From a291abfab9c96e74ae4f7ecbb8b336d7a8c6f426 Mon Sep 17 00:00:00 2001
From: Qingyun Wu <qw2ky@virginia.edu>
Date: Mon, 5 Jul 2021 21:17:26 -0400
Subject: [PATCH] Cha cha (#127)

* unordered categorical

* allow cost attribute to be None

* tensorboardX version

* quote

* cfo cat

* trunc

* Update version.py

* incumbent is normalized

* python 3.9

* remove ConcurrencyLimiter

* seed

* estimator

* update autovw notebook

Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qiw@microsoft.com>
---
 .github/workflows/python-package.yml |  5 +-
 README.md                            |  2 +-
 flaml/automl.py                      | 11 +---
 flaml/model.py                       |  6 ++
 flaml/searcher/blendsearch.py        |  9 ++-
 flaml/searcher/cfo_cat.py            | 31 +++++++++
 flaml/searcher/flow2.py              | 77 ++++++++++++++++------
 flaml/searcher/search_thread.py      | 10 +--
 notebook/flaml_autovw.ipynb          | 98 +++++++++++++++++++---------
 setup.py                             |  2 +-
 10 files changed, 177 insertions(+), 74 deletions(-)
 create mode 100644 flaml/searcher/cfo_cat.py

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 7cef0a948..bf7e25d60 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -16,7 +16,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-2019]
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.6, 3.7, 3.8, 3.9]
 
     steps:
       - uses: actions/checkout@v2
@@ -39,9 +39,10 @@ jobs:
           python -m pip install --upgrade pip
           pip install -e .[test]
       - name: If linux or mac, install ray
-        if: matrix.os == 'macOS-latest' || matrix.os == 'ubuntu-latest'
+        if: (matrix.os == 'macOS-latest' || matrix.os == 'ubuntu-latest') && matrix.python-version != '3.9'
         run: |
           pip install -e .[ray]
+          pip install 'tensorboardX<=2.2'
       - name: Lint with flake8
         run: |
           # stop the build if there are Python syntax errors or undefined names
diff --git a/README.md b/README.md
index e5e84d48f..857f1816d 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 [![PyPI version](https://badge.fury.io/py/FLAML.svg)](https://badge.fury.io/py/FLAML)
 [![Build](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml/badge.svg)](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml)
-![Python Version](https://img.shields.io/badge/3.6%20%7C%203.7%20%7C%203.8-blue)
+![Python Version](https://img.shields.io/badge/3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)
 [![Downloads](https://pepy.tech/badge/flaml/month)](https://pepy.tech/project/flaml)
 [![Join the chat at https://gitter.im/FLAMLer/community](https://badges.gitter.im/FLAMLer/community.svg)](https://gitter.im/FLAMLer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
diff --git a/flaml/automl.py b/flaml/automl.py
index abf189d4c..359781091 100644
--- a/flaml/automl.py
+++ b/flaml/automl.py
@@ -981,15 +981,9 @@ class AutoML:
         self._retrained_config = {}
         est_retrain_time = next_trial_time = 0
         best_config_sig = None
-        # use ConcurrencyLimiter to limit the amount of concurrency when
-        # using a search algorithm
         better = True  # whether we find a better model in one trial
         if self._ensemble:
             self.best_model = {}
-        try:
-            from ray.tune.suggest import ConcurrencyLimiter
-        except ImportError:
-            from .searcher.suggestion import ConcurrencyLimiter
         if self._hpo_method in ('cfo', 'grid'):
             from flaml import CFO as SearchAlgo
         elif 'optuna' == self._hpo_method:
@@ -1062,12 +1056,11 @@ class AutoML:
                         metric='val_loss', mode='min', space=search_space,
                         points_to_evaluate=points_to_evaluate,
                     )
-                search_state.search_alg = ConcurrencyLimiter(algo,
-                                                             max_concurrent=1)
+                search_state.search_alg = algo
             else:
                 search_space = None
                 if self._hpo_method in ('bs', 'cfo'):
-                    search_state.search_alg.searcher.set_search_properties(
+                    search_state.search_alg.set_search_properties(
                         config={
                             'metric_target': self._state.best_loss,
                         },
diff --git a/flaml/model.py b/flaml/model.py
index 57fd34ca9..1363816cc 100644
--- a/flaml/model.py
+++ b/flaml/model.py
@@ -67,6 +67,12 @@ class BaseEstimator:
         '''
         return self._model
 
+    @property
+    def estimator(self):
+        '''Trained model after fit() is called, or None before fit() is called
+        '''
+        return self._model
+
     def _preprocess(self, X):
         return X
 
diff --git a/flaml/searcher/blendsearch.py b/flaml/searcher/blendsearch.py
index 04d3b2bfb..fd9f35a3d 100644
--- a/flaml/searcher/blendsearch.py
+++ b/flaml/searcher/blendsearch.py
@@ -17,7 +17,7 @@ except ImportError:
     from .suggestion import OptunaSearch as GlobalSearch
     from .variant_generator import generate_variants
 from .search_thread import SearchThread
-from .flow2 import FLOW2 as LocalSearch
+from .flow2 import FLOW2
 
 import logging
 logger = logging.getLogger(__name__)
@@ -30,6 +30,7 @@ class BlendSearch(Searcher):
     cost_attr = "time_total_s"  # cost attribute in result
     lagrange = '_lagrange'      # suffix for lagrange-modified metric
     penalty = 1e+10             # penalty term for constraints
+    LocalSearch = FLOW2
 
     def __init__(self,
                  metric: Optional[str] = None,
@@ -131,7 +132,7 @@ class BlendSearch(Searcher):
                 self._gs = GlobalSearch(space=space, metric=metric, mode=mode)
         else:
             self._gs = None
-        self._ls = LocalSearch(
+        self._ls = self.LocalSearch(
             init_config, metric, mode, cat_hp_cost, space,
             prune_attr, min_resource, max_resource, reduction_factor, seed)
         self._init_search()
@@ -277,7 +278,9 @@ class BlendSearch(Searcher):
                     self._search_thread_pool[self._thread_count] = SearchThread(
                         self._ls.mode,
                         self._ls.create(
-                            config, objective, cost=result[self.cost_attr])
+                            config, objective,
+                            cost=result.get(self.cost_attr, 1)),
+                        self.cost_attr
                     )
                     thread_id = self._thread_count
                     self._thread_count += 1
diff --git a/flaml/searcher/cfo_cat.py b/flaml/searcher/cfo_cat.py
new file mode 100644
index 000000000..a6f884211
--- /dev/null
+++ b/flaml/searcher/cfo_cat.py
@@ -0,0 +1,31 @@
+'''!
+ * Copyright (c) 2021 Microsoft Corporation. All rights reserved.
+ * Licensed under the MIT License. See LICENSE file in the
+ * project root for license information.
+'''
+from .flow2 import FLOW2
+from .blendsearch import CFO
+
+
+class FLOW2Cat(FLOW2):
+    '''Local search algorithm optimized for categorical variables
+    '''
+
+    def _init_search(self):
+        super()._init_search()
+        self.step_ub = 1
+        self.step = self.STEPSIZE * self.step_ub
+        lb = self.step_lower_bound
+        if lb > self.step:
+            self.step = lb * 2
+        # upper bound
+        if self.step > self.step_ub:
+            self.step = self.step_ub
+        self._trunc = self.dim
+
+
+class CFOCat(CFO):
+    '''CFO optimized for categorical variables
+    '''
+
+    LocalSearch = FLOW2Cat
diff --git a/flaml/searcher/flow2.py b/flaml/searcher/flow2.py
index 60fce8d11..ad7a9d9cc 100644
--- a/flaml/searcher/flow2.py
+++ b/flaml/searcher/flow2.py
@@ -172,12 +172,12 @@ class FLOW2(Searcher):
         self._num_complete4incumbent = self._cost_complete4incumbent = 0
         self._num_allowed4incumbent = 2 * self.dim
         self._proposed_by = {}  # trial_id: int -> incumbent: Dict
-        self.step = self.STEPSIZE * np.sqrt(self.dim)
+        self.step_ub = np.sqrt(self.dim)
+        self.step = self.STEPSIZE * self.step_ub
         lb = self.step_lower_bound
         if lb > self.step:
             self.step = lb * 2
         # upper bound
-        self.step_ub = np.sqrt(self.dim)
         if self.step > self.step_ub:
             self.step = self.step_ub
         # maximal # consecutive no improvements
@@ -189,8 +189,11 @@ class FLOW2(Searcher):
         self._reset_times = 0
         # record intermediate trial cost
         self._trial_cost = {}
-        self._same = False  # whether the proposedd config is the same as best_config
-        self._init_phrase = True  # initial phase to increase initial stepsize
+        self._same = False  # whether the proposed config is the same as best_config
+        self._init_phase = True  # initial phase to increase initial stepsize
+        self._trunc = 0
+        # no truncation by default. when > 0, it means how many
+        # non-zero dimensions to keep in the random unit vector
 
