.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/experiment_pipeline_lisn.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end <sphx_glr_download_auto_examples_experiment_pipeline_lisn.py>` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_experiment_pipeline_lisn.py: Running an Experiment on a dataset using pipeline API ===================================================== This notebook demonstrates running an experiment to search for hyperparameters and saving the scores of the experiment for a specified set of parameters using `cartodata.pipeline.PipelineExperiment` for `lisn` dataset. First we will define necessary global variables. .. GENERATED FROM PYTHON SOURCE LINES 9-26 .. code-block:: Python from pathlib import Path # noqa ROOT_DIR = Path.cwd().parent SOURCE = "authors" NATURE = "articles" # The directory where the artifacts of the experiment will be saved TOP_DIR = ROOT_DIR / "experiment_pipeline_lisn" # The directory where dataset.yaml files reside CONF_DIR = ROOT_DIR / "conf" # The directory where files necessary to load dataset columns reside INPUT_DIR = ROOT_DIR / "datas" TOP_DIR .. rst-class:: sphx-glr-script-out .. code-block:: none PosixPath('/builds/2mk6rsew/0/hgozukan/cartolabe-data/experiment_pipeline_lisn') .. GENERATED FROM PYTHON SOURCE LINES 27-31 Initialize Parameter Iterator ----------------------------- We will initilialize a parameter iterator to iterate through our parameters. We have two options `GridIterator` and `RandomIterator`. .. GENERATED FROM PYTHON SOURCE LINES 31-39 .. code-block:: Python from cartodata.model_selection.iterator import GridIterator, RandomIterator # noqa help(GridIterator) "" help(RandomIterator) .. rst-class:: sphx-glr-script-out .. code-block:: none Help on class GridIterator in module cartodata.model_selection.iterator: class GridIterator(ParameterIteratorBase) | GridIterator(df=None, csv_filepath=None, params_dict=None, index_col=0) | | A parameter iterator class that iterates through a given set of | parameters in order. | | Method resolution order: | GridIterator | ParameterIteratorBase | builtins.object | | Methods defined here: | | __init__(self, df=None, csv_filepath=None, params_dict=None, index_col=0) | Initializes the params_frame either by the specified ``df``, from the | specified ``csv_filepath`` or the ``params_dict``. | | Parameters | ---------- | df: pandas.DataFrame | the dataframe that contains the list of parameter sets. | csv_filepath: str, Path | the path of the .csv file that contains the list of parameter sets. | params_dict: dict | dictionary of possible parameters specified as key: list pairs. | index_col: int | index column if a csv_filepath is specified. | | ---------------------------------------------------------------------- | Methods inherited from ParameterIteratorBase: | | has_next(self) | Checks if there is a set of parameters that is that used yet. | | Returns | ------- | bool | True if there still is a set of parameters that is not used yet; | False otherwise. | | head(self, n=10) | Views top `n` entries in the `params_frame.` | | Parameters | ---------- | n: int | the number of rows to view. | | next(self) | Returns the next set of values as dictionary. | | Removes ``selected`` column before returning. | | Example set of values: | { | "id": 7b5e167fb3242dacae5c, | "robustseed" : 0, | "dataset" : "lisn", | "min_df" : 10, | "max_df" : 0.5, | "max_words" : "None", | "vocab_sample" : "None", | "filter_min_score" : 3, | "projectionnD" : "lsa", | "num_dim" : 50, | "projection2D": "umap", | "n_neighbors": 15, | "min_dist": 0.1, | "metric": "euclidean", | "clustering" : "kmeans", | "base_factor" : 3, | "weight_name_length" : 0, | } | | set_initial_executed(self) | Check params_frame for the rows that are already run and sets | `nb_initial_executed` so that these row are not considered for `nbmax` | value specified for `stop` function. | | stop(self, nbmax=None) | Checks if stopping condition is satisfied. If the experiment is run | `nbmax` times or there are no other set of parameters to test, stopping | condition is satisfied. | | Parameters | ---------- | nbmax: int, default=None | maximum number of experiments to run. | | Returns | ------- | bool | True if stopping condition is satisfied; False otherwise. | | to_csv(self, filepath) | Persists `params_frame` to the specified `filepath`. | | Parameters | ---------- | filepath: str, Path | the path to save the contents of params_frame. | | transform(self, x) | | ---------------------------------------------------------------------- | Readonly properties inherited from ParameterIteratorBase: | | columns | Returns the column names of `params_frame`. | | ---------------------------------------------------------------------- | Data descriptors inherited from ParameterIteratorBase: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) Help on class RandomIterator in module cartodata.model_selection.iterator: class RandomIterator(ParameterIteratorBase) | RandomIterator(df=None, csv_filepath=None, params_dict=None, index_col=0, seed=None) | | A parameter iterator class that iterates through a given set of | parameters by random selections. | | Method resolution order: | RandomIterator | ParameterIteratorBase | builtins.object | | Methods defined here: | | __init__(self, df=None, csv_filepath=None, params_dict=None, index_col=0, seed=None) | Initializes the params_frame either by the specified ``df``, from the | specified ``csv_filepath`` or the ``params_dict``. | | Parameters | ---------- | df: pandas.DataFrame | the dataframe that contains the list of parameter sets. | csv_filepath: str, Path | the path of the .csv file that contains the list of parameter sets. | params_dict: dict | dictionary of possible parameters specified as key: list pairs. | index_col: int | index column if a csv_filepath is specified. | seed: int | seed for random generator | | ---------------------------------------------------------------------- | Methods inherited from ParameterIteratorBase: | | has_next(self) | Checks if there is a set of parameters that is that used yet. | | Returns | ------- | bool | True if there still is a set of parameters that is not used yet; | False otherwise. | | head(self, n=10) | Views top `n` entries in the `params_frame.` | | Parameters | ---------- | n: int | the number of rows to view. | | next(self) | Returns the next set of values as dictionary. | | Removes ``selected`` column before returning. | | Example set of values: | { | "id": 7b5e167fb3242dacae5c, | "robustseed" : 0, | "dataset" : "lisn", | "min_df" : 10, | "max_df" : 0.5, | "max_words" : "None", | "vocab_sample" : "None", | "filter_min_score" : 3, | "projectionnD" : "lsa", | "num_dim" : 50, | "projection2D": "umap", | "n_neighbors": 15, | "min_dist": 0.1, | "metric": "euclidean", | "clustering" : "kmeans", | "base_factor" : 3, | "weight_name_length" : 0, | } | | set_initial_executed(self) | Check params_frame for the rows that are already run and sets | `nb_initial_executed` so that these row are not considered for `nbmax` | value specified for `stop` function. | | stop(self, nbmax=None) | Checks if stopping condition is satisfied. If the experiment is run | `nbmax` times or there are no other set of parameters to test, stopping | condition is satisfied. | | Parameters | ---------- | nbmax: int, default=None | maximum number of experiments to run. | | Returns | ------- | bool | True if stopping condition is satisfied; False otherwise. | | to_csv(self, filepath) | Persists `params_frame` to the specified `filepath`. | | Parameters | ---------- | filepath: str, Path | the path to save the contents of params_frame. | | transform(self, x) | | ---------------------------------------------------------------------- | Readonly properties inherited from ParameterIteratorBase: | | columns | Returns the column names of `params_frame`. | | ---------------------------------------------------------------------- | Data descriptors inherited from ParameterIteratorBase: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) .. GENERATED FROM PYTHON SOURCE LINES 40-41 We define the set of parameters that we want to test. Phase parameters should be specified using phase name and list of dictionaries for phase parameters. .. GENERATED FROM PYTHON SOURCE LINES 41-73 .. code-block:: Python from cartodata.phases import PhaseProjectionND, PhaseProjection2D, PhaseClustering params = { "robustseed" : [0], "authors__filter_min_score": [4], "filter_min_score": [6], "words__column_names": [["en_keyword_s", "en_domainAllCodeLabel_fs", "en_abstract_s", "en_title_s" ], ["en_abstract_s", "en_title_s", "en_keyword_s", "en_domainAllCodeLabel_fs", ]], PhaseProjectionND.NAME : [ { "key": ["bert"], "family": ["all-MiniLM-L6-v2", "specter2"], "max_length": [256, 512] }, { "key": ["lsa"], "num_dims": [50, 100, 200] } ], PhaseProjection2D.NAME : [ { "key": ["umap"], "n_neighbors" : [10, 20, 50], "min_dist" : [0.1, 0.25, 0.5], "metric" : ["euclidean"] }, { "key": ["tsne"], "perplexity" : [30, 50] } ] } "" param_iterator = GridIterator(params_dict=params) "" param_iterator.params_frame.shape "" param_iterator.params_frame .. raw:: html <div class="output_subarea output_html rendered_html output_result"> <div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>id</th> <th>robustseed</th> <th>authors__filter_min_score</th> <th>filter_min_score</th> <th>words__column_names</th> <th>projection_nD</th> <th>projection_2D</th> <th>selected</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>5acf1c353d2c392a1729</td> <td>0</td> <td>4</td> <td>6</td> <td>[en_keyword_s, en_domainAllCodeLabel_fs, en_ab...