.61002 \nL 146.989553 217.831783 \nL 147.273967 218.080526 \nL 147.599012 217.550855 \nL 147.680273 217.448329 \nL 147.720904 217.481792 \nL 148.452254 217.870802 \nL 148.492885 217.868974 \nL 148.696037 217.071192 \nL 148.817929 217.207794 \nL 149.224235 217.616098 \nL 149.264865 217.567836 \nL 149.508649 217.288681 \nL 149.630541 216.852692 \nL 149.671171 216.878941 \nL 150.158738 217.325676 \nL 150.199369 217.280695 \nL 150.565044 216.754336 \nL 150.605674 216.797082 \nL 151.093241 217.219889 \nL 151.133872 217.176733 \nL 151.540178 216.648692 \nL 151.621439 216.735617 \nL 151.865222 216.954188 \nL 152.109006 217.121442 \nL 152.43405 216.820673 \nL 152.515311 216.680694 \nL 152.555942 216.725907 \nL 153.12477 217.178098 \nL 153.571706 216.810691 \nL 154.099904 217.205371 \nL 154.140534 217.146599 \nL 154.50621 216.636521 \nL 154.54684 216.670824 \nL 155.034407 216.996634 \nL 155.196929 217.054071 \nL 155.440713 216.846571 \nL 155.521974 216.773125 \nL 155.562605 216.817659 \nL 156.050171 217.189846 \nL 156.090802 217.144254 \nL 156.172063 217.067683 \nL 156.212694 217.109771 \nL 156.253324 217.141389 \nL 156.293955 217.038382 \nL 156.415847 216.732225 \nL 156.456477 216.757848 \nL 157.106566 217.009312 \nL 157.512872 216.918842 \nL 157.675394 217.061195 \nL 157.959808 217.246925 \nL 158.122331 217.071751 \nL 158.162961 217.114436 \nL 158.203592 217.117263 \nL 158.366114 216.773472 \nL 158.975573 217.293376 \nL 159.097465 217.178864 \nL 159.138095 217.222804 \nL 159.219356 217.194578 \nL 159.341248 217.064319 \nL 159.381879 217.090524 \nL 159.950707 217.523703 \nL 160.397643 217.164286 \nL 160.92584 217.563713 \nL 161.332146 217.193357 \nL 161.941605 217.658141 \nL 162.347911 217.424761 \nL 162.957369 217.76235 \nL 163.119891 217.680853 \nL 163.241783 217.588124 \nL 163.282414 217.628188 \nL 163.810611 217.947262 \nL 164.135656 217.843289 \nL 164.298178 217.950873 \nL 164.582592 218.17414 \nL 164.785745 218.272497 \nL 165.192051 218.056858 \nL 165.760879 218.452408 \nL 166.289076 218.47611 \nL 166.776643 218.802826 \nL 167.101688 218.651388 \nL 167.792408 219.074236 \nL 168.117452 218.850402 \nL 168.808172 219.291452 \nL 169.255108 219.246767 \nL 169.742675 219.554111 \nL 170.06772 219.493315 \nL 170.636548 219.888782 \nL 170.880331 219.822192 \nL 171.124115 219.873762 \nL 171.855465 220.200903 \nL 172.017987 220.175116 \nL 172.546185 220.559471 \nL 173.115013 220.589233 \nL 173.561949 220.861523 \nL 174.090147 220.902083 \nL 174.577714 221.16593 \nL 174.740236 221.018289 \nL 174.780866 221.053766 \nL 175.105911 221.09645 \nL 175.552847 221.345914 \nL 175.796631 221.268413 \nL 176.040414 221.191571 \nL 176.527981 221.504884 \nL 176.690503 221.456169 \nL 176.771765 221.433543 \nL 176.853026 221.275885 \nL 176.934287 221.339454 \nL 177.503115 221.603428 \nL 177.665637 221.552348 \nL 178.478249 221.890276 \nL 178.803293 221.934355 \nL 179.697166 222.366597 \nL 179.819058 222.308852 \nL 180.347255 222.645661 \nL 180.469147 222.607133 \nL 180.6723 222.52048 \nL 180.956714 222.632283 \nL 181.322389 222.794043 \nL 181.931848 222.714705 \nL 182.419415 222.973428 \nL 182.78509 222.84858 \nL 183.353918 223.212191 \nL 183.51644 223.180591 \nL 183.882115 223.18832 \nL 184.288421 223.434886 \nL 184.654096 223.318389 \nL 185.304185 223.642678 \nL 185.588599 223.558416 \nL 185.791752 223.668302 \nL 186.279319 223.924995 \nL 186.482472 223.88258 \nL 186.766886 224.020511 \nL 187.254453 224.304608 \nL 187.498236 224.265447 \nL 187.701389 224.348419 \nL 188.310848 224.654512 \nL 188.676523 224.538364 \nL 189.245351 224.864282 \nL 189.651657 224.786168 \nL 190.423638 225.087594 \nL 190.708052 224.944752 \nL 191.154988 224.664408 \nL 191.764447 224.773205 \nL 192.130122 