     @property
     def step_lower_bound(self) -> float:
@@ -215,7 +218,7 @@ class FLOW2(Searcher):
         if np.isinf(step_lb):
             step_lb = self.STEP_LOWER_BOUND
         else:
-            step_lb *= np.sqrt(self.dim)
+            step_lb *= self.step_ub
         return step_lb
 
     @property
@@ -285,12 +288,14 @@ class FLOW2(Searcher):
         return unflatten_dict(config)
 
     def create(self, init_config: Dict, obj: float, cost: float) -> Searcher:
-        flow2 = FLOW2(init_config, self.metric, self.mode, self._cat_hp_cost,
-                      unflatten_dict(self.space), self.prune_attr,
-                      self.min_resource, self.max_resource,
-                      self.resource_multiple_factor, self._seed + 1)
+        flow2 = self.__class__(
+            init_config, self.metric, self.mode, self._cat_hp_cost,
+            unflatten_dict(self.space), self.prune_attr,
+            self.min_resource, self.max_resource,
+            self.resource_multiple_factor, self._seed + 1)
         flow2.best_obj = obj * self.metric_op  # minimize internally
         flow2.cost_incumbent = cost
+        self._seed += 1
         return flow2
 
     def normalize(self, config) -> Dict:
@@ -315,10 +320,11 @@ class FLOW2(Searcher):
                         elif key in self.incumbent:
                             config_norm[key] = self.incumbent[
                                 key] if value == self.best_config[
-                                    key] else (self.incumbent[
-                                        key] + 1) % self._unordered_cat_hp[key]
+                                    key] else (
+                                        self.incumbent[key]
+                                        + 1.0 / self._unordered_cat_hp[key]) % 1
                         else:
-                            config_norm[key] = 0
+                            config_norm[key] = 0.5
                         continue
                     # Uniform/LogUniform/Normal/Base
                     sampler = domain.get_sampler()
@@ -365,7 +371,8 @@ class FLOW2(Searcher):
                             config_denorm[key] = l[min(n - 1, int(np.floor(value * n)))]
                         else:
                             assert key in self.incumbent
-                            if round(value) == self.incumbent[key]:
+                            n = self._unordered_cat_hp[key]
+                            if np.floor(value * n) == np.floor(self.incumbent[key] * n):
                                 config_denorm[key] = self.best_config[key]
                             else:  # ****random value each time!****
                                 config_denorm[key] = self._random.choice(
@@ -448,7 +455,11 @@ class FLOW2(Searcher):
                     if self.step > self.step_ub:
                         self.step = self.step_ub
                     self._iter_best_config = self.trial_count_complete
+                    if self._trunc:
+                        self._trunc = min(self._trunc + 1, self.dim)
                     return
+                elif self._trunc:
+                    self._trunc = max(self._trunc >> 1, 1)
         proposed_by = self._proposed_by.get(trial_id)
         if proposed_by == self.incumbent:
             # proposed by current incumbent and no better
@@ -494,8 +505,10 @@ class FLOW2(Searcher):
             # record the cost in case it is pruned and cost info is lost
             self._trial_cost[trial_id] = cost
 
-    def rand_vector_unit_sphere(self, dim) -> np.ndarray:
+    def rand_vector_unit_sphere(self, dim, trunc=0) -> np.ndarray:
         vec = self._random.normal(0, 1, dim)
+        if 0 < trunc < dim:
+            vec[np.abs(vec).argsort()[:dim - trunc]] = 0
         mag = np.linalg.norm(vec)
         return vec / mag
 
@@ -532,7 +545,7 @@ class FLOW2(Searcher):
         else:
             # propose a new direction
             self._direction_tried = self.rand_vector_unit_sphere(
-                self.dim) * self.step
+                self.dim, self._trunc) * self.step
             for i, key in enumerate(self._tunable_keys):
                 move[key] += self._direction_tried[i]
         self._project(move)
@@ -540,13 +553,14 @@ class FLOW2(Searcher):
         self._proposed_by[trial_id] = self.incumbent
         self._configs[trial_id] = (config, self.step)
         self._num_proposedby_incumbent += 1
-        if self._init_phrase:
+        best_config = flatten_dict(self.best_config)
+        if self._init_phase:
             if self._direction_tried is None:
                 if self._same:
-                    # check if the new config is different from self.best_config
+                    # check if the new config is different from best_config
                     same = True
                     for key, value in config.items():
-                        if key not in self.best_config or value != self.best_config[key]:
+                        if key not in best_config or value != best_config[key]:
                             same = False
                             break
                     if same:
@@ -555,10 +569,10 @@ class FLOW2(Searcher):
                         if self.step > self.step_ub:
                             self.step = self.step_ub
             else:
-                # check if the new config is different from self.best_config
+                # check if the new config is different from best_config
                 same = True
                 for key, value in config.items():
-                    if key not in self.best_config or value != self.best_config[key]:
+                    if key not in best_config or value != best_config[key]:
                         same = False
                         break
                 self._same = same
@@ -566,7 +580,7 @@ class FLOW2(Searcher):
                 not self._resource or self._resource == self.max_resource):
             # check stuck condition if using max resource
             self._num_proposedby_incumbent -= 2
-            self._init_phrase = False
+            self._init_phase = False
             if self.step >= self.step_lower_bound:
                 # decrease step size
                 self._oldK = self._K if self._K else self._iter_best_config
@@ -574,6 +588,27 @@ class FLOW2(Searcher):
                 self.step *= np.sqrt(self._oldK / self._K)
             else:
                 return None
+        if self._init_phase:
+            return unflatten_dict(config)
+        if self._trunc == 1 and self._direction_tried is not None:
+            # random
+            for i, key in enumerate(self._tunable_keys):
+                if self._direction_tried[i] != 0:
+                    for _, generated in generate_variants({'config': {
+                        key: self.space[key]
+                    }}):
+                        if generated['config'][key] != best_config[key]:
+                            config[key] = generated['config'][key]
+                            return unflatten_dict(config)
+                        break
+        else:
+            # check if config == best_config
+            if len(config) == len(best_config):
+                for key, value in best_config.items():
+                    if value != config[key]:
+                        return unflatten_dict(config)
+                # print('move to', move)
+                self.incumbent = move
         return unflatten_dict(config)
 
     def _project(self, config):
diff --git a/flaml/searcher/search_thread.py b/flaml/searcher/search_thread.py
index 48f44394c..8640e6132 100644
--- a/flaml/searcher/search_thread.py
+++ b/flaml/searcher/search_thread.py
@@ -19,11 +19,11 @@ class SearchThread:
     '''Class of global or local search thread
     '''
 
-    cost_attr = 'time_total_s'
     _eps = 1.0
 
     def __init__(self, mode: str = "min",
-                 search_alg: Optional[Searcher] = None):
+                 search_alg: Optional[Searcher] = None,
+                 cost_attr: Optional[str] = 'time_total_s'):
         ''' When search_alg is omitted, use local search FLOW2
         '''
         self._search_alg = search_alg
@@ -40,6 +40,7 @@ class SearchThread:
         self.priority = self.speed = 0
         self._init_config = True
         self.running = 0    # the number of running trials from the thread
+        self.cost_attr = cost_attr
 