</td> <td>{'key': 'bert', 'family': 'all-MiniLM-L6-v2', ...</td> <td>{'key': 'umap', 'n_neighbors': 10, 'min_dist':...</td> <td>False</td> </tr> <tr> <th>1</th> <td>da34d4237bc01ee70684</td> <td>0</td> <td>4</td> <td>6</td> <td>[en_keyword_s, en_domainAllCodeLabel_fs, en_ab...</td> <td>{'key': 'bert', 'family': 'all-MiniLM-L6-v2', ...</td> <td>{'key': 'umap', 'n_neighbors': 10, 'min_dist':...</td> <td>False</td> </tr> <tr> <th>2</th> <td>a862f98f80e4912e4e08</td> <td>0</td> <td>4</td> <td>6</td> <td>[en_keyword_s, en_domainAllCodeLabel_fs, en_ab...</td> <td>{'key': 'bert', 'family': 'all-MiniLM-L6-v2', ...</td> <td>{'key': 'umap', 'n_neighbors': 10, 'min_dist':...</td> <td>False</td> </tr> <tr> <th>3</th> <td>a3cd00f35555d3301dab</td> <td>0</td> <td>4</td> <td>6</td> <td>[en_keyword_s, en_domainAllCodeLabel_fs, en_ab...</td> <td>{'key': 'bert', 'family': 'all-MiniLM-L6-v2', ...</td> <td>{'key': 'umap', 'n_neighbors': 20, 'min_dist':...</td> <td>False</td> </tr> <tr> <th>4</th> <td>7e3fa98210bea7d56da4</td> <td>0</td> <td>4</td> <td>6</td> <td>[en_keyword_s, en_domainAllCodeLabel_fs, en_ab...</td> <td>{'key': 'bert', 'family': 'all-MiniLM-L6-v2', ...</td> <td>{'key': 'umap', 'n_neighbors': 20, 'min_dist':...</td> <td>False</td> </tr> <tr> <th>...</th> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> <tr> <th>149</th> <td>3bc0eaea7e130fc4bcef</td> <td>0</td> <td>4</td> <td>6</td> <td>[en_abstract_s, en_title_s, en_keyword_s, en_d...</td> <td>{'key': 'lsa', 'num_dims': 200}</td> <td>{'key': 'umap', 'n_neighbors': 50, 'min_dist':...</td> <td>False</td> </tr> <tr> <th>150</th> <td>f702093065fd195285b0</td> <td>0</td> <td>4</td> <td>6</td> <td>[en_abstract_s, en_title_s, en_keyword_s, en_d...</td> <td>{'key': 'lsa', 'num_dims': 200}</td> <td>{'key': 'umap', 'n_neighbors': 50, 'min_dist':...</td> <td>False</td> </tr> <tr> <th>151</th> <td>f3c02394b1531b5bd3ae</td> <td>0</td> <td>4</td> <td>6</td> <td>[en_abstract_s, en_title_s, en_keyword_s, en_d...</td> <td>{'key': 'lsa', 'num_dims': 200}</td> <td>{'key': 'umap', 'n_neighbors': 50, 'min_dist':...</td> <td>False</td> </tr> <tr> <th>152</th> <td>1c855fd02dab6b8f60aa</td> <td>0</td> <td>4</td> <td>6</td> <td>[en_abstract_s, en_title_s, en_keyword_s, en_d...</td> <td>{'key': 'lsa', 'num_dims': 200}</td> <td>{'key': 'tsne', 'perplexity': 30}</td> <td>False</td> </tr> <tr> <th>153</th> <td>83b2e801649d7f3bf7f8</td> <td>0</td> <td>4</td> <td>6</td> <td>[en_abstract_s, en_title_s, en_keyword_s, en_d...</td> <td>{'key': 'lsa', 'num_dims': 200}</td> <td>{'key': 'tsne', 'perplexity': 50}</td> <td>False</td> </tr> </tbody> </table> <p>154 rows × 8 columns</p> </div> </div> <br /> <br /> .. GENERATED FROM PYTHON SOURCE LINES 74-79 Run Experiment -------------------------------------- We will run the expriment using `cartodata.pipeline.PipelineExperiment`. .. GENERATED FROM PYTHON SOURCE LINES 79-84 .. code-block:: Python from cartodata.pipeline.experiment import PipelineExperiment # noqa help(PipelineExperiment) .. rst-class:: sphx-glr-script-out .. code-block:: none Help on class PipelineExperiment in module cartodata.pipeline.experiment: class PipelineExperiment(cartodata.model_selection.experiment.BaseExperiment) | PipelineExperiment(dataset_name, dataset_version, top_dir, conf_dir, input_dir, nature, source, selector, score_list=None, **score_params) | | Method resolution order: | PipelineExperiment | cartodata.model_selection.experiment.BaseExperiment | builtins.object | | Methods defined here: | | run_steps(self, next_params) | | ---------------------------------------------------------------------- | Methods inherited from cartodata.model_selection.experiment.BaseExperiment: | | __init__(self, dataset_name, dataset_version, top_dir, conf_dir, input_dir, nature, source, selector, score_list=None, **score_params) | Parameters | ---------- | nature: str | source: str | top_dir: str or Path | the top directory inside which experiment artifacts will be | generated | selector: cartodata.model_selection.selector.ParameterSelector | the selector that provides the next set of parameters for the | experiment | score_list: list of cartodata.model_selection.evaluators.Evaluators, | default=None | score_params: kwargs | The parameters that will be used by evaluators should be specified | as kwargs in the format ``evaluator_key__parameter_name=value`` | For example ``name_list`` parameter for ``final_score`` should be | specified as: | | ``final_score__name_list=["neighbors_nd_articles_authors", | "neighbors_2d_articles_authors", | "clu_score"] | | add_2D_scores(self, all_natures, dir_mat, key_nD, key_2D, dir_nD, dir_2D, plots=None, run_params={}) | | add_clustering_scores(self, all_natures, cluster_natures, key_nD, key_2D, dir_mat, dir_nD, dir_2D, dir_clus, words_index, plots=None, run_params={}) | | add_nD_scores(self, key_nD=None, dir_mat=None, dir_nD=None, run_params={}) | | add_plots_to_scoring(self, plots, natures) | | add_post_scores(self, dir_clus, run_params={}) | | finish_iteration(self, next_params, dir_clus) | | persist_result(self, result, phase, phase_result, dump_dir) | | run(self, nbmax) | Runs the experiment for the specified `nbmax` number of times. | | :param nbmax: maximum number of experiments to run. | | save_plots(self, natures, matrices, dir_dump, title_parts, annotations=None, annotation_mat=None, file_ext='.png') | | ---------------------------------------------------------------------- | Data descriptors inherited from cartodata.model_selection.experiment.BaseExperiment: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) .. GENERATED FROM PYTHON SOURCE LINES 85-86 All possible scoring classes are in the `cartodata.model_selection.scoring` module. We will run scoring for each, so we will import them all. .. GENERATED FROM PYTHON SOURCE LINES 86-92 .. code-block:: Python from cartodata.model_selection.scoring import ( NeighborsND, Neighbors2D, Comparative, TrustworthinessSklearn, TrustworthinessUmap, Clustering, FinalScore ) .. GENERATED FROM PYTHON SOURCE LINES 93-101 We need to specify which scores to calculate to the experiment using the parameter `score_list`. If we do not specify, the experiment will evaluate scores for all available scoring classes. We will do it explicitly and specify all classes defined in `cartodata.model_selection.scoring` module. It is possible to specify parameters for score classes as keyword arguments. For example, if `cartodata.model_selection.scoring.FinalScore` is specified in the `score_list`, the experiment calculates an aggregated score taking average of all scores at the end of each run. If instead of all scores, we want a subset of scores to be included in the average, we can specify it using `final_score__name_list`. For each scoring class, we should name the parameter in the format `scoring_KEY__scoring_parameter`. .. GENERATED FROM PYTHON SOURCE LINES 101-122 .. code-block:: Python experiment = PipelineExperiment( "lisn", "2022.11.15.1", TOP_DIR, CONF_DIR, INPUT_DIR, NATURE, SOURCE, param_iterator, score_list=[NeighborsND, Neighbors2D, Comparative, TrustworthinessSklearn, # TrustworthinessUmap, Clustering, FinalScore], final_score__name_list=[ PhaseProjectionND.prefix("neighbors_articles_authors"), PhaseProjection2D.prefix("neighbors_articles_authors"), PhaseClustering.prefix("clu_score") ], neighbors__recompute=True, neighbors__min_score=30, trustworthiness_sklearn__n_neighbors=10 ) .. GENERATED FROM PYTHON SOURCE LINES 123-124 Now we will run the experiment for 3 different set of parameters. .. GENERATED FROM PYTHON SOURCE LINES 124-127 .. code-block:: Python results = experiment.run(3) .. rst-class:: sphx-glr-script-out .. code-block:: none Downloading data from https://zenodo.org/records/7323538/files/lisn_2000_2022.csv (6.3 MB) file_sizes: 0%| | 0.00/6.59M [00:00<?, ?B/s] file_sizes: 63%|████████████████▍ | 4.16M/6.59M [00:00<00:00, 39.7MB/s] file_sizes: 100%|██████████████████████████| 6.59M/6.59M [00:00<00:00, 53.4MB/s] Successfully downloaded file to /builds/2mk6rsew/0/hgozukan/cartolabe-data/experiment_pipeline_lisn/lisn/2022.11.15.1/lisn_2000_2022.csv Processing batches: 0%| | 0/427 [00:00<?, ?it/s] Processing batches: 0%| | 1/427 [00:03<21:37, 3.05s/it] Processing batches: 0%| | 2/427 [00:05<17:03, 2.41s/it] Processing batches: 1%| | 3/427 [00:07<18:32, 2.62s/it] Processing batches: 1%| | 4/427 [00:11<21:59, 3.12s/it] Processing batches: 1%| | 5/427 [00:14<21:00, 2.99s/it] Processing batches: 1%|▏ | 6/427 [00:16<18:37, 2.65s/it] Processing batches: 2%|▏ | 7/427 [00:18<16:55, 2.42s/it] Processing batches: 2%|▏ | 8/427 [00:20<15:50, 2.27s/it] Processing batches: 2%|▏ | 9/427 [00:22<16:18, 2.34s/it] Processing batches: 2%|▏ | 10/427 [00:24<14:40, 2.11s/it] Processing batches: 3%|▎ | 11/427 [00:25<13:01, 1.88s/it] Processing batches: 3%|▎ | 12/427 [00:28<15:27, 2.23s/it] Processing batches: 3%|▎ | 13/427 [00:31<15:15, 2.21s/it] Processing batches: 3%|▎ | 14/427 [00:34<18:14, 2.65s/it] Processing batches: 4%|▎ | 15/427 [00:36<16:45, 2.44s/it] Processing batches: 4%|▎ | 16/427 [00:41<20:50, 3.04s/it] Processing batches: 4%|▍ | 17/427 [00:42<17:31, 2.56s/it] Processing batches: 4%|▍ | 18/427 [00:43<15:02, 2.21s/it] Processing batches: 4%|▍ | 19/427 [00:46<15:58, 2.35s/it] Processing batches: 5%|▍ | 20/427 [00:50<19:56, 2.94s/it] Processing batches: 5%|▍ | 21/427 [00:54<20:20, 3.01s/it] Processing batches: 5%|▌ | 22/427 [00:55<17:40, 2.62s/it] Processing batches: 5%|▌ | 23/427 [00:58<17:31, 2.60s/it] Processing batches: 6%|▌ | 24/427 [01:01<18:07, 2.70s/it] Processing batches: 6%|▌ | 25/427 [01:03<16:47, 2.51s/it] Processing batches: 6%|▌ | 26/427 [01:07<20:28, 3.06s/it] Processing batches: 6%|▋ | 27/427 [01:09<17:47, 2.67s/it] Processing batches: 7%|▋ | 28/427 [01:11<16:05, 2.42s/it] Processing batches: 7%|▋ | 29/427 [01:13<15:01, 2.26s/it] Processing batches: 7%|▋ | 30/427 [01:15<15:48, 2.39s/it] Processing batches: 7%|▋ | 31/427 [01:17<14:41, 2.23s/it] Processing batches: 7%|▋ | 32/427 [01:22<19:05, 2.90s/it] Processing batches: 8%|▊ | 33/427 [01:26<21:39, 3.30s/it] Processing batches: 8%|▊ | 34/427 [01:30<23:37, 3.61s/it] Processing batches: 8%|▊ | 35/427 [01:32<19:50, 3.04s/it] Processing batches: 8%|▊ | 36/427 [01:34<17:11, 2.64s/it] Processing batches: 9%|▊ | 37/427 [01:38<20:25, 3.14s/it] Processing batches: 9%|▉ | 38/427 [01:41<19:50, 3.06s/it] Processing batches: 9%|▉ | 39/427 [01:42<16:20, 2.53s/it] Processing batches: 9%|▉ | 40/427 [01:44<15:33, 2.41s/it] Processing batches: 10%|▉ | 41/427 [01:46<14:24, 2.24s/it] Processing batches: 10%|▉ | 42/427 [01:47<12:30, 1.95s/it] Processing batches: 10%|█ | 43/427 [01:52<17:07, 2.68s/it] Processing batches: 10%|█ | 44/427 [01:56<20:13, 3.17s/it] Processing batches: 11%|█ | 45/427 [01:58<17:25, 2.74s/it] Processing batches: 11%|█ | 46/427 [02:00<15:23, 2.42s/it] Processing batches: 11%|█ | 47/427 [02:03<16:45, 2.65s/it] Processing batches: 11%|█ | 48/427 [02:07<19:53, 3.15s/it] Processing batches: 11%|█▏ | 49/427 [02:09<17:02, 2.71s/it] Processing batches: 12%|█▏ | 50/427 [02:12<18:46, 2.99s/it] Processing batches: 12%|█▏ | 51/427 [02:14<17:03, 2.72s/it] Processing batches: 12%|█▏ | 52/427 [02:17<16:09, 2.59s/it] Processing batches: 12%|█▏ | 53/427 [02:19<15:14, 2.45s/it] Processing batches: 13%|█▎ | 54/427 [02:21<14:15, 2.29s/it] Processing batches: 13%|█▎ | 55/427 [02:23<13:44, 2.22s/it] Processing batches: 13%|█▎ | 56/427 [02:24<12:33, 2.03s/it] Processing batches: 13%|█▎ | 57/427 [02:29<16:40, 2.70s/it] Processing batches: 14%|█▎ | 58/427 [02:30<14:40, 2.39s/it] Processing batches: 14%|█▍ | 59/427 [02:32<14:12, 2.32s/it] Processing batches: 14%|█▍ | 60/427 [02:36<16:45, 2.74s/it] Processing batches: 14%|█▍ | 61/427 [02:41<19:37, 3.22s/it] Processing batches: 15%|█▍ | 62/427 [02:42<16:54, 2.78s/it] Processing batches: 15%|█▍ | 63/427 [02:45<15:58, 2.63s/it] Processing batches: 15%|█▍ | 64/427 [02:48<17:39, 2.92s/it] Processing batches: 15%|█▌ | 65/427 [02:51<17:03, 2.83s/it] Processing batches: 15%|█▌ | 66/427 [02:53<15:36, 2.59s/it] Processing batches: 16%|█▌ | 67/427 [02:57<18:48, 3.13s/it] Processing batches: 16%|█▌ | 68/427 [02:59<16:53, 2.82s/it] Processing batches: 16%|█▌ | 69/427 [03:01<14:37, 2.45s/it] Processing batches: 16%|█▋ | 70/427 [03:05<17:51, 3.00s/it] Processing batches: 17%|█▋ | 71/427 [03:08<17:10, 2.89s/it] Processing batches: 17%|█▋ | 72/427 [03:12<19:26, 3.29s/it] Processing batches: 17%|█▋ | 73/427 [03:13<15:38, 2.65s/it] Processing batches: 17%|█▋ | 74/427 [03:16<15:12, 2.58s/it] Processing batches: 18%|█▊ | 75/427 [03:17<12:54, 2.20s/it] Processing batches: 18%|█▊ | 76/427 [03:19<12:51, 2.20s/it] Processing batches: 18%|█▊ | 77/427 [03:22<14:04, 2.41s/it] Processing batches: 18%|█▊ | 78/427 [03:25<14:19, 2.46s/it] Processing batches: 19%|█▊ | 79/427 [03:27<13:39, 2.35s/it] Processing batches: 19%|█▊ | 80/427 [03:29<13:04, 2.26s/it] Processing batches: 19%|█▉ | 81/427 [03:30<11:41, 2.03s/it] Processing batches: 19%|█▉ | 82/427 [03:32<11:23, 1.98s/it] Processing batches: 19%|█▉ | 83/427 [03:33<09:52, 1.72s/it] Processing batches: 20%|█▉ | 84/427 [03:36<10:49, 1.89s/it] Processing batches: 20%|█▉ | 85/427 [03:37<10:27, 1.83s/it] Processing batches: 20%|██ | 86/427 [03:40<12:50, 2.26s/it] Processing batches: 20%|██ | 87/427 [03:43<13:12, 2.33s/it] Processing batches: 21%|██ | 88/427 [03:45<13:28, 2.38s/it] Processing batches: 21%|██ | 89/427 [03:47<12:12, 2.17s/it] Processing batches: 21%|██ | 90/427 [03:51<15:51, 2.82s/it] Processing batches: 21%|██▏ | 91/427 [03:55<16:47, 3.00s/it] Processing batches: 22%|██▏ | 92/427 [03:58<16:10, 2.90s/it] Processing batches: 22%|██▏ | 93/427 [04:00<15:12, 2.73s/it] Processing batches: 22%|██▏ | 94/427 [04:02<14:53, 2.68s/it] Processing batches: 22%|██▏ | 95/427 [04:05<13:50, 2.50s/it] Processing batches: 22%|██▏ | 96/427 [04:06<12:38, 2.29s/it] Processing batches: 23%|██▎ | 97/427 [04:10<14:05, 2.56s/it] Processing batches: 23%|██▎ | 98/427 [04:12<14:06, 2.57s/it] Processing batches: 23%|██▎ | 99/427 [04:14<13:07, 2.40s/it] Processing batches: 23%|██▎ | 100/427 [04:17<13:03, 2.39s/it] Processing batches: 24%|██▎ | 101/427 [04:19<13:27, 2.48s/it] Processing batches: 24%|██▍ | 102/427 [04:24<16:29, 3.04s/it] Processing batches: 24%|██▍ | 103/427 [04:26<14:44, 2.73s/it] Processing batches: 24%|██▍ | 104/427 [04:28<14:35, 2.71s/it] Processing batches: 25%|██▍ | 105/427 [04:30<12:21, 2.30s/it] Processing batches: 25%|██▍ | 106/427 [04:31<10:39, 1.99s/it] Processing batches: 25%|██▌ | 107/427 [04:33<11:38, 2.18s/it] Processing batches: 25%|██▌ | 108/427 [04:38<14:49, 2.79s/it] Processing batches: 26%|██▌ | 109/427 [04:42<17:04, 3.22s/it] Processing batches: 26%|██▌ | 110/427 [04:45<16:06, 3.05s/it] Processing batches: 26%|██▌ | 111/427 [04:47<15:04, 2.86s/it] Processing batches: 26%|██▌ | 112/427 [04:51<16:57, 3.23s/it] Processing batches: 26%|██▋ | 113/427 [04:55<18:31, 3.54s/it] Processing batches: 27%|██▋ | 114/427 [04:59<19:24, 3.72s/it] Processing batches: 27%|██▋ | 115/427 [05:04<20:02, 3.86s/it] Processing batches: 27%|██▋ | 116/427 [05:08<20:42, 4.00s/it] Processing batches: 27%|██▋ | 117/427 [05:12<20:46, 4.02s/it] Processing batches: 28%|██▊ | 118/427 [05:16<21:14, 4.12s/it] Processing batches: 28%|██▊ | 119/427 [05:19<19:16, 3.75s/it] Processing batches: 28%|██▊ | 120/427 [05:21<16:27, 3.22s/it] Processing batches: 28%|██▊ | 121/427 [05:23<13:41, 2.68s/it] Processing batches: 29%|██▊ | 122/427 [05:25<12:20, 2.43s/it] Processing batches: 29%|██▉ | 123/427 [05:28<14:21, 2.83s/it] Processing batches: 29%|██▉ | 124/427 [05:29<11:45, 2.33s/it] Processing batches: 29%|██▉ | 125/427 [05:31<11:00, 2.19s/it] Processing batches: 30%|██▉ | 126/427 [05:36<14:09, 2.82s/it] Processing batches: 30%|██▉ | 127/427 [05:38<14:10, 2.84s/it] Processing batches: 30%|██▉ | 128/427 [05:43<16:22, 3.29s/it] Processing batches: 30%|███ | 129/427 [05:45<14:11, 2.86s/it] Processing batches: 30%|███ | 130/427 [05:47<13:22, 