224.935151 \nL 192.536428 224.893022 \nL 193.349039 225.172709 \nL 193.511561 225.209413 \nL 194.161651 225.535226 \nL 194.364804 225.525364 \nL 194.567956 225.614411 \nL 195.299307 225.915241 \nL 195.461829 225.727862 \nL 196.23381 226.119036 \nL 196.315071 226.036669 \nL 196.477593 225.899459 \nL 197.290205 226.153616 \nL 197.533988 226.248882 \nL 197.980925 226.445832 \nL 198.184078 226.410104 \nL 198.38723 226.453107 \nL 198.915428 226.703251 \nL 199.281103 226.601516 \nL 199.931192 226.915018 \nL 200.134345 226.869547 \nL 200.296867 226.8829 \nL 200.906326 227.205011 \nL 201.19074 227.135272 \nL 201.434524 227.228545 \nL 201.92209 227.474328 \nL 202.369027 227.322293 \nL 202.978485 227.624123 \nL 203.141008 227.620338 \nL 203.262899 227.484132 \nL 203.30353 227.512585 \nL 203.912989 227.804311 \nL 204.116142 227.731832 \nL 204.238033 227.755553 \nL 204.847492 228.039486 \nL 205.294428 228.072408 \nL 205.781995 228.304857 \nL 206.10704 228.233658 \nL 206.797759 228.555637 \nL 207.041543 228.485125 \nL 207.325957 228.644426 \nL 207.772893 228.823058 \nL 207.976046 228.762343 \nL 208.179199 228.837158 \nL 208.748027 229.051981 \nL 209.113702 228.878091 \nL 209.723161 229.183602 \nL 210.129467 229.106092 \nL 210.698295 229.342431 \nL 211.023339 229.260312 \nL 211.592167 229.554631 \nL 211.957842 229.451631 \nL 212.607932 229.74059 \nL 212.973607 229.667117 \nL 213.583065 229.943462 \nL 213.90811 229.88829 \nL 214.558199 230.202761 \nL 214.761352 230.174555 \nL 214.883244 230.111008 \nL 215.533333 230.432972 \nL 215.736486 230.368324 \nL 216.061531 230.362158 \nL 216.589728 230.553278 \nL 216.955403 230.515644 \nL 217.605492 230.821558 \nL 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|
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"text/plain": [
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"<Figure size 576x432 with 1 Axes>"
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]
|
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},
|
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"metadata": {
|
|
"needs_background": "light"
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|
},
|
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"output_type": "display_data"
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}
|
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],
|
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"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"def plot_progressive_loss(obj_list, alias, result_interval=1):\n",
|
|
" \"\"\"Show real-time progressive validation loss\n",
|
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" \"\"\"\n",
|
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" avg_list = [sum(obj_list[:i]) / i for i in range(1, len(obj_list))]\n",
|
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" total_obs = len(avg_list)\n",
|
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" warm_starting_point = 10 #0\n",
|
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" plt.plot(range(warm_starting_point, len(avg_list)), avg_list[warm_starting_point:], label = alias)\n",
|
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" plt.xlabel('# of data samples',)\n",
|
|
" plt.ylabel('Progressive validation loss')\n",
|
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" plt.yscale('log')\n",
|
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" plt.legend(loc='upper right')\n",
|
|
"plt.figure(figsize=(8, 6))\n",
|
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"plot_progressive_loss(loss_list_vanilla, 'VanillaVW')\n",
|
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"plot_progressive_loss(loss_list_autovw_ni, 'AutoVW:NI')\n",
|
|
"plt.show()"