     @classmethod
     def set_eps(cls, time_budget_s):
@@ -108,9 +109,8 @@ class SearchThread:
                 # under this thread
                 self._init_config = False
         if result:
-            if self.cost_attr in result:
-                self.cost_last = result[self.cost_attr]
-                self.cost_total += self.cost_last
+            self.cost_last = result.get(self.cost_attr, 1)
+            self.cost_total += self.cost_last
             if self._search_alg.metric in result:
                 obj = result[self._search_alg.metric] * self._metric_op
                 if obj < self.obj_best1:
diff --git a/notebook/flaml_autovw.ipynb b/notebook/flaml_autovw.ipynb
index 508628e93..f9a1bb91d 100644
--- a/notebook/flaml_autovw.ipynb
+++ b/notebook/flaml_autovw.ipynb
@@ -64,7 +64,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "(36203, 17) (36203,)\n"
+     "text": [
+      "(36203, 17) (36203,)\n"
+     ]
     }
    ],
    "source": [
@@ -88,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "tags": []
    },
@@ -96,7 +98,9 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "openml example: 8.170000076293945 [1.0000e+01 7.0000e+00 3.0000e+00 4.0000e+00        nan 6.3300e+00\n 1.3600e-01 7.3300e+00 7.0100e+00 6.9800e+00 3.0000e-03 7.0000e+00\n 9.7000e+00 1.2300e+01 1.0217e+03 0.0000e+00 5.8000e+01]\nvw 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"
+     "text": [
+      "openml example: 8.170000076293945 [1.0000e+01 7.0000e+00 3.0000e+00 4.0000e+00        nan 6.3300e+00\n 1.3600e-01 7.3300e+00 7.0100e+00 6.9800e+00 3.0000e-03 7.0000e+00\n 9.7000e+00 1.2300e+01 1.0217e+03 0.0000e+00 5.8000e+01]\nvw 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"
+     ]
     }
    ],
    "source": [
@@ -139,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +153,6 @@
     "    \"\"\"\n",
     "    print('Online learning for', iter_num, 'steps...')\n",
     "    loss_list = []\n",
-    "    y_predict_list = []\n",
     "    for i in range(iter_num):\n",
     "        vw_x = vw_examples[i]\n",
     "        y_true = float(vw_examples[i].split('|')[0])\n",
@@ -160,7 +163,6 @@
     "        # calculate one step loss\n",
     "        loss = mean_squared_error([y_pred], [y_true])\n",
     "        loss_list.append(loss)\n",
-    "        y_predict_list.append([y_pred, y_true])\n",
     "    return loss_list\n",
     "\n",
     "max_iter_num = 10000  # or len(vw_examples)"
@@ -176,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "tags": []
    },
@@ -184,13 +186,16 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Online learning for 10000 steps...\nFinal progressive validation loss of vanilla vw: 15.180878192648041\n"
+     "text": [
+      "Online learning for 10000 steps...\n",
+      "Final progressive validation loss of vanilla vw: 15.18087237487917\n"
+     ]
     }
    ],
    "source": [
     "from vowpalwabbit import pyvw\n",
     "''' create a vanilla vw instance '''\n",
-    "vanilla_vw = pyvw.vw()\n",
+    "vanilla_vw = pyvw.vw('--quiet')\n",
     "\n",
     "# online learning with vanilla VW\n",
     "loss_list_vanilla = online_learning_loop(max_iter_num, vw_examples, vanilla_vw)\n",
@@ -207,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -218,7 +223,16 @@
     {
      "output_type": "stream",
      "name": "stderr",
-     "text": "Seed namespaces (singletons and interactions): ['e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nCreated challengers from champion |\nNew challenger size 37, ['|ah', '|ch', '|df', '|ef', '|ag', '|bg', '|be', '|eh', '|hi', '|cd', '|ci', '|eg', '|bh', '|ad', '|bi', '|ab', '|cg', '|bc', '|gi', '|ai', '|cf', '|ei', '|dg', '|ac', '|af', '|ce', '|ae', '|de', '|fi', '|bd', '|gh', '|bf', '|dh', '|di', '|fh', '|fg', '|']\nOnline learning for 10000 steps...\nSeed namespaces (singletons and interactions): ['dh', 'e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nCreated challengers from champion |dh\nNew challenger size 43, ['|dh_ei', '|bd_dh', '|cdh_dh', '|ac_dh', '|bh_dh', '|ab_dh', '|dh_gi', '|cg_dh', '|bf_dh', '|dh_dhi', '|deh_dh', '|dh_fi', '|ad_dh', '|dh_hi', '|dh_eg', '|bdh_dh', '|dh_eh', '|ag_dh', '|dh', '|de_dh', '|dgh_dh', '|bc_dh', '|cd_dh', '|dh_ef', '|cf_dh', '|dh_di', '|bi_dh', '|ah_dh', '|dh_fh', '|ce_dh', '|ae_dh', '|adh_dh', '|df_dh', '|ch_dh', '|dh_fg', '|ai_dh', '|ci_dh', '|dh_gh', '|dfh_dh', '|af_dh', '|dg_dh', '|be_dh', '|bg_dh']\nFinal progressive validation loss of autovw: 10.744201540966063\n"
+     "text": [
+      "Seed namespaces (singletons and interactions): ['g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
+      "Created challengers from champion ||\n",
+      "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",
+      "Online learning for 10000 steps...\n",
+      "Seed namespaces (singletons and interactions): ['ce', 'g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
+      "Created challengers from champion |ce|\n",
+      "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",
+      "Final progressive validation loss of autovw: 8.718817421944529\n"
+     ]
     }
    ],
    "source": [
@@ -226,7 +240,9 @@
     "from flaml import AutoVW\n",
     "\n",
     "'''create an AutoVW instance for tuning namespace interactions'''\n",
-    "autovw_ni = AutoVW(max_live_model_num=5, search_space={'interactions': AutoVW.AUTOMATIC})\n",
+    "# configure both hyperparamters to tune, e.g., 'interactions', and fixed arguments about the online learner,\n",
+    "# e.g., 'quiet' in the search_space argument.\n",
+    "autovw_ni = AutoVW(max_live_model_num=5, search_space={'interactions': AutoVW.AUTOMATIC, 'quiet': ''})\n",
     "\n",
     "# online learning with AutoVW\n",
     "loss_list_autovw_ni = online_learning_loop(max_iter_num, vw_examples, autovw_ni)\n",
@@ -242,15 +258,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "output_type": "display_data",
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131.712457 229.040651 \nL 131.834349 228.882241 \nL 131.874979 228.947694 \nL 131.91561 228.934325 \nL 132.037502 228.699723 \nL 132.078132 228.716288 \nL 132.64696 229.400584 \nL 132.768852 229.244273 \nL 132.809483 229.310362 \nL 132.890744 229.22663 \nL 133.012636 229.135268 \nL 133.540833 229.799778 \nL 133.703355 229.760614 \nL 133.987769 229.399182 \nL 134.06903 229.503201 \nL 134.434706 229.991288 \nL 134.597228 230.162828 \nL 134.637858 230.11541 \nL 134.75975 230.027125 \nL 134.800381 230.081951 \nL 134.841011 230.104768 \nL 135.003534 229.776485 \nL 135.572362 230.399369 \nL 135.897406 229.985878 \nL 136.384973 230.527609 \nL 136.466234 230.56763 \nL 136.750648 230.246853 \nL 136.87254 230.259556 \nL 137.400738 230.900585 \nL 137.481999 230.78976 \nL 137.725782 230.499716 \nL 137.847674 230.404137 \nL 137.888304 230.460318 \nL 138.416502 231.067652 \nL 138.538394 230.920616 \nL 138.700916 230.855437 \nL 138.863438 230.831192 \nL 139.351005 231.388483 \nL 139.472897 231.317379 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232.697372 \nL 99.858086 231.351553 \nL 99.939348 231.414889 \nL 100.061239 231.558775 \nL 100.548806 232.75336 \nL 100.670698 232.95024 \nL 100.711328 232.876622 \nL 100.83322 231.949361 \nL 100.914481 232.050946 \nL 101.239526 232.897728 \nL 101.442679 233.285428 \nL 101.645832 233.044366 \nL 101.767723 232.073284 \nL 101.808354 232.097229 \nL 102.336551 233.260661 \nL 102.377182 233.221876 \nL 