2.70s/it] Processing batches: 31%|███ | 131/427 [05:51<15:41, 3.18s/it] Processing batches: 31%|███ | 132/427 [05:54<14:38, 2.98s/it] Processing batches: 31%|███ | 133/427 [05:56<13:49, 2.82s/it] Processing batches: 31%|███▏ | 134/427 [05:58<11:27, 2.35s/it] Processing batches: 32%|███▏ | 135/427 [06:02<14:12, 2.92s/it] Processing batches: 32%|███▏ | 136/427 [06:03<12:02, 2.48s/it] Processing batches: 32%|███▏ | 137/427 [06:07<14:10, 2.93s/it] Processing batches: 32%|███▏ | 138/427 [06:10<13:58, 2.90s/it] Processing batches: 33%|███▎ | 139/427 [06:14<15:46, 3.28s/it] Processing batches: 33%|███▎ | 140/427 [06:18<17:01, 3.56s/it] Processing batches: 33%|███▎ | 141/427 [06:20<14:42, 3.09s/it] Processing batches: 33%|███▎ | 142/427 [06:23<13:19, 2.80s/it] Processing batches: 33%|███▎ | 143/427 [06:27<15:01, 3.17s/it] Processing batches: 34%|███▎ | 144/427 [06:31<16:05, 3.41s/it] Processing batches: 34%|███▍ | 145/427 [06:35<17:10, 3.66s/it] Processing batches: 34%|███▍ | 146/427 [06:39<17:55, 3.83s/it] Processing batches: 34%|███▍ | 147/427 [06:41<14:45, 3.16s/it] Processing batches: 35%|███▍ | 148/427 [06:45<16:21, 3.52s/it] Processing batches: 35%|███▍ | 149/427 [06:47<14:07, 3.05s/it] Processing batches: 35%|███▌ | 150/427 [06:49<12:38, 2.74s/it] Processing batches: 35%|███▌ | 151/427 [06:50<10:53, 2.37s/it] Processing batches: 36%|███▌ | 152/427 [06:53<10:25, 2.27s/it] Processing batches: 36%|███▌ | 153/427 [06:55<11:10, 2.45s/it] Processing batches: 36%|███▌ | 154/427 [07:00<13:57, 3.07s/it] Processing batches: 36%|███▋ | 155/427 [07:02<12:57, 2.86s/it] Processing batches: 37%|███▋ | 156/427 [07:07<14:50, 3.29s/it] Processing batches: 37%|███▋ | 157/427 [07:11<16:07, 3.58s/it] Processing batches: 37%|███▋ | 158/427 [07:15<16:54, 3.77s/it] Processing batches: 37%|███▋ | 159/427 [07:19<17:21, 3.89s/it] Processing batches: 37%|███▋ | 160/427 [07:23<17:00, 3.82s/it] Processing batches: 38%|███▊ | 161/427 [07:26<15:50, 3.57s/it] Processing batches: 38%|███▊ | 162/427 [07:29<15:41, 3.55s/it] Processing batches: 38%|███▊ | 163/427 [07:31<13:24, 3.05s/it] Processing batches: 38%|███▊ | 164/427 [07:35<14:44, 3.36s/it] Processing batches: 39%|███▊ | 165/427 [07:39<14:44, 3.38s/it] Processing batches: 39%|███▉ | 166/427 [07:43<15:36, 3.59s/it] Processing batches: 39%|███▉ | 167/427 [07:47<15:43, 3.63s/it] Processing batches: 39%|███▉ | 168/427 [07:51<16:39, 3.86s/it] Processing batches: 40%|███▉ | 169/427 [07:55<16:18, 3.79s/it] Processing batches: 40%|███▉ | 170/427 [07:59<16:44, 3.91s/it] Processing batches: 40%|████ | 171/427 [08:01<14:54, 3.50s/it] Processing batches: 40%|████ | 172/427 [08:04<13:41, 3.22s/it] Processing batches: 41%|████ | 173/427 [08:08<14:40, 3.47s/it] Processing batches: 41%|████ | 174/427 [08:10<12:50, 3.04s/it] Processing batches: 41%|████ | 175/427 [08:13<13:21, 3.18s/it] Processing batches: 41%|████ | 176/427 [08:18<14:53, 3.56s/it] Processing batches: 41%|████▏ | 177/427 [08:22<15:00, 3.60s/it] Processing batches: 42%|████▏ | 178/427 [08:25<14:22, 3.46s/it] Processing batches: 42%|████▏ | 179/427 [08:26<11:31, 2.79s/it] Processing batches: 42%|████▏ | 180/427 [08:28<11:10, 2.72s/it] Processing batches: 42%|████▏ | 181/427 [08:33<13:08, 3.20s/it] Processing batches: 43%|████▎ | 182/427 [08:35<12:07, 2.97s/it] Processing batches: 43%|████▎ | 183/427 [08:40<13:39, 3.36s/it] Processing batches: 43%|████▎ | 184/427 [08:41<11:55, 2.94s/it] Processing batches: 43%|████▎ | 185/427 [08:45<12:19, 3.06s/it] Processing batches: 44%|████▎ | 186/427 [08:48<12:30, 3.11s/it] Processing batches: 44%|████▍ | 187/427 [08:50<11:13, 2.81s/it] Processing batches: 44%|████▍ | 188/427 [08:53<11:06, 2.79s/it] Processing batches: 44%|████▍ | 189/427 [08:57<12:59, 3.28s/it] Processing batches: 44%|████▍ | 190/427 [09:02<14:20, 3.63s/it] Processing batches: 45%|████▍ | 191/427 [09:06<14:55, 3.79s/it] Processing batches: 45%|████▍ | 192/427 [09:10<15:26, 3.94s/it] Processing batches: 45%|████▌ | 193/427 [09:14<15:44, 4.04s/it] Processing batches: 45%|████▌ | 194/427 [09:19<15:54, 4.09s/it] Processing batches: 46%|████▌ | 195/427 [09:21<13:39, 3.53s/it] Processing batches: 46%|████▌ | 196/427 [09:23<12:19, 3.20s/it] Processing batches: 46%|████▌ | 197/427 [09:28<13:35, 3.55s/it] Processing batches: 46%|████▋ | 198/427 [09:32<14:24, 3.78s/it] Processing batches: 47%|████▋ | 199/427 [09:34<11:54, 3.13s/it] Processing batches: 47%|████▋ | 200/427 [09:35<10:18, 2.72s/it] Processing batches: 47%|████▋ | 201/427 [09:40<11:52, 3.15s/it] Processing batches: 47%|████▋ | 202/427 [09:44<13:14, 3.53s/it] Processing batches: 48%|████▊ | 203/427 [09:45<10:53, 2.92s/it] Processing batches: 48%|████▊ | 204/427 [09:47<09:07, 2.46s/it] Processing batches: 48%|████▊ | 205/427 [09:50<09:49, 2.66s/it] Processing batches: 48%|████▊ | 206/427 [09:54<11:19, 3.07s/it] Processing batches: 48%|████▊ | 207/427 [09:56<09:32, 2.60s/it] Processing batches: 49%|████▊ | 208/427 [09:58<09:12, 2.53s/it] Processing batches: 49%|████▉ | 209/427 [10:01<09:51, 2.71s/it] Processing batches: 49%|████▉ | 210/427 [10:03<09:31, 2.63s/it] Processing batches: 49%|████▉ | 211/427 [10:08<11:09, 3.10s/it] Processing batches: 50%|████▉ | 212/427 [10:09<09:44, 2.72s/it] Processing batches: 50%|████▉ | 213/427 [10:11<08:24, 2.36s/it] Processing batches: 50%|█████ | 214/427 [10:13<08:06, 2.28s/it] Processing batches: 50%|█████ | 215/427 [10:17<10:15, 2.90s/it] Processing batches: 51%|█████ | 216/427 [10:19<08:16, 2.35s/it] Processing batches: 51%|█████ | 217/427 [10:20<07:04, 2.02s/it] Processing batches: 51%|█████ | 218/427 [10:24<09:10, 2.63s/it] Processing batches: 51%|█████▏ | 219/427 [10:27<09:39, 2.79s/it] Processing batches: 52%|█████▏ | 220/427 [10:30<10:18, 2.99s/it] Processing batches: 52%|█████▏ | 221/427 [10:33<10:07, 2.95s/it] Processing batches: 52%|█████▏ | 222/427 [10:36<09:52, 2.89s/it] Processing batches: 52%|█████▏ | 223/427 [10:40<11:09, 3.28s/it] Processing batches: 52%|█████▏ | 224/427 [10:45<12:16, 3.63s/it] Processing batches: 53%|█████▎ | 225/427 [10:49<12:56, 3.84s/it] Processing batches: 53%|█████▎ | 226/427 [10:53<13:25, 4.01s/it] Processing batches: 53%|█████▎ | 227/427 [10:57<12:44, 3.82s/it] Processing batches: 53%|█████▎ | 228/427 [11:01<13:12, 3.98s/it] Processing batches: 54%|█████▎ | 229/427 [11:05<13:27, 4.08s/it] Processing batches: 54%|█████▍ | 230/427 [11:10<13:33, 4.13s/it] Processing batches: 54%|█████▍ | 231/427 [11:14<13:36, 4.17s/it] Processing batches: 54%|█████▍ | 232/427 [11:16<11:17, 3.48s/it] Processing batches: 55%|█████▍ | 233/427 [11:18<09:44, 3.01s/it] Processing batches: 55%|█████▍ | 234/427 [11:22<11:10, 3.47s/it] Processing batches: 55%|█████▌ | 235/427 [11:26<11:45, 3.68s/it] Processing batches: 55%|█████▌ | 236/427 [11:30<11:21, 3.57s/it] Processing batches: 56%|█████▌ | 237/427 [11:34<11:41, 3.69s/it] Processing batches: 56%|█████▌ | 238/427 [11:37<11:12, 3.56s/it] Processing batches: 56%|█████▌ | 239/427 [11:39<10:07, 3.23s/it] Processing batches: 56%|█████▌ | 240/427 [11:43<10:12, 3.27s/it] Processing batches: 56%|█████▋ | 241/427 [11:46<09:33, 3.08s/it] Processing batches: 57%|█████▋ | 242/427 [11:50<10:44, 3.48s/it] Processing batches: 57%|█████▋ | 243/427 [11:52<09:10, 2.99s/it] Processing batches: 57%|█████▋ | 244/427 [11:56<10:23, 3.41s/it] Processing batches: 57%|█████▋ | 245/427 [11:58<08:43, 2.88s/it] Processing batches: 58%|█████▊ | 246/427 [12:00<08:15, 2.74s/it] Processing batches: 58%|█████▊ | 247/427 [12:01<06:49, 2.27s/it] Processing batches: 58%|█████▊ | 248/427 [12:03<06:04, 2.04s/it] Processing batches: 58%|█████▊ | 249/427 [12:06<06:37, 2.23s/it] Processing batches: 59%|█████▊ | 250/427 [12:08<06:35, 2.24s/it] Processing batches: 59%|█████▉ | 251/427 [12:12<08:15, 2.81s/it] Processing batches: 59%|█████▉ | 252/427 [12:16<09:12, 3.15s/it] Processing batches: 59%|█████▉ | 253/427 [12:20<09:56, 3.43s/it] Processing batches: 59%|█████▉ | 254/427 [12:22<08:28, 2.94s/it] Processing batches: 60%|█████▉ | 255/427 [12:25<08:28, 2.96s/it] Processing batches: 60%|█████▉ | 256/427 [12:27<07:36, 2.67s/it] Processing batches: 60%|██████ | 257/427 [12:29<07:20, 2.59s/it] Processing batches: 60%|██████ | 258/427 [12:32<07:27, 2.65s/it] Processing batches: 61%|██████ | 259/427 [12:35<07:24, 2.64s/it] Processing batches: 61%|██████ | 260/427 [12:39<08:33, 3.07s/it] Processing batches: 61%|██████ | 261/427 [12:43<09:35, 3.47s/it] Processing batches: 61%|██████▏ | 262/427 [12:47<10:04, 3.67s/it] Processing batches: 62%|██████▏ | 263/427 [12:51<10:30, 3.84s/it] Processing