|
|
]
|
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},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
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"source": [
|
|
"### AutoVW which tunes both namespace interactions and learning rate\n",
|
|
"Create and run an AutoVW instance which tunes both namespace interactions and learning rate."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Seed namespaces (singletons and interactions): ['g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
|
|
"No low-cost partial config given to the search algorithm. For cost-frugal search, consider providing low-cost values for cost-related hps via 'low_cost_partial_config'.\n",
|
|
"Created challengers from champion ||0.5|\n",
|
|
"New challenger size 39, ['|gi|0.5|', '|af|0.5|', '|df|0.5|', '|gh|0.5|', '|ae|0.5|', '|di|0.5|', '|be|0.5|', '|ac|0.5|', '|hi|0.5|', '|de|0.5|', '|ef|0.5|', '|bc|0.5|', '|cf|0.5|', '|dg|0.5|', '|fg|0.5|', '|bh|0.5|', '|ei|0.5|', '|ce|0.5|', '|bf|0.5|', '|ah|0.5|', '|ad|0.5|', '|bg|0.5|', '|bd|0.5|', '|ab|0.5|', '|bi|0.5|', '|eg|0.5|', '|ai|0.5|', '|eh|0.5|', '|dh|0.5|', '|cd|0.5|', '|fi|0.5|', '|ci|0.5|', '|ag|0.5|', '|fh|0.5|', '|ch|0.5|', '|cg|0.5|', '||0.05358867312681484|', '||1.0|', '||0.5|']\n",
|
|
"Online learning for 10000 steps...\n",
|
|
"Seed namespaces (singletons and interactions): ['g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
|
|
"No low-cost partial config given to the search algorithm. For cost-frugal search, consider providing low-cost values for cost-related hps via 'low_cost_partial_config'.\n",
|
|
"Created challengers from champion ||1.0|\n",
|
|
"New challenger size 50, ['|gi|0.5|', '|af|0.5|', '|df|0.5|', '|gh|0.5|', '|ae|0.5|', '|di|0.5|', '|be|0.5|', '|ac|0.5|', '|hi|0.5|', '|de|0.5|', '|ef|0.5|', '|bc|0.5|', '|dh|1.0|', '|ah|1.0|', '|cd|1.0|', '|bh|1.0|', '|bi|1.0|', '|ab|1.0|', '|gi|1.0|', '|bg|1.0|', '|bd|1.0|', '|eh|1.0|', '|af|1.0|', '|hi|1.0|', '|cf|1.0|', '|ei|1.0|', '|ef|1.0|', '|ai|1.0|', '|ch|1.0|', '|gh|1.0|', '|fg|1.0|', '|ad|1.0|', '|ci|1.0|', '|bc|1.0|', '|ag|1.0|', '|df|1.0|', '|dg|1.0|', '|de|1.0|', '|di|1.0|', '|cg|1.0|', '|be|1.0|', '|eg|1.0|', '|ce|1.0|', '|fi|1.0|', '|ae|1.0|', '|bf|1.0|', '|fh|1.0|', '|ac|1.0|', '||0.10717734625362937|', '||0.3273795141019504|']\n",
|
|
"Final progressive validation loss of autovw_nilr: 7.611900319489723\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from flaml.tune import loguniform\n",
|
|
"''' create another AutoVW instance for tuning namespace interactions and learning rate'''\n",
|
|
"# set up the search space and init config\n",
|
|
"search_space_nilr = {'interactions': AutoVW.AUTOMATIC, 'learning_rate': loguniform(lower=2e-10, upper=1.0), 'quiet': ''}\n",
|
|
"init_config_nilr = {'interactions': set(), 'learning_rate': 0.5}\n",
|
|
"# create an AutoVW instance\n",
|
|
"autovw_nilr = AutoVW(max_live_model_num=5, search_space=search_space_nilr, init_config=init_config_nilr)\n",
|
|
"\n",
|
|
"# online learning with AutoVW\n",
|
|
"loss_list_autovw_nilr = online_learning_loop(max_iter_num, vw_examples, autovw_nilr)\n",
|
|
"print('Final progressive validation loss of autovw_nilr:', sum(loss_list_autovw_nilr)/len(loss_list_autovw_nilr))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Online performance comparison between vanilla VW and two AutoVW instances\n",
|
|