102.742857 232.543964 \nL 102.824118 232.741388 \nL 103.311685 233.826712 \nL 103.352316 233.763238 \nL 103.392946 233.775335 \nL 103.555469 233.615361 \nL 103.717991 233.257659 \nL 103.758622 233.348255 \nL 104.246188 234.48375 \nL 104.36808 234.461843 \nL 104.489972 234.188864 \nL 104.530603 234.24053 \nL 104.652494 233.997974 \nL 104.693125 234.063203 \nL 105.261953 235.276398 \nL 105.343214 235.226661 \nL 105.465106 235.04106 \nL 105.505736 235.084777 \nL 105.546367 234.998103 \nL 105.668259 234.895618 \nL 105.708889 234.927339 \nL 106.196456 235.969814 \nL 106.318348 236.077572 \nL 106.358978 236.004115 \nL 106.44024 235.798051 \nL 106.48087 235.824232 \nL 106.521501 235.913065 \nL 106.562131 235.814265 \nL 106.684023 235.451218 \nL 106.724654 235.548739 \nL 107.293482 236.716912 \nL 107.456004 236.520539 \nL 107.537265 236.611654 \nL 107.659157 236.246337 \nL 107.699787 236.347313 \nL 108.227985 237.448799 \nL 108.309246 237.348679 \nL 108.349877 237.278388 \nL 108.390507 237.31778 \nL 108.431138 237.364204 \nL 108.471768 237.314847 \nL 108.59366 236.937017 \nL 108.634291 237.003634 \nL 109.121857 237.961045 \nL 109.162488 237.997309 \nL 109.203119 237.930725 \nL 109.528163 237.472389 \nL 109.568794 237.545023 \nL 110.01573 238.417066 \nL 110.137622 238.438454 \nL 110.340775 237.591046 \nL 110.422036 237.682433 \nL 110.543928 237.753876 \nL 111.072125 238.676137 \nL 111.112756 238.644585 \nL 111.234647 238.435682 \nL 111.39717 238.241484 \nL 111.519061 238.097519 \nL 112.087889 239.085996 \nL 112.209781 238.763093 \nL 112.372303 238.54064 \nL 112.494195 238.345176 \nL 112.534826 238.420305 \nL 113.022393 239.190833 \nL 113.144284 239.100993 \nL 113.306807 238.637312 \nL 113.347437 238.724407 \nL 113.428698 238.486298 \nL 113.469329 238.300969 \nL 113.55059 238.399351 \nL 114.119418 239.353549 \nL 114.28194 239.070165 \nL 114.322571 239.161029 \nL 114.363202 239.128645 \nL 114.485093 238.726435 \nL 114.525724 238.766253 \nL 115.053921 239.651438 \nL 115.419597 239.27344 \nL 115.66338 239.685052 \nL 115.988425 240.182887 \nL 116.272839 239.706801 \nL 116.3541 239.649801 \nL 116.39473 239.685976 \nL 116.963558 240.557109 \nL 117.288603 240.0595 \nL 117.369864 240.068825 \nL 118.019953 240.914551 \nL 118.223106 240.683759 \nL 118.344998 240.574713 \nL 118.385629 240.653999 \nL 118.873195 241.451797 \nL 118.995087 241.457367 \nL 119.076348 241.314561 \nL 119.157609 241.375508 \nL 119.401393 241.356533 \nL 119.92959 242.082813 \nL 120.010852 241.957848 \nL 120.092113 241.783219 \nL 120.132743 241.80883 \nL 120.173374 241.858869 \nL 120.335896 241.424801 \nL 120.376527 241.483827 \nL 120.945355 242.436261 \nL 121.107877 242.313125 \nL 121.189138 242.374012 \nL 121.31103 242.055261 \nL 121.351661 242.094545 \nL 121.920489 242.9946 \nL 122.245533 242.600313 \nL 122.286164 242.63124 \nL 122.773731 243.359084 \nL 122.895622 243.205656 \nL 123.139406 242.971672 \nL 123.220667 242.943588 \nL 123.261298 243.007571 \nL 123.789495 243.873089 \nL 123.830126 243.843834 \nL 123.911387 243.757797 \nL 123.952017 243.806263 \nL 124.073909 243.89517 \nL 124.15517 243.772565 \nL 124.195801 243.797864 \nL 124.723998 244.624874 \nL 124.764629 244.597321 \nL 125.170935 244.334479 \nL 125.211565 244.393049 \nL 125.699132 245.108987 \nL 125.861654 244.97763 \nL 126.105438 244.730857 \nL 126.227329 244.851598 \nL 126.755527 245.543933 \nL 126.877419 245.428412 \nL 126.918049 245.374686 \nL 126.95868 245.413565 \nL 126.99931 245.47298 \nL 127.161833 244.946871 \nL 127.202463 244.994356 \nL 127.771291 245.77706 \nL 127.811922 245.731104 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248.498357 \nL 136.710018 248.535164 \nL 136.913171 248.642024 \nL 137.441368 249.231553 \nL 137.685152 248.969549 \nL 137.847674 248.860728 \nL 138.457133 249.543642 \nL 138.904069 249.476995 \nL 139.432266 249.974422 \nL 139.838572 249.842536 \nL 140.4074 250.497132 \nL 140.529292 250.422349 \nL 140.610553 250.417975 \nL 140.651184 250.476946 \nL 140.691814 250.417466 \nL 140.813706 250.212915 \nL 140.854336 250.26076 \nL 141.463795 250.945633 \nL 141.951362 250.80002 \nL 142.398298 251.346228 \nL 142.438929 251.312878 \nL 142.642082 251.014013 \nL 142.723343 250.637348 \nL 142.804604 250.704902 \nL 143.495324 251.103293 \nL 143.617216 251.024721 \nL 143.698477 250.972725 \nL 143.739107 251.012673 \nL 144.267305 251.543676 \nL 144.754872 251.359156 \nL 145.3237 251.956062 \nL 145.486222 251.935706 \nL 145.689375 251.884666 \nL 146.298833 252.51847 \nL 146.420725 252.385006 \nL 146.461356 252.434861 \nL 146.74577 252.397363 \nL 147.273967 252.967655 \nL 147.314598 252.929099 \nL 147.680273 252.442548 \nL 147.720904 252.483013 \nL 148.452254 252.972842 \nL 148.533515 252.860896 \nL 148.696037 252.236477 \nL 148.736668 252.289716 \nL 149.224235 252.836609 \nL 149.305496 252.762152 \nL 149.508649 252.547488 \nL 149.630541 252.15129 \nL 149.671171 252.184164 \nL 150.158738 252.684275 \nL 150.199369 252.646067 \nL 150.565044 252.191439 \nL 150.605674 252.239402 \nL 151.093241 252.710829 \nL 151.662069 252.332614 \nL 152.149636 252.718576 \nL 152.190267 252.638938 \nL 152.271528 252.548268 \nL 152.312159 252.599358 \nL 152.39342 252.634749 \nL 152.515311 252.375625 \nL 152.555942 252.425443 \nL 153.12477 252.938406 \nL 153.571706 252.644288 \nL 154.099904 253.095064 \nL 154.140534 253.043768 \nL 154.50621 252.609193 \nL 155.156299 253.123275 \nL 155.643866 253.042966 \nL 156.050171 253.372969 \nL 156.172063 253.266708 \nL 156.212694 253.313165 \nL 156.253324 253.346593 \nL 156.293955 253.247358 \nL 156.415847 252.986571 \nL 156.456477 253.018148 \nL 157.065936 253.36166 \nL 157.431611 253.253921 \nL 157.959808 253.691221 \nL 158.122331 253.538325 \nL 158.162961 253.582956 \nL 158.203592 253.585008 \nL 158.366114 253.283892 \nL 158.975573 253.870204 \nL 159.097465 253.77204 \nL 159.138095 253.819573 \nL 159.219356 253.803247 \nL 159.341248 253.712708 \nL 159.381879 253.743784 \nL 159.950707 254.233221 \nL 160.15386 254.137171 \nL 160.235121 254.105271 \nL 160.397643 253.958459 \nL 160.92584 254.398226 \nL 161.291516 254.081391 \nL 161.61656 254.399916 \nL 161.982235 254.593405 \nL 162.429172 254.546286 \nL 162.876108 254.922773 \nL 163.404306 254.850302 \nL 163.810611 255.154314 \nL 164.095025 255.119808 \nL 164.216917 255.144512 \nL 164.745114 255.615011 \nL 165.232681 255.484652 \nL 165.801509 255.889006 \nL 166.126554 255.851425 \nL 166.817274 256.326571 \nL 167.101688 256.253621 \nL 167.792408 256.747529 \nL 167.99556 256.720912 \nL 168.239344 256.695773 \nL 168.970694 257.117791 \nL 169.255108 257.114482 \nL 169.742675 257.468742 \nL 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263.061237 \nL 185.304185 263.446447 \nL 185.588599 263.398282 \nL 185.791752 263.527371 \nL 186.279319 263.830154 \nL 186.482472 263.810739 \nL 186.807517 264.014513 \nL 187.254453 264.308404 \nL 187.538867 264.303243 \nL 187.78265 264.463263 \nL 188.270217 264.784348 \nL 188.676523 264.707621 \nL 189.245351 265.079484 \nL 189.611026 265.053331 \nL 190.423638 265.463042 \nL 190.708052 265.353148 \nL 191.154988 265.137701 \nL 191.805077 265.342653 \nL 192.170753 265.496826 \nL 192.577058 265.52858 \nL 192.820842 265.697369 \nL 193.145886 265.87055 \nL 193.470931 265.88689 \nL 194.12102 266.319115 \nL 194.446065 266.340289 \nL 195.136784 266.773459 \nL 195.339937 266.717843 \nL 195.421199 266.619476 \nL 