batches: 62%|██████▏ | 264/427 [12:53<08:13, 3.03s/it] Processing batches: 62%|██████▏ | 265/427 [12:55<07:38, 2.83s/it] Processing batches: 62%|██████▏ | 266/427 [12:57<06:45, 2.52s/it] Processing batches: 63%|██████▎ | 267/427 [13:01<08:16, 3.11s/it] Processing batches: 63%|██████▎ | 268/427 [13:03<07:16, 2.75s/it] Processing batches: 63%|██████▎ | 269/427 [13:07<08:21, 3.17s/it] Processing batches: 63%|██████▎ | 270/427 [13:10<08:07, 3.11s/it] Processing batches: 63%|██████▎ | 271/427 [13:14<08:55, 3.43s/it] Processing batches: 64%|██████▎ | 272/427 [13:19<09:28, 3.67s/it] Processing batches: 64%|██████▍ | 273/427 [13:21<08:45, 3.41s/it] Processing batches: 64%|██████▍ | 274/427 [13:26<09:15, 3.63s/it] Processing batches: 64%|██████▍ | 275/427 [13:30<09:38, 3.80s/it] Processing batches: 65%|██████▍ | 276/427 [13:32<08:15, 3.28s/it] Processing batches: 65%|██████▍ | 277/427 [13:34<07:06, 2.84s/it] Processing batches: 65%|██████▌ | 278/427 [13:35<06:16, 2.53s/it] Processing batches: 65%|██████▌ | 279/427 [13:37<05:21, 2.17s/it] Processing batches: 66%|██████▌ | 280/427 [13:41<06:48, 2.78s/it] Processing batches: 66%|██████▌ | 281/427 [13:42<05:38, 2.32s/it] Processing batches: 66%|██████▌ | 282/427 [13:44<04:57, 2.05s/it] Processing batches: 66%|██████▋ | 283/427 [13:46<05:03, 2.10s/it] Processing batches: 67%|██████▋ | 284/427 [13:47<04:27, 1.87s/it] Processing batches: 67%|██████▋ | 285/427 [13:52<06:10, 2.61s/it] Processing batches: 67%|██████▋ | 286/427 [13:54<05:52, 2.50s/it] Processing batches: 67%|██████▋ | 287/427 [13:55<05:06, 2.19s/it] Processing batches: 67%|██████▋ | 288/427 [14:00<06:30, 2.81s/it] Processing batches: 68%|██████▊ | 289/427 [14:04<07:21, 3.20s/it] Processing batches: 68%|██████▊ | 290/427 [14:06<06:55, 3.04s/it] Processing batches: 68%|██████▊ | 291/427 [14:11<07:46, 3.43s/it] Processing batches: 68%|██████▊ | 292/427 [14:15<08:18, 3.69s/it] Processing batches: 69%|██████▊ | 293/427 [14:20<08:51, 3.97s/it] Processing batches: 69%|██████▉ | 294/427 [14:21<07:24, 3.34s/it] Processing batches: 69%|██████▉ | 295/427 [14:24<06:53, 3.13s/it] Processing batches: 69%|██████▉ | 296/427 [14:28<07:19, 3.36s/it] Processing batches: 70%|██████▉ | 297/427 [14:31<07:15, 3.35s/it] Processing batches: 70%|██████▉ | 298/427 [14:33<06:21, 2.96s/it] Processing batches: 70%|███████ | 299/427 [14:35<05:38, 2.64s/it] Processing batches: 70%|███████ | 300/427 [14:37<05:17, 2.50s/it] Processing batches: 70%|███████ | 301/427 [14:41<06:03, 2.89s/it] Processing batches: 71%|███████ | 302/427 [14:43<05:31, 2.65s/it] Processing batches: 71%|███████ | 303/427 [14:45<05:10, 2.50s/it] Processing batches: 71%|███████ | 304/427 [14:48<05:10, 2.53s/it] Processing batches: 71%|███████▏ | 305/427 [14:51<05:23, 2.65s/it] Processing batches: 72%|███████▏ | 306/427 [14:54<05:17, 2.63s/it] Processing batches: 72%|███████▏ | 307/427 [14:58<06:19, 3.16s/it] Processing batches: 72%|███████▏ | 308/427 [15:01<06:09, 3.11s/it] Processing batches: 72%|███████▏ | 309/427 [15:05<06:44, 3.43s/it] Processing batches: 73%|███████▎ | 310/427 [15:08<06:35, 3.38s/it] Processing batches: 73%|███████▎ | 311/427 [15:10<05:33, 2.88s/it] Processing batches: 73%|███████▎ | 312/427 [15:14<06:17, 3.28s/it] Processing batches: 73%|███████▎ | 313/427 [15:18<06:16, 3.30s/it] Processing batches: 74%|███████▎ | 314/427 [15:20<05:23, 2.86s/it] Processing batches: 74%|███████▍ | 315/427 [15:24<06:07, 3.28s/it] Processing batches: 74%|███████▍ | 316/427 [15:28<06:32, 3.54s/it] Processing batches: 74%|███████▍ | 317/427 [15:32<06:51, 3.74s/it] Processing batches: 74%|███████▍ | 318/427 [15:36<06:36, 3.64s/it] Processing batches: 75%|███████▍ | 319/427 [15:38<05:51, 3.26s/it] Processing batches: 75%|███████▍ | 320/427 [15:40<05:15, 2.95s/it] Processing batches: 75%|███████▌ | 321/427 [15:44<05:53, 3.34s/it] Processing batches: 75%|███████▌ | 322/427 [15:46<04:58, 2.84s/it] Processing batches: 76%|███████▌ | 323/427 [15:49<04:47, 2.77s/it] Processing batches: 76%|███████▌ | 324/427 [15:53<05:26, 3.17s/it] Processing batches: 76%|███████▌ | 325/427 [15:56<05:10, 3.04s/it] Processing batches: 76%|███████▋ | 326/427 [16:00<05:46, 3.43s/it] Processing batches: 77%|███████▋ | 327/427 [16:03<05:24, 3.25s/it] Processing batches: 77%|███████▋ | 328/427 [16:05<04:47, 2.91s/it] Processing batches: 77%|███████▋ | 329/427 [16:06<04:00, 2.45s/it] Processing batches: 77%|███████▋ | 330/427 [16:09<04:02, 2.50s/it] Processing batches: 78%|███████▊ | 331/427 [16:12<04:08, 2.58s/it] Processing batches: 78%|███████▊ | 332/427 [16:15<04:33, 2.88s/it] Processing batches: 78%|███████▊ | 333/427 [16:19<05:08, 3.28s/it] Processing batches: 78%|███████▊ | 334/427 [16:24<05:33, 3.58s/it] Processing batches: 78%|███████▊ | 335/427 [16:28<05:43, 3.73s/it] Processing batches: 79%|███████▊ | 336/427 [16:32<05:52, 3.87s/it] Processing batches: 79%|███████▉ | 337/427 [16:35<05:38, 3.76s/it] Processing batches: 79%|███████▉ | 338/427 [16:39<05:22, 3.62s/it] Processing batches: 79%|███████▉ | 339/427 [16:42<05:13, 3.56s/it] Processing batches: 80%|███████▉ | 340/427 [16:46<05:25, 3.74s/it] Processing batches: 80%|███████▉ | 341/427 [16:50<05:28, 3.82s/it] Processing batches: 80%|████████ | 342/427 [16:54<05:18, 3.75s/it] Processing batches: 80%|████████ | 343/427 [16:56<04:36, 3.29s/it] Processing batches: 81%|████████ | 344/427 [16:58<04:02, 2.92s/it] Processing batches: 81%|████████ | 345/427 [17:00<03:33, 2.60s/it] Processing batches: 81%|████████ | 346/427 [17:04<04:07, 3.06s/it] Processing batches: 81%|████████▏ | 347/427 [17:07<03:54, 2.94s/it] Processing batches: 81%|████████▏ | 348/427 [17:09<03:23, 2.58s/it] Processing batches: 82%|████████▏ | 349/427 [17:12<03:50, 2.95s/it] Processing batches: 82%|████████▏ | 350/427 [17:15<03:29, 2.71s/it] Processing batches: 82%|████████▏ | 351/427 [17:18<03:54, 3.08s/it] Processing batches: 82%|████████▏ | 352/427 [17:22<03:54, 3.13s/it] Processing batches: 83%|████████▎ | 353/427 [17:26<04:17, 3.48s/it] Processing batches: 83%|████████▎ | 354/427 [17:30<04:30, 3.70s/it] Processing batches: 83%|████████▎ | 355/427 [17:34<04:37, 3.86s/it] Processing batches: 83%|████████▎ | 356/427 [17:39<04:41, 3.96s/it] Processing batches: 84%|████████▎ | 357/427 [17:42<04:33, 3.90s/it] Processing batches: 84%|████████▍ | 358/427 [17:47<04:37, 4.02s/it] Processing batches: 84%|████████▍ | 359/427 [17:48<03:47, 3.35s/it] Processing batches: 84%|████████▍ | 360/427 [17:51<03:30, 3.14s/it] Processing batches: 85%|████████▍ | 361/427 [17:53<03:01, 2.74s/it] Processing batches: 85%|████████▍ | 362/427 [17:56<03:08, 2.91s/it] Processing batches: 85%|████████▌ | 363/427 [17:58<02:50, 2.66s/it] Processing batches: 85%|████████▌ | 364/427 [18:01<02:47, 2.66s/it] Processing batches: 85%|████████▌ | 365/427 [18:05<03:10, 3.08s/it] Processing batches: 86%|████████▌ | 366/427 [18:07<02:54, 2.86s/it] Processing batches: 86%|████████▌ | 367/427 [18:10<02:51, 2.86s/it] Processing batches: 86%|████████▌ | 368/427 [18:14<03:09, 3.21s/it] Processing batches: 86%|████████▋ | 369/427 [18:18<03:23, 3.50s/it] Processing batches: 87%|████████▋ | 370/427 [18:23<03:33, 3.75s/it] Processing batches: 87%|████████▋ | 371/427 [18:27<03:37, 3.89s/it] Processing batches: 87%|████████▋ | 372/427 [18:29<02:57, 3.23s/it] Processing batches: 87%|████████▋ | 373/427 [18:33<03:08, 3.49s/it] Processing batches: 88%|████████▊ | 374/427 [18:37<03:18, 3.74s/it] Processing batches: 88%|████████▊ | 375/427 [18:40<02:54, 3.35s/it] Processing batches: 88%|████████▊ | 376/427 [18:43<02:46, 3.27s/it] Processing batches: 88%|████████▊ | 377/427 [18:46<02:44, 3.28s/it] Processing batches: 89%|████████▊ | 378/427 [18:48<02:17, 2.81s/it] Processing batches: 89%|████████▉ | 379/427 [18:50<02:05, 2.62s/it] Processing batches: 89%|████████▉ | 380/427 [18:52<01:59, 2.55s/it] Processing batches: 89%|████████▉ | 381/427 [18:56<02:19, 3.03s/it] Processing batches: 89%|████████▉ | 382/427 [18:58<01:56, 2.58s/it] Processing batches: 90%|████████▉ | 383/427 [19:02<02:15, 3.08s/it] Processing batches: 90%|████████▉ | 384/427 [19:05<02:10, 3.03s/it] Processing batches: 90%|█████████ | 385/427 [19:09<02:20, 3.35s/it] Processing batches: 90%|█████████ | 386/427 [19:12<02:07, 3.12s/it] Processing batches: 91%|█████████ | 387/427 [19:14<01:53, 2.84s/it] Processing batches: 91%|█████████ | 388/427 [19:16<01:40, 2.58s/it] Processing batches: 91%|█████████ | 389/427 [19:20<01:55, 3.03s/it] Processing batches: 91%|█████████▏| 390/427 [19:22<01:36, 2.62s/it] Processing