"Compare the online progressive validation loss from the vanilla VW and two AutoVW instances."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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197.203277 \nL 99.939348 197.240712 \nL 100.061239 197.338342 \nL 100.467545 198.418402 \nL 100.548806 198.37588 \nL 100.670698 198.538934 \nL 100.711328 198.457058 \nL 100.83322 197.502732 \nL 100.914481 197.569681 \nL 101.158265 198.097288 \nL 101.442679 198.623394 \nL 101.483309 198.55319 \nL 101.645832 198.345849 \nL 101.767723 197.344496 \nL 101.808354 197.353538 \nL 102.336551 198.331096 \nL 102.377182 198.286788 \nL 102.742857 197.533699 \nL 102.824118 197.70109 \nL 103.311685 198.630731 \nL 103.392946 198.570655 \nL 103.555469 198.395897 \nL 103.717991 197.985594 \nL 103.758622 198.061549 \nL 104.246188 199.066893 \nL 104.36808 199.028434 \nL 104.611864 198.539776 \nL 104.652494 198.502013 \nL 104.693125 198.551891 \nL 105.261953 199.596813 \nL 105.343214 199.536235 \nL 105.465106 199.334769 \nL 105.505736 199.372559 \nL 105.668259 199.141994 \nL 105.708889 199.164163 \nL 106.196456 200.070808 \nL 106.318348 200.152779 \nL 106.684023 199.450327 \nL 106.724654 199.534573 \nL 107.293482 200.548949 \nL 107.456004 200.321911 \nL 107.537265 200.391332 \nL 107.659157 199.997069 \nL 107.699787 200.085117 \nL 108.227985 201.041616 \nL 108.309246 200.927921 \nL 108.349877 200.850421 \nL 108.431138 200.919595 \nL 108.512399 200.642806 \nL 108.59366 200.464044 \nL 108.634291 200.517895 \nL 109.121857 201.350636 \nL 109.162488 201.379368 \nL 109.203119 201.30899 \nL 109.528163 200.7976 \nL 109.568794 200.858209 \nL 110.01573 201.619671 \nL 110.137622 201.61865 \nL 110.340775 200.748029 \nL 110.422036 200.822931 \nL 110.543928 200.864371 \nL 111.072125 201.660065 \nL 111.112756 201.622321 \nL 111.234647 201.396044 \nL 111.39717 201.190629 \nL 111.519061 201.027882 \nL 111.559692 201.103567 \nL 112.047259 201.904146 \nL 112.169151 201.711688 \nL 112.372303 201.304139 \nL 112.494195 201.096579 \nL 112.534826 201.16239 \nL 112.981762 201.763558 \nL 113.063023 201.817361 \nL 113.103654 201.786784 \nL 113.50996 200.8935 \nL 113.591221 201.023732 \nL 114.119418 201.769676 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203.893696 \nL 123.180036 203.577864 \nL 123.261298 203.630801 \nL 123.789495 204.382023 \nL 123.952017 204.284076 \nL 124.073909 204.351025 \nL 124.195801 204.222858 \nL 124.723998 204.939791 \nL 124.764629 204.905696 \nL 125.170935 204.572412 \nL 125.211565 204.622616 \nL 125.699132 205.244176 \nL 125.861654 205.082026 \nL 126.105438 204.787182 \nL 126.227329 204.87901 \nL 126.755527 205.465989 \nL 126.877419 205.327379 \nL 126.918049 205.266645 \nL 126.95868 205.295705 \nL 126.99931 205.35043 \nL 127.161833 204.810711 \nL 127.243094 204.897989 \nL 127.527508 205.273663 \nL 127.771291 205.519133 \nL 128.177597 205.04166 \nL 128.218228 205.095983 \nL 128.746425 205.62146 \nL 128.868317 205.415704 \nL 128.908947 205.460927 \nL 128.990209 205.364814 \nL 129.274623 204.863294 \nL 129.640298 205.301383 \nL 129.80282 205.090431 \nL 129.884081 205.02261 \nL 129.924712 205.076208 \nL 130.005973 205.022331 \nL 130.127865 204.878321 \nL 130.168495 204.92353 \nL 130.656062 205.445183 \nL 130.696693 205.377803 \nL 130.981107 205.009779 \nL 131.062368 205.051402 \nL 131.590565 205.643659 \nL 131.712457 205.620279 \nL 132.037502 205.317921 \nL 132.159393 205.441394 \nL 132.60633 205.939982 \nL 132.687591 205.879543 \nL 132.768852 205.800864 \nL 132.809483 205.859477 \nL 132.890744 205.785218 \nL 133.012636 205.704191 \nL 133.540833 206.293523 \nL 133.703355 206.25879 \nL 133.987769 205.938248 \nL 134.06903 206.030499 \nL 134.475336 206.51918 \nL 134.597228 206.6155 \nL 134.637858 206.573446 \nL 134.75975 206.495149 \nL 134.800381 206.543773 \nL 134.841011 206.564008 \nL 135.003534 206.272865 \nL 135.572362 206.825281 \nL 135.897406 206.458569 \nL 136.384973 206.939013 \nL 136.466234 206.974506 \nL 136.750648 206.690019 \nL 136.87254 206.701285 \nL 137.441368 207.259595 \nL 137.644521 206.946112 \nL 