195.461829 266.640497 \nL 196.23381 267.094225 \nL 196.315071 267.022135 \nL 196.477593 266.922663 \nL 197.290205 267.258023 \nL 197.574619 267.411185 \nL 197.980925 267.606782 \nL 198.143447 267.594976 \nL 198.38723 267.672788 \nL 198.915428 267.961084 \nL 199.281103 267.897689 \nL 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272.879223 \nL 215.533333 273.241507 \nL 215.736486 273.207041 \nL 216.0209 273.206093 \nL 216.549097 273.481396 \nL 216.914773 273.45896 \nL 217.646123 273.844815 \nL 218.214951 273.844325 \nL 218.580626 273.970735 \nL 218.783779 273.84933 \nL 222.603053 275.250154 \nL 222.765575 275.199307 \nL 223.415665 275.509352 \nL 223.903231 275.607444 \nL 224.47206 275.82214 \nL 224.797104 275.889518 \nL 225.24404 276.065283 \nL 225.853499 276.183459 \nL 226.462958 276.31327 \nL 226.666111 276.358656 \nL 227.234939 276.629714 \nL 227.519353 276.55888 \nL 228.331964 276.889438 \nL 228.73827 276.998309 \nL 229.225837 277.165808 \nL 229.550881 277.133135 \nL 230.16034 277.424185 \nL 230.607276 277.467337 \nL 231.338627 277.819467 \nL 231.704302 277.883764 \nL 232.232499 278.074499 \nL 232.476283 278.0053 \nL 233.207633 278.356265 \nL 233.451417 278.315616 \nL 234.182767 278.672129 \nL 234.42655 278.638015 \nL 235.157901 278.999142 \nL 235.442315 279.004165 \nL 236.092404 279.298384 \nL 236.53934 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206.230873 \nL 98.801691 205.453705 \nL 98.842322 205.390101 \nL 98.882953 205.480279 \nL 98.964214 205.692852 \nL 99.004844 205.581585 \nL 99.045475 205.468625 \nL 99.086105 205.53155 \nL 99.695564 206.790481 \nL 99.898717 205.373684 \nL 99.939348 205.412865 \nL 100.061239 205.515049 \nL 100.467545 206.645483 \nL 100.548806 206.600978 \nL 100.670698 206.771637 \nL 100.711328 206.685942 \nL 100.83322 205.687106 \nL 100.914481 205.757178 \nL 101.158265 206.309392 \nL 101.442679 206.860036 \nL 101.645832 206.569547 \nL 101.767723 205.52149 \nL 101.808354 205.530954 \nL 102.336551 206.554105 \nL 102.377182 206.507731 \nL 102.742857 205.719518 \nL 102.824118 205.894716 \nL 103.311685 206.867716 \nL 103.392946 206.804838 \nL 103.555469 206.621929 \nL 103.717991 206.192489 \nL 103.758622 206.271987 \nL 104.246188 207.324221 \nL 104.36808 207.283968 \nL 104.611864 206.772518 \nL 104.652494 206.732994 \nL 104.693125 206.785198 \nL 105.261953 207.878857 \nL 105.343214 207.815453 \nL 105.465106 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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 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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 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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 217.727384 230.778388 \nL 217.889906 230.669841 \nL 218.255582 230.767781 \nL 218.621257 230.787239 \nL 218.86504 230.732415 \nL 219.474499 231.004005 \nL 219.921435 230.997033 \nL 220.449633 231.214186 \nL 220.774677 231.190069 \nL 221.506028 231.521659 \nL 221.790442 231.507838 \nL 222.3999 231.784032 \nL 222.806206 231.715802 \nL 223.375034 231.970856 \nL 223.78134 231.935309 \nL 224.431429 232.218233 \nL 224.715843 232.168691 \nL 225.20341 232.385376 \nL 225.650346 232.326488 \nL 226.178544 232.54526 \nL 226.584849 232.50507 \nL 227.072416 232.734067 \nL 227.275569 232.742557 \nL 227.559983 232.689109 \nL 228.169442 232.94414 \nL 228.941423 233.127572 \nL 229.225837 233.155914 \nL 229.550881 233.090982 \nL 230.16034 233.344475 \nL 230.566646 233.305958 \nL 231.297996 233.629618 \nL 231.460518 233.558095 \nL 231.623041 233.616949 \nL 232.151238 233.811845 \nL 232.557544 233.762842 \nL 235.889251 234.694667 \nL 236.173665 234.734494 \nL 236.295557 234.698882 \nL 236.417449 234.692336 \nL 237.067538 234.940698 \nL 237.555105 235.01224 \nL 238.123933 235.185855 \nL 238.327086 235.095114 \nL 238.895914 235.272245 \nL 239.261589 235.241218 \nL 239.830417 235.465527 \nL 240.317984 235.487311 \nL 241.130595 235.635494 \nL 241.293118 235.681556 \nL 241.943207 235.885631 \nL 242.065098 235.79996 \nL 242.227621 235.803336 \nL 242.796449 235.993419 \nL 243.040232 235.971502 \nL 243.243385 235.90049 \nL 243.812213 236.109298 \nL 244.381041 236.118552 \nL 244.9905 236.177295 \nL 245.153022 236.158801 \nL 245.803111 236.312054 \nL 246.900137 236.555805 \nL 247.062659 236.520116 \nL 247.712748 236.766426 \nL 248.119054 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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -286,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "tags": []
    },
@@ -294,14 +310,25 @@
     {
      "output_type": "stream",
      "name": "stderr",
-     "text": "Seed namespaces (singletons and interactions): ['e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nNo low-cost init config given to the search algorithm.For cost-frugal search, consider providing init values for cost-related hps via 'init_config'.\nCreated challengers from champion ||0.5\nNew challenger size 39, ['|dh|0.5', '|ci|0.5', '|bd|0.5', '|bh|0.5', '|ei|0.5', '|ch|0.5', '|bg|0.5', '|bc|0.5', '|cd|0.5', '|ag|0.5', '|eh|0.5', '|hi|0.5', '|dg|0.5', '|fi|0.5', '|ad|0.5', '|cf|0.5', '|ce|0.5', '|be|0.5', '|ab|0.5', '|ah|0.5', '|fh|0.5', '|di|0.5', '|gi|0.5', '|bf|0.5', '|de|0.5', '|ac|0.5', '|ai|0.5', '|df|0.5', '|cg|0.5', '|ae|0.5', '|fg|0.5', '|ef|0.5', '|eg|0.5', '|gh|0.5', '|af|0.5', '|bi|0.5', '||0.05358867312681484', '||1.0', '||0.5']\nOnline learning for 10000 steps...\nSeed namespaces (singletons and interactions): ['e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nNo low-cost init config given to the search algorithm.For cost-frugal search, consider providing init values for cost-related hps via 'init_config'.\nCreated challengers from champion ||1.0\nNew challenger size 38, ['|bf|1.0', '|ab|1.0', '|fg|1.0', '|bg|1.0', '|ad|1.0', '|fi|1.0', '|be|1.0', '|gi|1.0', '|df|1.0', '|de|1.0', '|cg|1.0', '|hi|1.0', '|di|1.0', '|ei|1.0', '|ai|1.0', '|bc|1.0', '|af|1.0', '|ef|1.0', '|ag|1.0', '|dh|1.0', '|fh|1.0', '|cd|1.0', '|dg|1.0', '|gh|1.0', '|ah|1.0', '|eg|1.0', '|ci|1.0', '|ch|1.0', '|eh|1.0', '|ac|1.0', '|ce|1.0', '|bi|1.0', '|bd|1.0', '|ae|1.0', '|cf|1.0', '|bh|1.0', '||0.10717734625362937', '||0.3273795141019504']\nSeed namespaces (singletons and interactions): ['de', 'e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nNo low-cost init config given to the search algorithm.For cost-frugal search, consider providing init values for cost-related hps via 'init_config'.\nCreated challengers from champion |de|1.0\nNew challenger size 45, ['|cf_de|1.0', '|ci_de|1.0', '|cd_de|1.0', '|ac_de|1.0', '|de_dh|1.0', '|ab_de|1.0', '|de_ef|1.0', '|ce_de|1.0', '|de_hi|1.0', '|bg_de|1.0', '|de_fi|1.0', '|ah_de|1.0', '|de_dg|1.0', '|de_fg|1.0', '|ai_de|1.0', '|de_gh|1.0', '|bh_de|1.0', '|ch_de|1.0', '|de|1.0', '|af_de|1.0', '|de_deg|1.0', '|de_eh|1.0', '|de_eg|1.0', '|de_di|1.0', '|de_ei|1.0', '|ag_de|1.0', '|ae_de|1.0', '|de_deh|1.0', '|be_de|1.0', '|de_fh|1.0', '|cg_de|1.0', '|bf_de|1.0', '|bi_de|1.0', '|ad_de|1.0', '|ade_de|1.0', '|de_def|1.0', '|bde_de|1.0', '|cde_de|1.0', '|de_df|1.0', '|bc_de|1.0', '|de_dei|1.0', '|bd_de|1.0', '|de_gi|1.0', '|de|0.10717734625362937', '|de|0.3273795141019504']\nFinal progressive validation loss of autovw_nilr: 6.271218842008241\n"
+     "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)}\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",