batches: 92%|█████████▏| 391/427 [19:26<01:48, 3.01s/it] Processing batches: 92%|█████████▏| 392/427 [19:30<01:55, 3.31s/it] Processing batches: 92%|█████████▏| 393/427 [19:31<01:31, 2.68s/it] Processing batches: 92%|█████████▏| 394/427 [19:35<01:44, 3.16s/it] Processing batches: 93%|█████████▎| 395/427 [19:38<01:36, 3.03s/it] Processing batches: 93%|█████████▎| 396/427 [19:42<01:45, 3.39s/it] Processing batches: 93%|█████████▎| 397/427 [19:46<01:47, 3.59s/it] Processing batches: 93%|█████████▎| 398/427 [19:48<01:33, 3.21s/it] Processing batches: 93%|█████████▎| 399/427 [19:51<01:22, 2.95s/it] Processing batches: 94%|█████████▎| 400/427 [19:55<01:28, 3.29s/it] Processing batches: 94%|█████████▍| 401/427 [19:59<01:32, 3.56s/it] Processing batches: 94%|█████████▍| 402/427 [20:03<01:32, 3.69s/it] Processing batches: 94%|█████████▍| 403/427 [20:07<01:32, 3.84s/it] Processing batches: 95%|█████████▍| 404/427 [20:10<01:21, 3.56s/it] Processing batches: 95%|█████████▍| 405/427 [20:13<01:13, 3.35s/it] Processing batches: 95%|█████████▌| 406/427 [20:15<01:04, 3.05s/it] Processing batches: 95%|█████████▌| 407/427 [20:18<00:56, 2.84s/it] Processing batches: 96%|█████████▌| 408/427 [20:22<01:00, 3.16s/it] Processing batches: 96%|█████████▌| 409/427 [20:26<01:01, 3.43s/it] Processing batches: 96%|█████████▌| 410/427 [20:30<01:01, 3.60s/it] Processing batches: 96%|█████████▋| 411/427 [20:34<01:00, 3.78s/it] Processing batches: 96%|█████████▋| 412/427 [20:37<00:51, 3.45s/it] Processing batches: 97%|█████████▋| 413/427 [20:39<00:44, 3.15s/it] Processing batches: 97%|█████████▋| 414/427 [20:41<00:38, 2.95s/it] Processing batches: 97%|█████████▋| 415/427 [20:46<00:39, 3.31s/it] Processing batches: 97%|█████████▋| 416/427 [20:50<00:39, 3.57s/it] Processing batches: 98%|█████████▊| 417/427 [20:52<00:32, 3.29s/it] Processing batches: 98%|█████████▊| 418/427 [20:55<00:27, 3.01s/it] Processing batches: 98%|█████████▊| 419/427 [20:57<00:21, 2.68s/it] Processing batches: 98%|█████████▊| 420/427 [20:59<00:18, 2.61s/it] Processing batches: 99%|█████████▊| 421/427 [21:03<00:18, 3.09s/it] Processing batches: 99%|█████████▉| 422/427 [21:07<00:16, 3.35s/it] Processing batches: 99%|█████████▉| 423/427 [21:10<00:12, 3.16s/it] Processing batches: 99%|█████████▉| 424/427 [21:12<00:08, 2.87s/it] Processing batches: 100%|█████████▉| 425/427 [21:16<00:06, 3.22s/it] Processing batches: 100%|█████████▉| 426/427 [21:20<00:03, 3.45s/it] Processing batches: 100%|██████████| 427/427 [21:20<00:00, 2.47s/it] Processing batches: 100%|██████████| 427/427 [21:20<00:00, 3.00s/it] ================================ Run params : {'2_nD__rkey': 'bert', '2_nD__rnum_dims': 768, '2_nD__rnormalize': True, '2_nD__rfamily': 'all-MiniLM-L6-v2', '2_nD__rmax_length': 256} -------------------------------- Scoring params : {'2_nD__smin_score': 30, '2_nD__srecompute': True, '2_nD__ssample_size': None, '2_nD__sn_neighbors': 10, '2_nD__srandom_state': 42} ================================ ----------- Scores ----------- 2_nD__neighbors_articles_authors : 0.5509 --------- Desc Scores --------- 2_nD__neighbors_articles_authors_det 0 Céline Gicquel : 0.86 1 Loïc Paulevé : 0.79 2 Nikolaus Hansen : 0.76 3 Dimo Brockhoff : 0.75 4 Anne Auger : 0.68 5 Tobias Isenberg : 0.67 6 Paola Tubaro : 0.67 7 Yann Ponty : 0.66 8 Ioana Manolescu : 0.66 9 Isabelle Guyon : 0.66 10 Franck Cappello : 0.65 11 Marc Baboulin : 0.65 12 Cyril Furtlehner : 0.64 13 Nicolas Bredeche : 0.64 14 Sébastien Tixeuil : 0.62 15 Fatiha Saïs : 0.62 16 François Goasdoué : 0.62 17 Philippe Caillou : 0.62 18 Nathalie Pernelle : 0.62 19 Jean-Daniel Fekete : 0.60 20 Petra Isenberg : 0.59 21 Olivier Teytaud : 0.58 22 Albert Cohen : 0.57 23 Sarah Cohen-Boulakia : 0.56 24 Guillaume Melquiond : 0.56 25 Marc Schoenauer : 0.56 26 Guillaume Charpiat : 0.56 27 Sylvie Boldo : 0.56 28 Fatiha Zaidi : 0.55 29 Evelyne Lutton : 0.54 30 Raymond Ros : 0.54 31 Claude Marché : 0.53 32 Lonni Besançon : 0.52 33 Nathann Cohen : 0.52 34 Chantal Reynaud : 0.51 35 Michèle Sebag : 0.43 36 Steven Martin : 0.42 37 Michel Beaudouin-Lafon : 0.41 38 Balázs Kégl : 0.40 39 Olivier Chapuis : 0.40 40 Pierre Dragicevic : 0.39 41 Emmanuel Pietriga : 0.36 42 Alain Denise : 0.36 43 Wendy Mackay : 0.34 44 Caroline Appert : 0.34 45 Anastasia Bezerianos : 0.34 46 Johanne Cohen : 0.33 47 Wendy E. Mackay : 0.23 dtype: object VALUE : 0.5509 --------- Raw Scores --------- 2_nD__neighbors_articles_authors Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 dtype: float64 ================================ Run params : {'3_2D__rkey': 'umap', '3_2D__rmetric': 'euclidean', '3_2D__rn_neighbors': 10, '3_2D__rmin_dist': 0.1, '3_2D__rinit': 'random', '3_2D__rlearning_rate': 1.0, '3_2D__rn_epochs': None, '3_2D__rrandom_state': None} -------------------------------- Scoring params : {'3_2D__smin_score': 30, '3_2D__srecompute': True, '3_2D__ssample_size': None, '3_2D__sn_neighbors': 10, '3_2D__srandom_state': 42, '3_2D__smetric': 'euclidean'} ================================ ----------- Scores ----------- 3_2D__neighbors_articles_authors : 0.5361 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_mean : -0.0148 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_median : -0.0111 3_2D__trustworthiness_sklearn : 0.9486 --------- Desc Scores --------- 3_2D__neighbors_articles_authors_det 0 Céline Gicquel : 0.79 1 Loïc Paulevé : 0.76 2 Isabelle Guyon : 0.75 3 Nikolaus Hansen : 0.74 4 Sébastien Tixeuil : 0.70 5 Paola Tubaro : 0.69 6 Dimo Brockhoff : 0.67 7 Franck Cappello : 0.66 8 Sarah Cohen-Boulakia : 0.66 9 Cyril Furtlehner : 0.66 10 Tobias Isenberg : 0.64 11 Fatiha Zaidi : 0.64 12 Yann Ponty : 0.64 13 Albert Cohen : 0.63 14 Nathalie Pernelle : 0.62 15 Fatiha Saïs : 0.61 16 Marc Baboulin : 0.59 17 Nicolas Bredeche : 0.58 18 Guillaume Charpiat : 0.58 19 Ioana Manolescu : 0.58 20 Olivier Teytaud : 0.58 21 Marc Schoenauer : 0.57 22 Raymond Ros : 0.57 23 Chantal Reynaud : 0.57 24 Philippe Caillou : 0.57 25 Petra Isenberg : 0.56 26 Anne Auger : 0.54 27 Jean-Daniel Fekete : 0.52 28 Lonni Besançon : 0.52 29 Sylvie Boldo : 0.49 30 Claude Marché : 0.49 31 François Goasdoué : 0.48 32 Michèle Sebag : 0.48 33 Nathann Cohen : 0.48 34 Guillaume Melquiond : 0.46 35 Steven Martin : 0.45 36 Evelyne Lutton : 0.44 37 Balázs Kégl : 0.41 38 Michel Beaudouin-Lafon : 0.40 39 Olivier Chapuis : 0.39 40 Caroline Appert : 0.37 41 Alain Denise : 0.37 42 Anastasia Bezerianos : 0.34 43 Pierre Dragicevic : 0.33 44 Emmanuel Pietriga : 0.32 45 Wendy Mackay : 0.29 46 Johanne Cohen : 0.28 47 Wendy E. Mackay : 0.27 dtype: object VALUE : 0.5361 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_det 0 François Goasdoué : -0.14 1 Anne Auger : -0.14 2 Guillaume Melquiond : -0.11 3 Evelyne Lutton : -0.10 4 Jean-Daniel Fekete : -0.08 5 Ioana Manolescu : -0.08 6 Dimo Brockhoff : -0.08 7 Céline Gicquel : -0.07 8 Sylvie Boldo : -0.06 9 Pierre Dragicevic : -0.06 10 Marc Baboulin : -0.06 11 Philippe Caillou : -0.05 12 Wendy Mackay : -0.05 13 Nicolas Bredeche : -0.05 14 Johanne Cohen : -0.04 15 Nathann Cohen : -0.04 16 Emmanuel Pietriga : -0.04 17 Claude Marché : -0.04 18 Petra Isenberg : -0.04 19 Tobias Isenberg : -0.03 20 Yann Ponty : -0.03 21 Loïc Paulevé : -0.03 22 Nikolaus Hansen : -0.03 23 Michel Beaudouin-Lafon : -0.01 24 Fatiha Saïs : -0.01 25 Olivier Chapuis : -0.01 26 Lonni Besançon : -0.01 27 Anastasia Bezerianos : 0.00 28 Olivier Teytaud : 0.00 29 Balázs Kégl : 0.01 30 Nathalie Pernelle : 0.01 31 Alain Denise : 0.01 32 Franck Cappello : 0.01 33 Cyril Furtlehner : 0.01 34 Marc Schoenauer : 0.02 35 Paola Tubaro : 0.02 36 Guillaume Charpiat : 0.02 37 Caroline Appert : 0.03 38 Steven Martin : 0.03 39 Raymond Ros : 0.04 40 Wendy E. Mackay : 0.04 41 Michèle Sebag : 0.05 42 Chantal Reynaud : 0.06 43 Albert Cohen : 0.06 44 Sébastien Tixeuil : 0.07 45 Fatiha Zaidi : 0.09 46 Isabelle Guyon : 0.09 47 Sarah Cohen-Boulakia : 0.09 dtype: object VALUE : -0.0148 --------- Raw Scores --------- 3_2D__neighbors_articles_authors Sébastien Tixeuil 0.695745 Michèle Sebag 0.483212 Johanne Cohen 0.281395 Albert Cohen 0.625862 Wendy E. Mackay 0.269565 Philippe Caillou 0.565116 Alain Denise 0.366667 Jean-Daniel Fekete 0.520755 Emmanuel Pietriga 0.323810 Yann Ponty 0.636364 Marc Schoenauer 0.574820 Franck Cappello 0.663415 Caroline Appert 0.371739 Michel Beaudouin-Lafon 0.401235 Wendy Mackay 0.291489 Anne Auger 0.541772 Evelyne Lutton 0.443243 Pierre Dragicevic 0.334568 Ioana Manolescu 0.579268 Nikolaus Hansen 0.735802 Nicolas Bredeche 0.582353 Olivier Teytaud 0.578302 François Goasdoué 0.484906 