137.685152 206.967535 \nL 137.847674 206.82951 \nL 137.888304 206.879335 \nL 138.416502 207.417959 \nL 138.538394 207.287558 \nL 138.700916 207.229753 \nL 138.863438 207.208251 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158.366114 208.095064 \nL 158.975573 208.591801 \nL 159.097465 208.482391 \nL 159.138095 208.524374 \nL 159.219356 208.497405 \nL 159.341248 208.37295 \nL 159.381879 208.397988 \nL 159.950707 208.811863 \nL 160.397643 208.468463 \nL 160.92584 208.850091 \nL 161.332146 208.496238 \nL 161.941605 208.940311 \nL 162.347911 208.717331 \nL 162.957369 209.039875 \nL 163.119891 208.96201 \nL 163.282414 208.911693 \nL 163.810611 209.216547 \nL 164.135656 209.117208 \nL 164.298178 209.219998 \nL 164.582592 209.433315 \nL 164.785745 209.52729 \nL 165.192051 209.32126 \nL 165.760879 209.699184 \nL 166.289076 209.721829 \nL 166.776643 210.033986 \nL 167.101688 209.889296 \nL 167.792408 210.293301 \nL 168.117452 210.079442 \nL 168.808172 210.500837 \nL 169.255108 210.458144 \nL 169.742675 210.751792 \nL 170.06772 210.693705 \nL 170.636548 211.07155 \nL 170.880331 211.007927 \nL 171.124115 211.057199 \nL 171.855465 211.369761 \nL 172.017987 211.345124 \nL 172.586815 211.706751 \nL 173.155643 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187.701389 215.332455 \nL 188.310848 215.624908 \nL 188.676523 215.513936 \nL 189.245351 215.82533 \nL 189.651657 215.750697 \nL 190.423638 216.03869 \nL 190.708052 215.902214 \nL 191.195619 215.635111 \nL 191.723816 215.707714 \nL 192.130122 215.893041 \nL 192.536428 215.852789 \nL 193.349039 216.120013 \nL 193.511561 216.155081 \nL 194.202281 216.487348 \nL 194.527326 216.530367 \nL 194.81174 216.711898 \nL 195.136784 216.824826 \nL 195.339937 216.74479 \nL 195.461829 216.650426 \nL 196.23381 217.024169 \nL 196.315071 216.945472 \nL 196.477593 216.814377 \nL 197.980925 217.336402 \nL 198.184078 217.302267 \nL 198.38723 217.343353 \nL 198.915428 217.58235 \nL 199.281103 217.485149 \nL 199.931192 217.78468 \nL 200.134345 217.741236 \nL 200.296867 217.753994 \nL 200.906326 218.061751 \nL 201.19074 217.99512 \nL 201.434524 218.084236 \nL 201.92209 218.319066 \nL 202.409657 218.201458 \nL 202.937855 218.460137 \nL 203.181638 218.374127 \nL 203.262899 218.328434 \nL 203.30353 218.355618 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"text/plain": [
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"<Figure size 576x432 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"plt.figure(figsize=(8, 6))\n",
|
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"plot_progressive_loss(loss_list_vanilla, 'VanillaVW')\n",
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"plot_progressive_loss(loss_list_autovw_ni, 'AutoVW:NI')\n",
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"plot_progressive_loss(loss_list_autovw_nilr, 'AutoVW:NI+LR')\n",
|
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"plt.show()"
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]
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},
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{
|
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"cell_type": "markdown",
|
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"metadata": {},
|
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"source": [
|
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"### AutoVW based on customized VW arguments\n",
|
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"You can easily create an AutoVW instance based on customized VW arguments (For now only arguments that are compatible with supervised regression task are well supported). The customized arguments can be passed to AutoVW through init_config and search space."