@@ -321,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {
     "tags": []
    },
@@ -330,8 +357,8 @@
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200.879361 \nL 107.537265 200.955335 \nL 107.659157 200.650721 \nL 107.699787 200.734918 \nL 108.227985 201.653378 \nL 108.471768 201.541684 \nL 108.59366 201.226635 \nL 108.634291 201.282182 \nL 109.121857 202.080507 \nL 109.162488 202.110746 \nL 109.203119 202.055226 \nL 109.528163 201.673048 \nL 109.568794 201.733613 \nL 110.056361 202.47592 \nL 110.096991 202.527443 \nL 110.137622 202.478588 \nL 110.340775 201.771988 \nL 110.422036 201.84819 \nL 110.543928 201.907762 \nL 111.072125 202.676778 \nL 111.112756 202.650468 \nL 111.234647 202.476277 \nL 111.39717 202.314348 \nL 111.519061 202.194304 \nL 111.559692 202.266256 \nL 112.047259 203.03345 \nL 112.169151 202.885737 \nL 112.372303 202.563795 \nL 112.494195 202.40081 \nL 112.534826 202.463456 \nL 113.022393 203.10595 \nL 113.144284 203.031038 \nL 113.306807 202.644403 \nL 113.347437 202.717027 \nL 113.428698 202.518483 \nL 113.469329 202.363948 \nL 113.55059 202.445983 \nL 114.119418 203.241628 \nL 114.28194 203.005332 \nL 114.322571 203.081098 \nL 114.363202 203.054096 \nL 114.485093 202.718718 \nL 114.525724 202.751919 \nL 115.053921 203.490019 \nL 115.419597 203.17483 \nL 115.704011 203.575019 \nL 115.988425 203.933161 \nL 116.029055 203.838166 \nL 116.313469 203.487262 \nL 116.435361 203.578577 \nL 116.922928 204.245016 \nL 117.004189 204.163791 \nL 117.207342 203.881577 \nL 117.451125 203.960063 \nL 117.979323 204.565446 \nL 118.060584 204.452844 \nL 118.223106 204.350807 \nL 118.344998 204.25988 \nL 118.385629 204.325992 \nL 118.913826 205.020387 \nL 119.238871 204.849119 \nL 119.320132 204.805772 \nL 119.360762 204.844187 \nL 119.88896 205.514388 \nL 120.010852 205.413189 \nL 120.335896 204.968715 \nL 120.945355 205.812107 \nL 121.067246 205.708273 \nL 121.229769 205.658424 \nL 121.31103 205.494416 \nL 121.351661 205.527172 \nL 121.920489 206.277671 \nL 122.286164 205.974688 \nL 122.773731 206.581591 \nL 122.895622 206.453658 \nL 123.139406 206.258553 \nL 123.261298 206.288486 \nL 123.830126 206.985793 \nL 123.952017 206.954465 \nL 124.073909 207.028599 \nL 124.195801 206.947462 \nL 124.764629 207.614079 \nL 125.211565 207.443749 \nL 125.699132 208.040725 \nL 126.024177 207.853119 \nL 126.146068 207.730842 \nL 126.186699 207.765771 \nL 126.714896 208.391938 \nL 126.836788 208.348865 \nL 126.95868 208.294693 \nL 126.99931 208.344236 \nL 127.161833 207.905546 \nL 127.202463 207.945141 \nL 127.771291 208.597789 \nL 127.811922 208.559469 \nL 128.177597 208.252376 \nL 128.218228 208.305047 \nL 128.746425 208.825259 \nL 128.868317 208.668013 \nL 128.908947 208.707644 \nL 128.990209 208.638705 \nL 129.233992 208.315322 \nL 129.315253 208.35322 \nL 129.680928 208.696414 \nL 130.127865 208.430304 \nL 130.656062 208.986259 \nL 130.696693 208.935033 \nL 130.981107 208.660827 \nL 131.062368 208.710438 \nL 131.631196 209.299173 \nL 131.753088 209.241256 \nL 131.834349 209.180656 \nL 131.874979 209.23411 \nL 131.956241 209.132533 \nL 132.078132 209.096724 \nL 132.64696 209.688713 \nL 132.768852 209.591049 \nL 132.809483 209.646062 \nL 132.890744 209.592091 \nL 133.012636 209.543199 \nL 133.581464 210.132977 \nL 133.703355 210.112358 \nL 134.06903 209.973947 \nL 134.841011 210.528499 \nL 135.003534 210.31606 \nL 135.572362 210.86183 \nL 135.897406 210.593452 \nL 136.506865 211.056153 \nL 136.547496 210.984292 \nL 136.669387 210.866906 \nL 136.710018 210.897597 \nL 136.913171 210.986701 \nL 137.441368 211.478272 \nL 137.685152 211.259804 \nL 137.847674 211.169064 \nL 138.457133 211.738503 \nL 138.904069 211.682931 \nL 139.432266 212.097704 \nL 139.838572 211.987733 \nL 140.4074 212.533559 \nL 140.691814 212.46713 \nL 140.813706 212.296568 \nL 140.854336 212.336463 \nL 141.463795 212.907535 \nL 141.951362 212.786117 \nL 142.438929 213.213758 \nL 142.642082 212.964553 \nL 142.723343 212.650476 \nL 142.804604 212.706805 \nL 143.535954 213.044293 \nL 143.94226 213.070428 \nL 144.307935 213.389302 \nL 144.714241 213.205524 \nL 145.3237 213.750069 \nL 145.567483 213.675912 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214.932265 \nL 157.309719 214.832544 \nL 157.431611 214.832271 \nL 157.959808 215.196907 \nL 158.162961 215.106632 \nL 158.203592 215.108344 \nL 158.366114 214.857262 \nL 158.975573 215.34615 \nL 159.138095 215.303933 \nL 159.219356 215.290319 \nL 159.381879 215.240736 \nL 159.991337 215.615706 \nL 160.397643 215.419741 \nL 160.92584 215.786434 \nL 161.332146 215.542435 \nL 161.941605 215.976541 \nL 162.30728 215.81818 \nL 163.079261 216.209682 \nL 163.241783 216.073714 \nL 163.769981 216.426041 \nL 164.216917 216.408715 \nL 164.785745 216.78541 \nL 165.192051 216.657632 \nL 165.760879 217.029384 \nL 166.289076 217.111899 \nL 166.817274 217.39436 \nL 167.101688 217.333532 \nL 167.792408 217.74537 \nL 168.117452 217.607236 \nL 168.808172 218.029018 \nL 169.255108 218.051349 \nL 169.742675 218.346744 \nL 170.027089 218.332188 \nL 170.839701 218.685905 \nL 171.286637 218.887545 \nL 171.814835 219.150906 \nL 172.058618 219.174222 \nL 172.586815 219.494225 \nL 173.236905 219.67734 \nL 173.846363 219.803336 \nL 174.008886 219.867963 \nL 174.537083 220.175802 \nL 174.658975 220.115078 \nL 174.902758 220.069041 \nL 175.02465 220.128032 \nL 175.593478 220.40177 \nL 176.040414 220.356597 \nL 176.568612 220.624932 \nL 176.934287 220.561198 \nL 177.503115 220.824633 \nL 177.665637 220.800131 \nL 178.518879 221.177336 \nL 178.843924 221.285177 \nL 179.575274 221.664768 \nL 180.550408 221.911449 \nL 180.753561 221.939043 \nL 181.36302 222.222066 \nL 181.647434 222.164014 \nL 181.769325 222.126118 \nL 182.419415 222.49506 \nL 182.78509 222.444952 \nL 183.353918 222.788091 \nL 183.597701 222.749122 \nL 183.760224 222.758673 \nL 184.288421 223.062423 \nL 184.654096 223.009972 \nL 185.304185 223.331175 \nL 185.588599 223.291013 \nL 185.832383 223.428534 \nL 186.279319 223.651123 \nL 186.482472 223.634935 \nL 186.848147 223.834592 \nL 187.416975 224.027464 \nL 187.78265 224.179033 \nL 188.310848 224.433316 \nL 188.676523 224.382788 \nL 189.285982 224.681185 \nL 189.732918 224.738325 \nL 190.261115 224.993848 \nL 190.58616 224.980501 \nL 191.439402 224.738075 \nL 191.805077 224.912301 \nL 192.170753 225.040856 \nL 192.617689 225.07755 \nL 194.283542 225.732521 \nL 194.608587 225.837072 \nL 195.177415 226.108361 \nL 195.299307 226.122449 \nL 195.461829 225.994492 \nL 196.23381 226.372826 \nL 196.355702 226.25982 \nL 196.518224 226.248834 \nL 197.290205 226.509407 \nL 197.61525 226.661879 \nL 198.184078 226.790054 \nL 198.38723 226.855254 \nL 198.915428 227.095645 \nL 199.321734 227.068241 \nL 199.890562 227.336315 \nL 200.784434 227.640934 \nL 201.068848 227.645815 \nL 201.515785 227.792295 \nL 202.003352 227.954569 \nL 202.165874 227.90661 \nL 202.328396 227.869696 \nL 203.100377 228.224037 \nL 203.344161 228.15922 \nL 204.03488 228.447272 \nL 204.238033 228.410104 \nL 204.888122 228.685616 \nL 206.025779 228.962623 \nL 206.188301 229.009485 \nL 206.797759 229.270396 \nL 207.041543 229.239933 \nL 207.447849 229.455707 \nL 207.772893 229.56985 \nL 207.976046 229.539361 \nL 208.21983 229.639849 \nL 208.95118 229.799196 \nL 209.194963 229.7813 \nL 209.723161 230.018664 \nL 210.170097 230.025205 \nL 210.698295 230.233006 \nL 211.06397 230.227177 \nL 211.632798 230.467164 \nL 211.957842 230.43496 \nL 212.607932 230.714519 \nL 213.014237 230.709982 \nL 213.583065 230.952273 \nL 213.90811 230.940407 \nL 214.59883 231.249333 \nL 214.923874 231.220922 \nL 215.533333 231.498657 \nL 215.777117 231.459264 \nL 216.061531 231.493271 \nL 216.71162 231.713405 \nL 217.036664 231.751596 \nL 217.686754 231.99504 \nL 218.052429 231.917888 \nL 218.580626 232.106714 \nL 218.783779 232.005482 \nL 222.643684 233.137191 \nL 222.765575 233.131143 \nL 223.415665 233.38967 \nL 223.943862 233.491449 \nL 224.51269 233.648184 \nL 224.797104 233.706666 \nL 225.284671 233.85417 \nL 225.690977 233.879558 \nL 226.259805 234.067081 \nL 227.478722 234.258139 \nL 227.925658 234.439303 \nL 228.331964 234.540436 \nL 228.7789 234.651095 \nL 229.266467 234.758785 \nL 229.591512 234.766166 \nL 230.200971 234.981319 \nL 230.607276 235.022309 \nL 231.338627 235.315928 \nL 231.704302 235.369542 \nL 232.232499 235.528583 \nL 232.476283 235.470883 \nL 233.207633 235.76353 \nL 233.492047 235.751479 \nL 234.264028 235.989663 \nL 234.507812 236.029006 \nL 236.295557 236.5534 \nL 236.417449 236.557756 \nL 237.189429 236.818825 \nL 237.514474 236.875778 \nL 239.099066 237.198225 \nL 239.383481 237.256951 \nL 240.03357 237.447989 \nL 240.236723 237.438939 \nL 240.846181 237.626547 \nL 241.983837 237.920506 \nL 242.14636 237.826579 \nL 242.999602 238.07918 \nL 243.284016 238.036888 \nL 243.852844 238.218281 \nL 244.502933 238.309255 \nL 244.909239 238.402753 \nL 245.274914 238.400193 \nL 245.762481 238.525575 \nL 246.128156 238.545347 \nL 246.859506 238.78348 \nL 247.225181 238.82799 \nL 247.875271 239.034551 \nL 250.150583 239.629724 \nL 250.922564 239.870546 \nL 251.125717 239.941229 \nL 251.775806 240.18763 \nL 252.019589 240.090383 \nL 252.547787 240.241297 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246.090246 \nL 279.282705 246.064897 \nL 279.973425 246.228956 \nL 280.217208 246.24612 \nL 280.948559 246.443277 \nL 281.151711 246.351418 \nL 281.679909 246.442401 \nL 281.923692 246.436833 \nL 282.126845 246.373105 \nL 282.898826 246.563772 \nL 283.223871 246.585709 \nL 283.752068 246.704384 \nL 284.199005 246.677949 \nL 284.808463 246.814182 \nL 285.377291 246.900207 \nL 285.905489 246.993111 \nL 286.149272 246.992488 \nL 286.880623 247.121579 \nL 287.083775 247.105169 \nL 287.896387 247.257223 \nL 288.018279 247.218554 \nL 288.627737 247.370582 \nL 289.074674 247.380532 \nL 289.846655 247.480605 \nL 290.25296 247.594651 \nL 290.862419 247.672997 \nL 291.146833 247.747427 \nL 291.837553 247.917652 \nL 292.040706 247.93693 \nL 292.731425 248.070456 \nL 292.975209 248.056236 \nL 294.600432 248.408003 \nL 294.844215 248.341837 \nL 295.575566 248.511488 \nL 295.819349 248.467668 \nL 296.550699 248.650375 \nL 296.794483 248.626234 \nL 297.485203 248.787109 \nL 298.257184 248.901366 \nL 298.70412 248.963387 \nL 298.988534 249.044322 \nL 300.451235 249.310839 \nL 300.81691 249.28812 \nL 301.54826 249.358777 \nL 301.954566 249.456399 \nL 302.482763 249.512006 \nL 302.767177 249.574108 \nL 303.376636 249.718586 \nL 303.620419 249.668508 \nL 304.270509 249.805152 \nL 305.205012 249.942332 \nL 306.342668 250.192636 \nL 306.545821 250.102316 \nL 307.317802 250.275792 \nL 307.845999 250.27619 \nL 308.333566 250.374301 \nL 308.536719 250.394066 \nL 309.34933 250.56342 \nL 309.633744 250.622615 \nL 310.283834 250.757571 \nL 310.608878 250.810804 \nL 311.705904 251.037037 \nL 312.518515 251.223669 \nL 313.127974 251.352377 \nL 313.412388 251.342764 \nL 314.224999 251.496147 \nL 314.550044 251.550403 \nL 316.094006 251.866353 \nL 316.459681 251.942095 \nL 317.028509 251.9964 \nL 317.272293 251.927298 \nL 318.044273 252.040857 \nL 319.303821 252.280957 \nL 319.872649 252.329065 \nL 320.360216 252.375857 \nL 321.782286 252.713474 \nL 322.107331 252.688829 \nL 323.773185 253.033142 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196.705188 \nL 96.892054 196.731391 \nL 97.420252 197.761577 \nL 97.460882 197.703339 \nL 97.582774 197.505379 \nL 97.826558 196.881432 \nL 97.907819 196.973786 \nL 98.395386 198.090656 \nL 98.517277 198.022268 \nL 98.801691 197.279731 \nL 98.842322 197.218962 \nL 98.882953 197.305121 \nL 98.964214 197.508222 \nL 99.004844 197.401913 \nL 99.045475 197.293987 \nL 99.086105 197.354107 \nL 99.695564 198.556938 \nL 99.898717 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 \nL 114.28194 201.45498 \nL 114.322571 201.535274 \nL 114.363202 201.504524 \nL 114.485093 201.096663 \nL 114.525724 201.130386 \nL 115.053921 201.891421 \nL 115.419597 201.460106 \nL 115.460227 201.49729 \nL 115.988425 202.238037 \nL 116.272839 201.724809 \nL 116.3541 201.646694 \nL 116.39473 201.673203 \nL 116.963558 202.414371 \nL 117.288603 201.869725 \nL 117.369864 201.860941 \nL 117.410495 201.913551 \nL 117.898062 202.644064 \nL 118.019953 202.572805 \nL 118.223106 202.315436 \nL 118.344998 202.18295 \nL 118.385629 202.25218 \nL 118.873195 202.940669 \nL 118.995087 202.925509 \nL 119.116979 202.785955 \nL 119.157609 202.817292 \nL 119.360762 202.670631 \nL 119.401393 202.741894 \nL 119.92959 203.352569 \nL 120.010852 203.214795 \nL 120.335896 202.630986 \nL 120.945355 203.509886 \nL 121.067246 203.365245 \nL 121.229769 203.276553 \nL 121.31103 203.060945 \nL 121.351661 203.08987 \nL 121.920489 203.866865 \nL 122.286164 203.438915 \nL 122.773731 204.065893 \nL 122.895622 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 \nL 139.351005 207.702493 \nL 139.472897 207.639433 \nL 139.635419 207.496279 \nL 140.001094 207.608232 \nL 140.448031 208.020469 \nL 140.813706 207.670202 \nL 140.854336 207.70961 \nL 141.463795 208.290655 \nL 141.951362 208.057606 \nL 142.398298 208.528545 \nL 142.438929 208.489239 \nL 142.642082 208.181053 \nL 142.763973 207.79748 \nL 142.804604 207.843417 \nL 143.21091 208.189994 \nL 143.332802 208.235686 \nL 143.739107 207.988714 \nL 144.267305 208.435225 \nL 144.795502 208.219361 \nL 145.161177 208.586675 \nL 145.3237 208.680233 \nL 145.567483 208.546935 \nL 145.730005 208.577698 \nL 146.298833 209.066781 \nL 146.420725 208.917801 \nL 146.461356 208.963293 \nL 146.7864 208.894334 \nL 146.989553 209.106215 \nL 147.273967 209.343873 \nL 147.599012 208.837805 \nL 147.680273 208.739848 \nL 147.720904 208.77182 \nL 148.452254 209.143495 \nL 148.533515 209.03148 \nL 148.696037 208.379518 \nL 148.736668 208.42673 \nL 149.224235 208.900141 \nL 149.264865 208.85403 \nL 149.508649 208.587315 \nL 149.630541 208.170754 \nL 149.671171 208.195834 \nL 150.199369 208.579684 \nL 150.565044 208.076781 \nL 150.646305 208.157105 \nL 150.768197 208.202661 \nL 151.093241 208.521588 \nL 151.133872 208.480356 \nL 151.540178 207.975845 \nL 151.621439 208.058896 \nL 151.865222 208.267727 \nL 152.109006 208.427528 \nL 152.43405 208.140162 \nL 152.515311 208.006421 \nL 152.555942 208.049619 \nL 153.12477 208.48166 \nL 153.571706 208.130625 \nL 154.099904 208.507717 \nL 154.140534 208.451564 \nL 154.50621 207.964216 \nL 154.54684 207.996991 \nL 155.075038 208.351719 \nL 155.318821 208.406549 \nL 155.643866 208.214644 \nL 156.050171 208.492884 \nL 156.172063 208.376165 \nL 156.212694 208.416377 \nL 156.253324 208.446586 \nL 156.293955 208.34817 \nL 156.415847 208.055656 \nL 156.456477 208.080137 \nL 157.106566 208.320395 \nL 157.512872 208.233956 \nL 157.716025 208.407687 \nL 157.959808 208.547419 \nL 158.122331 208.380051 \nL 158.162961 208.420834 \nL 158.203592 208.423536 \nL 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 211.775735 \nL 173.561949 212.000943 \nL 174.090147 212.039695 \nL 174.577714 212.291785 \nL 174.740236 212.150723 \nL 174.780866 212.184619 \nL 175.105911 212.225401 \nL 175.552847 212.463748 \nL 175.959153 212.252915 \nL 176.527981 212.615634 \nL 176.731134 212.57011 \nL 177.015548 212.507442 \nL 177.543746 212.671429 \nL 177.665637 212.660983 \nL 178.478249 212.983853 \nL 178.803293 213.025967 \nL 179.697166 213.438948 \nL 179.819058 213.383776 \nL 180.347255 213.705576 \nL 180.469147 213.668765 \nL 180.6723 213.585973 \nL 180.956714 213.692794 \nL 181.322389 213.847345 \nL 181.931848 213.771543 \nL 182.419415 214.018737 \nL 182.78509 213.899452 \nL 183.353918 214.246859 \nL 183.51644 214.216668 \nL 183.882115 214.224052 \nL 184.288421 214.459631 \nL 184.654096 214.348325 \nL 185.304185 214.658163 \nL 185.588599 214.577656 \nL 185.791752 214.682645 \nL 186.279319 214.927899 \nL 186.482472 214.887374 \nL 186.766886 215.019159 \nL 187.254453 215.290596 \nL 187.498236 215.25318 \nL 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 \nL 203.912989 218.634345 \nL 204.116142 218.565095 \nL 204.278664 218.612844 \nL 205.010014 218.882491 \nL 205.253798 218.879939 \nL 205.578842 219.074408 \nL 205.903887 219.085936 \nL 206.432084 219.222023 \nL 206.797759 219.35219 \nL 207.041543 219.28482 \nL 207.325957 219.437022 \nL 207.772893 219.607694 \nL 207.976046 219.549685 \nL 208.179199 219.621165 \nL 208.748027 219.826415 \nL 209.113702 219.660274 \nL 209.723161 219.952171 \nL 210.129467 219.878116 \nL 210.698295 220.103923 \nL 211.023339 220.025463 \nL 211.592167 220.306666 \nL 211.957842 220.208257 \nL 212.607932 220.484338 \nL 212.973607 220.41414 \nL 213.623696 220.656674 \nL 213.90811 220.625457 \nL 214.558199 220.925914 \nL 214.801983 220.842687 \nL 214.964505 220.889715 \nL 215.533333 221.145866 \nL 215.777117 221.069547 \nL 216.0209 221.052745 \nL 216.549097 221.280848 \nL 216.914773 221.201111 \nL 217.646123 221.520157 \nL 218.214951 221.440601 \nL 218.499365 221.512412 \nL 219.149454 221.569635 \nL 219.55576 221.698141 \nL 220.083957 221.768426 \nL 220.449633 221.892268 \nL 220.774677 221.869226 \nL 221.506028 222.18604 \nL 221.831072 222.197472 \nL 222.562423 222.434394 \nL 222.928098 222.439873 \nL 223.456295 222.597842 \nL 223.659448 222.564773 \nL 223.78134 222.581256 \nL 224.431429 222.851573 \nL 224.715843 222.804238 \nL 225.24404 222.998973 \nL 225.812869 223.031685 \nL 226.300435 223.164036 \nL 226.62548 223.135826 \nL 227.275569 223.352531 \nL 227.559983 223.301465 \nL 228.169442 223.545131 \nL 228.941423 223.720389 \nL 229.225837 223.747468 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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -356,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {
     "tags": []
    },
@@ -364,15 +391,24 @@
     {
      "output_type": "stream",
      "name": "stderr",
-     "text": "Seed namespaces (singletons and interactions): ['e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nCreated challengers from champion |supervised||classic\nNew challenger size 37, ['|supervised|gi|classic', '|supervised|eh|classic', '|supervised|ad|classic', '|supervised|gh|classic', '|supervised|bc|classic', '|supervised|bd|classic', '|supervised|ae|classic', '|supervised|dg|classic', '|supervised|ei|classic', '|supervised|df|classic', '|supervised|fh|classic', '|supervised|ac|classic', '|supervised|ab|classic', '|supervised|cg|classic', '|supervised|hi|classic', '|supervised|fg|classic', '|supervised|bi|classic', '|supervised|be|classic', '|supervised|de|classic', '|supervised|ci|classic', '|supervised|fi|classic', '|supervised|cd|classic', '|supervised|af|classic', '|supervised|ce|classic', '|supervised|di|classic', '|supervised|bf|classic', '|supervised|ai|classic', '|supervised|bh|classic', '|supervised|ag|classic', '|supervised|bg|classic', '|supervised|eg|classic', '|supervised|ah|classic', '|supervised|cf|classic', '|supervised|dh|classic', '|supervised|ef|classic', '|supervised|ch|classic', '|supervised||classic']\nOnline learning for 10000 steps...\nSeed namespaces (singletons and interactions): ['cf', 'e', 'g', 'b', 'd', 'i', 'h', 'a', 'f', 'c']\nCreated challengers from champion |supervised|cf|classic\nNew challenger size 43, ['|supervised|bg_cf|classic', '|supervised|cf_dg|classic', '|supervised|ab_cf|classic', '|supervised|bh_cf|classic', '|supervised|cf_eg|classic', '|supervised|cf_ef|classic', '|supervised|be_cf|classic', '|supervised|cf_di|classic', '|supervised|cf_ci|classic', '|supervised|bd_cf|classic', '|supervised|cf_fi|classic', '|supervised|bf_cf|classic', '|supervised|ah_cf|classic', '|supervised|ac_cf|classic', '|supervised|ce_cf|classic', '|supervised|cf|classic', '|supervised|cf_cfg|classic', '|supervised|cf_gi|classic', '|supervised|ag_cf|classic', '|supervised|ae_cf|classic', '|supervised|cf_fg|classic', '|supervised|cf_hi|classic', '|supervised|cf_df|classic', '|supervised|cef_cf|classic', '|supervised|cdf_cf|classic', '|supervised|cd_cf|classic', '|supervised|bc_cf|classic', '|supervised|cf_gh|classic', '|supervised|cf_cg|classic', '|supervised|cf_ch|classic', '|supervised|bcf_cf|classic', '|supervised|af_cf|classic', '|supervised|cf_ei|classic', '|supervised|ai_cf|classic', '|supervised|cf_dh|classic', '|supervised|ad_cf|classic', '|supervised|cf_de|classic', '|supervised|cf_fh|classic', '|supervised|cf_eh|classic', '|supervised|acf_cf|classic', '|supervised|bi_cf|classic', '|supervised|cf_cfi|classic', '|supervised|cf_cfh|classic']\nAverage final loss of the AutoVW (tuning namespaces) based on customized vw arguments: 9.606119226635231\n"
+     "text": [
+      "Seed namespaces (singletons and interactions): ['g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
+      "Created challengers from champion |supervised||classic|\n",
+      "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",
+      "Online learning for 10000 steps...\n",
+      "Seed namespaces (singletons and interactions): ['df', 'g', 'a', 'h', 'b', 'c', 'i', 'd', 'e', 'f']\n",
+      "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'}\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.AUTO_STRING})\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",
@@ -390,12 +426,7 @@
  "metadata": {
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3",
-   "metadata": {
-    "interpreter": {
-     "hash": "0cfea3304185a9579d09e0953576b57c8581e46e6ebc6dfeb681bc5a511f7544"
-    }
-   }
+   "display_name": "Python 3.8.10 64-bit ('py38': conda)"
   },
   "language_info": {
    "codemirror_mode": {
@@ -407,7 +438,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.13-final"
+   "version": "3.8.10"
+  },
+  "interpreter": {
+   "hash": "4502d015faca2560a557f35a41b6dd402f7fdfc08e843ae17a9c41947939f10c"
   }
  },
  "nbformat": 4,
diff --git a/setup.py b/setup.py
index 462426243..75ad32fc9 100644
--- a/setup.py
+++ b/setup.py
@@ -56,7 +56,6 @@ setuptools.setup(
             "torch==1.8.1",
             "datasets==1.4.1",
             "azure-storage-blob",
-            "tensorflow"
         ],
         "blendsearch": [
             "optuna==2.3.0"
@@ -78,6 +77,7 @@ setuptools.setup(
             "ray[tune]>=1.2.0",
             "transformers",
             "datasets==1.4.1",
+            "tensorboardX<=2.2",
             "torch"
         ]
     },