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]
|
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},
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{
|
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"cell_type": "code",
|
|
"execution_count": 11,
|
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"metadata": {
|
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"tags": []
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},
|
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"outputs": [
|
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{
|
|
"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"Seed namespaces (singletons and interactions): ['g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
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"Created challengers from champion |supervised||classic|\n",
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"New challenger size 37, ['|supervised|fg|classic|', '|supervised|dh|classic|', '|supervised|ef|classic|', '|supervised|ei|classic|', '|supervised|di|classic|', '|supervised|ch|classic|', '|supervised|bh|classic|', '|supervised|cf|classic|', '|supervised|ae|classic|', '|supervised|bc|classic|', '|supervised|ci|classic|', '|supervised|eg|classic|', '|supervised|ag|classic|', '|supervised|be|classic|', '|supervised|bd|classic|', '|supervised|ce|classic|', '|supervised|af|classic|', '|supervised|ad|classic|', '|supervised|ab|classic|', '|supervised|dg|classic|', '|supervised|gh|classic|', '|supervised|bg|classic|', '|supervised|fh|classic|', '|supervised|gi|classic|', '|supervised|cg|classic|', '|supervised|cd|classic|', '|supervised|ai|classic|', '|supervised|ac|classic|', '|supervised|bi|classic|', '|supervised|eh|classic|', '|supervised|fi|classic|', '|supervised|de|classic|', '|supervised|hi|classic|', '|supervised|bf|classic|', '|supervised|df|classic|', '|supervised|ah|classic|', '|supervised||classic|']\n",
|
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"Online learning for 10000 steps...\n",
|
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"Seed namespaces (singletons and interactions): ['df', 'g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
|
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"Created challengers from champion |supervised|df|classic|\n",
|
|
"New challenger size 43, ['|supervised|ce_df|classic|', '|supervised|df_gi|classic|', '|supervised|df_fi|classic|', '|supervised|bd_df|classic|', '|supervised|ab_df|classic|', '|supervised|bi_df|classic|', '|supervised|df_ei|classic|', '|supervised|bh_df|classic|', '|supervised|cd_df|classic|', '|supervised|df_dfg|classic|', '|supervised|def_df|classic|', '|supervised|bdf_df|classic|', '|supervised|ag_df|classic|', '|supervised|cg_df|classic|', '|supervised|df_dg|classic|', '|supervised|af_df|classic|', '|supervised|ci_df|classic|', '|supervised|df_dh|classic|', '|supervised|ah_df|classic|', '|supervised|df|classic|', '|supervised|df_di|classic|', '|supervised|ad_df|classic|', '|supervised|df_ef|classic|', '|supervised|ae_df|classic|', '|supervised|ai_df|classic|', '|supervised|be_df|classic|', '|supervised|df_eg|classic|', '|supervised|ch_df|classic|', '|supervised|ac_df|classic|', '|supervised|df_gh|classic|', '|supervised|df_fg|classic|', '|supervised|bc_df|classic|', '|supervised|df_dfh|classic|', '|supervised|df_fh|classic|', '|supervised|df_dfi|classic|', '|supervised|de_df|classic|', '|supervised|bf_df|classic|', '|supervised|bg_df|classic|', '|supervised|df_hi|classic|', '|supervised|cdf_df|classic|', '|supervised|df_eh|classic|', '|supervised|cf_df|classic|', '|supervised|adf_df|classic|']\n",
|
|
"Average final loss of the AutoVW (tuning namespaces) based on customized vw arguments: 8.828759490602918\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"''' create an AutoVW instance with ustomized VW arguments'''\n",
|
|
"# parse the customized VW arguments\n",
|
|
"fixed_vw_hp_config = {'alg': 'supervised', 'loss_function': 'classic', 'quiet': ''}\n",
|
|
"search_space = fixed_vw_hp_config.copy()\n",
|
|
"search_space.update({'interactions': AutoVW.AUTOMATIC,})\n",
|
|
"\n",
|
|
"autovw_custom = AutoVW(max_live_model_num=5, search_space=search_space) \n",
|
|
"loss_list_custom = online_learning_loop(max_iter_num, vw_examples, autovw_custom)\n",
|
|
"print('Average final loss of the AutoVW (tuning namespaces) based on customized vw arguments:', sum(loss_list_custom)/len(loss_list_custom))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"interpreter": {
|
|
"hash": "4502d015faca2560a557f35a41b6dd402f7fdfc08e843ae17a9c41947939f10c"
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3.8.10 64-bit ('py38': conda)",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.10"
|
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}
|
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},
|
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"nbformat": 4,
|
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"nbformat_minor": 2
|
|
}
|