Nathalie Pernelle 0.623529 Fatiha Saïs 0.612195 Sarah Cohen-Boulakia 0.657576 Claude Marché 0.489362 Chantal Reynaud 0.570000 Olivier Chapuis 0.390385 Steven Martin 0.451282 Fatiha Zaidi 0.637500 Balázs Kégl 0.407895 Paola Tubaro 0.689744 Raymond Ros 0.573529 Cyril Furtlehner 0.656410 Anastasia Bezerianos 0.337313 Sylvie Boldo 0.494286 Guillaume Melquiond 0.457576 Marc Baboulin 0.593333 Dimo Brockhoff 0.671795 Nathann Cohen 0.478049 Petra Isenberg 0.555140 Tobias Isenberg 0.637607 Loïc Paulevé 0.759524 Céline Gicquel 0.789474 Isabelle Guyon 0.747191 Guillaume Charpiat 0.580645 Lonni Besançon 0.518182 dtype: float64 3_2D__2_nD_3_2D_neighbors_articles_authors_dim 2_nD__neighbors_articles_authors \ Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 3_2D__neighbors_articles_authors scdec Sébastien Tixeuil 0.695745 0.072340 Michèle Sebag 0.483212 0.050365 Johanne Cohen 0.281395 -0.044186 Albert Cohen 0.625862 0.060345 Wendy E. Mackay 0.269565 0.039130 Philippe Caillou 0.565116 -0.053488 Alain Denise 0.366667 0.008333 Jean-Daniel Fekete 0.520755 -0.080503 Emmanuel Pietriga 0.323810 -0.039683 Yann Ponty 0.636364 -0.027273 Marc Schoenauer 0.574820 0.015827 Franck Cappello 0.663415 0.012195 Caroline Appert 0.371739 0.030435 Michel Beaudouin-Lafon 0.401235 -0.012346 Wendy Mackay 0.291489 -0.053191 Anne Auger 0.541772 -0.135443 Evelyne Lutton 0.443243 -0.097297 Pierre Dragicevic 0.334568 -0.058025 Ioana Manolescu 0.579268 -0.080488 Nikolaus Hansen 0.735802 -0.025926 Nicolas Bredeche 0.582353 -0.052941 Olivier Teytaud 0.578302 0.002830 François Goasdoué 0.484906 -0.135849 Nathalie Pernelle 0.623529 0.005882 Fatiha Saïs 0.612195 -0.009756 Sarah Cohen-Boulakia 0.657576 0.093939 Claude Marché 0.489362 -0.038298 Chantal Reynaud 0.570000 0.060000 Olivier Chapuis 0.390385 -0.007692 Steven Martin 0.451282 0.033333 Fatiha Zaidi 0.637500 0.087500 Balázs Kégl 0.407895 0.005263 Paola Tubaro 0.689744 0.017949 Raymond Ros 0.573529 0.038235 Cyril Furtlehner 0.656410 0.012821 Anastasia Bezerianos 0.337313 0.001493 Sylvie Boldo 0.494286 -0.062857 Guillaume Melquiond 0.457576 -0.106061 Marc Baboulin 0.593333 -0.057778 Dimo Brockhoff 0.671795 -0.079487 Nathann Cohen 0.478049 -0.041463 Petra Isenberg 0.555140 -0.037383 Tobias Isenberg 0.637607 -0.034188 Loïc Paulevé 0.759524 -0.026190 Céline Gicquel 0.789474 -0.068421 Isabelle Guyon 0.747191 0.091011 Guillaume Charpiat 0.580645 0.022581 Lonni Besançon 0.518182 -0.006061 Nothing in cache, initial Fitting with min_cluster_size=15 Found 68 clusters in 0.27051661399991644s Max Fitting with min_cluster_size=30 Found 30 clusters in 0.10185916199998246s Max Fitting with min_cluster_size=60 Found 16 clusters in 0.10068853400025546s Max Fitting with min_cluster_size=120 Found 10 clusters in 0.09500915999979043s Max Fitting with min_cluster_size=240 Found 6 clusters in 0.08926718299971981s Midpoint Fitting with min_cluster_size=180 Found 9 clusters in 0.09352127000011023s Midpoint Fitting with min_cluster_size=210 Found 6 clusters in 0.09090468000022156s Midpoint Fitting with min_cluster_size=195 Found 8 clusters in 0.09274479600026098s No need Re-Fitting with min_cluster_size=195 Clusters cached: [6, 6, 8, 9, 10, 16, 30, 68] Nothing in cache, initial Fitting with min_cluster_size=15 Found 68 clusters in 0.10702741499972035s Max Fitting with min_cluster_size=30 Found 30 clusters in 0.1014529180001773s Max Fitting with min_cluster_size=60 Found 16 clusters in 0.10032602599994789s Midpoint Fitting with min_cluster_size=45 Found 21 clusters in 0.09942904999979874s Midpoint Fitting with min_cluster_size=37 Found 23 clusters in 0.09883285599971714s Re-Fitting with min_cluster_size=30 Found 30 clusters in 0.10167597100007697s Clusters cached: [16, 21, 23, 30, 68] Cluster 11 does not have enough number of labels! Number of labels in cluster 1. Warning: Less than 2 words in cluster 11 with (1) words. ================================ Run params : {'4_clus__rkey': 'hdbscan', '4_clus__rbase_factor': 3} -------------------------------- Scoring params : {'4_clus__siter_stab': 2, '4_clus__sremove_stab': [0, 0.01, 0.03, 0.1, 0.25], '4_clus__smetric': 'euclidean', '4_clus__srandom_state': None} ================================ ----------- Scores ----------- 4_clus__nb_clust_0 : 8.0000 4_clus__silhouette_0 : 0.3524 4_clus__avg_word_couv_0 : 0.4317 4_clus__med_word_couv_0 : 0.4484 4_clus__avg_word_couv_minus_0 : 0.4105 4_clus__big_small_ratio_0 : 4.5149 4_clus__stab_clus_0 : 0.4875 4_clus__nb_clust_1 : 24.0000 4_clus__silhouette_1 : 0.2507 4_clus__avg_word_couv_1 : 0.5749 4_clus__med_word_couv_1 : 0.5336 4_clus__avg_word_couv_minus_1 : 0.5567 4_clus__big_small_ratio_1 : 35.3667 4_clus__stab_clus_1 : 0.1667 4_clus__avg_stab_avg : 0.3271 4_clus__avg_couv_avg : 0.5033 4_clus__clu_score : 0.4152 --------- Desc Scores --------- 4_clus__clus_eval_pos_0_det 0 visualizations, human interaction : s 912 stb ... 1 optimization control, stochastic : s 738 stb 0... 2 cluster computing, networking internet archite... 3 logic science, verification : s 340 stb 0.50 +... 4 query, ontology : s 338 stb 0.50 + 0.57 - 0.03 5 discrete mathematics, combinatorics : s 266 st... 6 quantitative methods, bioinformatics : s 209 s... 7 natural language, french : s 202 stb 0.30 + 0.... dtype: object VALUE : 0.4317 4_clus__clus_eval_pos_1_det 0 programs, specification : s 340 stb 0.00 + 0.4... 1 information visualization, cognitive science :... 2 display, reality : s 188 stb 0.40 + 0.48 - 0.02 3 secondary structure, protein : s 174 stb 0.00 ... 4 natural language processing, computation langu... 5 architectures, parallelism : s 142 stb 0.80 + ... 6 vertices, minimum : s 134 stb 0.30 + 0.58 - 0.03 7 fluid, numerical simulations : s 133 stb 0.80 ... 8 internet architecture, cloud radio access netw... 9 ontologies, linked : s 121 stb 0.00 + 0.50 - 0.03 10 queries, updates : s 117 stb 0.00 + 0.74 - 0.03 11 evolution strategies, multi objective : s 113 ... 12 biological, metabolic : s 110 stb 0.50 + 0.60 ... 13 covariance matrix adaptation, benchmarking : s... 14 fault, cloud computing : s 99 stb 0.00 + 0.49 ... 15 music, designers : s 94 stb 0.00 + 0.48 - 0.01 16 mixed integer linear, numerical results : s 91... 17 discrete, permutations : s 76 stb 0.00 + 0.49 ... 18 automl, training : s 69 stb 0.00 + 0.55 - 0.03 19 stabilizing, population protocols : s 67 stb 0... 20 belief propagation, condensed matter : s 66 st... 21 discrete event systems, causal : s 60 stb 0.20... 22 social sciences, social network : s 56 stb 0.0... 23 robotics, electric power : s 52 stb 0.40 + 0.6... 24 linear systems, factorization : s 43 stb 0.00 ... 25 monte carlo search, games : s 41 stb 0.00 + 0.... 26 french language, signing : s 35 stb 0.30 + 0.6... 27 multi armed, bandit : s 34 stb 0.00 + 0.94 - 0.02 28 information retrieval, wikipedia : s 31 stb 0.... 29 planning, matrix factorization : s 30 stb 0.00... dtype: object VALUE : 0.5749 --------- Raw Scores --------- ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score'] ================================ Run params : {} -------------------------------- Scoring params : {'6_pst__sname_list': ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score']} ================================ ----------- Scores ----------- 6_pst__final_score : 0.5007 --------- Desc Scores --------- 6_pst__final_score_det 0 2_nD__neighbors_articles_authors : 0.55 1 3_2D__neighbors_articles_authors : 0.54 2 4_clus__clu_score : 0.42 dtype: object VALUE : 0.5007 --------- Raw Scores --------- ================================ Run params : {'2_nD__rkey': 'bert', '2_nD__rnum_dims': 768, '2_nD__rnormalize': True, '2_nD__rfamily': 'all-MiniLM-L6-v2', '2_nD__rmax_length': 256} -------------------------------- Scoring params : {'2_nD__smin_score': 30, '2_nD__srecompute': True, '2_nD__ssample_size': None, '2_nD__sn_neighbors': 10, '2_nD__srandom_state': 42} ================================ ----------- Scores ----------- 2_nD__neighbors_articles_authors : 0.5509 --------- Desc Scores --------- 2_nD__neighbors_articles_authors_det 0 Céline Gicquel : 0.86 1 Loïc Paulevé : 0.79 2 Nikolaus Hansen : 0.76 3 Dimo Brockhoff : 0.75 4 Anne Auger : 0.68 5 Tobias Isenberg : 0.67 6 Paola Tubaro : 0.67 7 Yann Ponty : 0.66 8 Ioana Manolescu : 0.66 9 Isabelle Guyon : 0.66 10 Franck Cappello : 0.65 11 Marc Baboulin : 0.65 12 Cyril Furtlehner : 0.64 13 Nicolas Bredeche : 0.64 14 Sébastien Tixeuil : 0.62 15 Fatiha Saïs : 0.62 16 François Goasdoué : 0.62 17 Philippe Caillou : 0.62 18 Nathalie Pernelle : 0.62 19 Jean-Daniel Fekete : 0.60 20 Petra Isenberg : 0.59 21 Olivier Teytaud : 0.58 22 Albert Cohen : 0.57 23 Sarah Cohen-Boulakia : 0.56 24 Guillaume Melquiond : 0.56 25 Marc Schoenauer : 0.56 26 Guillaume Charpiat : 0.56 27 Sylvie Boldo : 0.56 28 Fatiha Zaidi : 0.55 29 Evelyne Lutton : 0.54 30 Raymond Ros : 0.54 31 Claude Marché : 0.53 32 Lonni Besançon : 0.52 33 Nathann Cohen : 0.52 34 Chantal Reynaud : 0.51 35 Michèle Sebag : 0.43 36 Steven Martin : 0.42 37 Michel Beaudouin-Lafon : 0.41 38 Balázs Kégl : 0.40 39 Olivier Chapuis : 0.40 40 Pierre Dragicevic : 0.39 41 Emmanuel Pietriga : 0.36 42 Alain Denise : 0.36 43 Wendy Mackay : 0.34 44 Caroline Appert : 0.34 45 Anastasia Bezerianos : 0.34 46 Johanne Cohen : 0.33 47 Wendy E. Mackay : 0.23 Name: 0, dtype: object VALUE : 0.5509 --------- Raw Scores --------- 2_nD__neighbors_articles_authors Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 Name: 0, dtype: float64 ================================ Run params : {'3_2D__rkey': 'umap', '3_2D__rmetric': 'euclidean', '3_2D__rn_neighbors': 10, '3_2D__rmin_dist': 0.25, '3_2D__rinit': 'random', '3_2D__rlearning_rate': 1.0, '3_2D__rn_epochs': None, '3_2D__rrandom_state': None} -------------------------------- Scoring params : {'3_2D__smin_score': 30, '3_2D__srecompute': True, '3_2D__ssample_size': None, '3_2D__sn_neighbors': 10, '3_2D__srandom_state': 42, '3_2D__smetric': 'euclidean'} ================================ ----------- Scores ----------- 3_2D__neighbors_articles_authors : 0.5189 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_mean : -0.0320 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_median : -0.0446 3_2D__trustworthiness_sklearn : 0.9385 --------- Desc Scores --------- 3_2D__neighbors_articles_authors_det 0 Céline Gicquel : 0.81 1 Loïc Paulevé : 0.77 2 Sébastien Tixeuil : 0.74 3 Paola Tubaro : 0.73 4 Isabelle Guyon : 0.71 5 Nikolaus Hansen : 0.71 6 Franck Cappello : 0.66 7 Cyril Furtlehner : 0.66 8 Yann Ponty : 0.63 9 Dimo Brockhoff : 0.61 10 Sarah Cohen-Boulakia : 0.61 11 Marc Baboulin : 0.61 12 Philippe Caillou : 0.61 13 Albert Cohen : 0.61 14 Nicolas Bredeche : 0.61 15 Fatiha Zaidi : 0.59 16 Olivier Teytaud : 0.59 17 Ioana Manolescu : 0.59 18 Marc Schoenauer : 0.59 19 Sylvie Boldo : 0.58 20 Raymond Ros : 0.57 21 Tobias Isenberg : 0.57 22 Nathalie Pernelle : 0.57 23 Anne Auger : 0.56 24 Nathann Cohen : 0.54 25 Fatiha Saïs : 0.53 26 Guillaume Charpiat : 0.52 27 Jean-Daniel Fekete : 0.48 28 Petra Isenberg : 0.48 29 Claude Marché : 0.48 30 François Goasdoué : 0.47 31 Michèle Sebag : 0.46 32 Lonni Besançon : 0.45 33 Evelyne Lutton : 0.45 34 Chantal Reynaud : 0.43 35 Guillaume Melquiond : 0.42 36 Caroline Appert : 0.38 37 Alain Denise : 0.37 38 Anastasia Bezerianos : 0.36 39 Steven Martin : 0.35 40 Balázs Kégl : 0.34 41 Olivier Chapuis : 0.34 42 Michel Beaudouin-Lafon : 0.33 43 Pierre Dragicevic : 0.31 44 Emmanuel Pietriga : 0.31 45 Wendy Mackay : 0.28 46 Wendy E. Mackay : 0.27 47 Johanne Cohen : 0.25 dtype: object VALUE : 0.5189 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_det 0 Guillaume Melquiond : -0.15 1 François Goasdoué : -0.15 2 Dimo Brockhoff : -0.14 3 Anne Auger : -0.12 4 Jean-Daniel Fekete : -0.12 5 Petra Isenberg : -0.11 6 Tobias Isenberg : -0.10 7 Fatiha Saïs : -0.09 8 Evelyne Lutton : -0.09 9 Chantal Reynaud : -0.08 10 Michel Beaudouin-Lafon : -0.08 11 Pierre Dragicevic : -0.08 12 Johanne Cohen : -0.07 13 Lonni Besançon : -0.07 14 Ioana Manolescu : -0.07 15 Steven Martin : -0.06 16 Wendy Mackay : -0.06 17 Olivier Chapuis : -0.06 18 Balázs Kégl : -0.06 19 Nikolaus Hansen : -0.06 20 Emmanuel Pietriga : -0.05 21 Céline Gicquel : -0.05 22 Claude Marché : -0.05 23 Nathalie Pernelle : -0.05 24 Marc Baboulin : -0.04 25 Guillaume Charpiat : -0.04 26 Yann Ponty : -0.03 27 Nicolas Bredeche : -0.03 28 Loïc Paulevé : -0.02 29 Philippe Caillou : -0.01 30 Franck Cappello : 0.01 31 Alain Denise : 0.01 32 Cyril Furtlehner : 0.01 33 Olivier Teytaud : 0.02 34 Nathann Cohen : 0.02 35 Anastasia Bezerianos : 0.02 36 Michèle Sebag : 0.02 37 Sylvie Boldo : 0.03 38 Marc Schoenauer : 0.03 39 Caroline Appert : 0.03 40 Wendy E. Mackay : 0.04 41 Raymond Ros : 0.04 42 Albert Cohen : 0.04 43 Fatiha Zaidi : 0.04 44 Sarah Cohen-Boulakia : 0.05 45 Paola Tubaro : 0.06 46 Isabelle Guyon : 0.06 47 Sébastien Tixeuil : 0.11 dtype: object VALUE : -0.0320 --------- Raw Scores --------- 3_2D__neighbors_articles_authors Sébastien Tixeuil 0.736170 Michèle Sebag 0.457664 Johanne Cohen 0.253488 Albert Cohen 0.606897 Wendy E. Mackay 0.267391 Philippe Caillou 0.606977 Alain Denise 0.369444 Jean-Daniel Fekete 0.483648 Emmanuel Pietriga 0.309524 Yann Ponty 0.633333 Marc Schoenauer 0.588489 Franck Cappello 0.660976 Caroline Appert 0.376087 Michel Beaudouin-Lafon 0.332099 Wendy Mackay 0.282979 Anne Auger 0.556962 Evelyne Lutton 0.454054 Pierre Dragicevic 0.313580 Ioana Manolescu 0.592683 Nikolaus Hansen 0.706173 Nicolas Bredeche 0.605882 Olivier Teytaud 0.593396 François Goasdoué 0.473585 Nathalie Pernelle 0.570588 Fatiha Saïs 0.529268 Sarah Cohen-Boulakia 0.612121 Claude Marché 0.478723 Chantal Reynaud 0.426667 Olivier Chapuis 0.336538 Steven Martin 0.353846 Fatiha Zaidi 0.593750 Balázs Kégl 0.342105 Paola Tubaro 0.728205 Raymond Ros 0.573529 Cyril Furtlehner 0.656410 Anastasia Bezerianos 0.359701 Sylvie Boldo 0.582857 Guillaume Melquiond 0.415152 Marc Baboulin 0.608889 Dimo Brockhoff 0.612821 Nathann Cohen 0.539024 Petra Isenberg 0.482243 Tobias Isenberg 0.573504 Loïc Paulevé 0.769048 Céline Gicquel 0.807895 Isabelle Guyon 0.714607 Guillaume Charpiat 0.522581 Lonni Besançon 0.454545 dtype: float64 3_2D__2_nD_3_2D_neighbors_articles_authors_dim 2_nD__neighbors_articles_authors \ Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 3_2D__neighbors_articles_authors scdec Sébastien Tixeuil 0.736170 0.112766 Michèle Sebag 0.457664 0.024818 Johanne Cohen 0.253488 -0.072093 Albert Cohen 0.606897 0.041379 Wendy E. Mackay 0.267391 0.036957 Philippe Caillou 0.606977 -0.011628 Alain Denise 0.369444 0.011111 Jean-Daniel Fekete 0.483648 -0.117610 Emmanuel Pietriga 0.309524 -0.053968 Yann Ponty 0.633333 -0.030303 Marc Schoenauer 0.588489 0.029496 Franck Cappello 0.660976 0.009756 Caroline Appert 0.376087 0.034783 Michel Beaudouin-Lafon 0.332099 -0.081481 Wendy Mackay 0.282979 -0.061702 Anne Auger 0.556962 -0.120253 Evelyne Lutton 0.454054 -0.086486 Pierre Dragicevic 0.313580 -0.079012 Ioana Manolescu 0.592683 -0.067073 Nikolaus Hansen 0.706173 -0.055556 Nicolas Bredeche 0.605882 -0.029412 Olivier Teytaud 0.593396 0.017925 François Goasdoué 0.473585 -0.147170 Nathalie Pernelle 0.570588 -0.047059 Fatiha Saïs 0.529268 -0.092683 Sarah Cohen-Boulakia 0.612121 0.048485 Claude Marché 0.478723 -0.048936 Chantal Reynaud 0.426667 -0.083333 Olivier Chapuis 0.336538 -0.061538 Steven Martin 0.353846 -0.064103 Fatiha Zaidi 0.593750 0.043750 Balázs Kégl 0.342105 -0.060526 Paola Tubaro 0.728205 0.056410 Raymond Ros 0.573529 0.038235 Cyril Furtlehner 0.656410 0.012821 Anastasia Bezerianos 0.359701 0.023881 Sylvie Boldo 0.582857 0.025714 Guillaume Melquiond 0.415152 -0.148485 Marc Baboulin 0.608889 -0.042222 Dimo Brockhoff 0.612821 -0.138462 Nathann Cohen 0.539024 0.019512 Petra Isenberg 0.482243 -0.110280 Tobias Isenberg 0.573504 -0.098291 Loïc Paulevé 0.769048 -0.016667 Céline Gicquel 0.807895 -0.050000 Isabelle Guyon 0.714607 0.058427 Guillaume Charpiat 0.522581 -0.035484 Lonni Besançon 0.454545 -0.069697 Nothing in cache, initial Fitting with min_cluster_size=15 Found 56 clusters in 0.28196315100012725s Max Fitting with min_cluster_size=30 Found 31 clusters in 0.10167976899992937s Max Fitting with min_cluster_size=60 Found 19 clusters in 0.0976447210000515s Max Fitting with min_cluster_size=120 Found 10 clusters in 0.09320349900008296s Max Fitting with min_cluster_size=240 Found 5 clusters in 0.09002576800003226s Midpoint Fitting with min_cluster_size=180 Found 5 clusters in 0.08962468700019599s Midpoint Fitting with min_cluster_size=150 Found 7 clusters in 0.09160015399993426s Midpoint Fitting with min_cluster_size=135 Found 8 clusters in 0.09431141200002457s No need Re-Fitting with min_cluster_size=135 Clusters cached: [5, 5, 7, 8, 10, 19, 31, 56] Nothing in cache, initial Fitting with min_cluster_size=15 Found 56 clusters in 0.10585523600002489s Max Fitting with min_cluster_size=30 Found 31 clusters in 0.10178884100014329s Max Fitting with min_cluster_size=60 Found 19 clusters in 0.09873409099964192s Midpoint Fitting with min_cluster_size=45 Found 21 clusters in 0.09887258800017662s Midpoint Fitting with min_cluster_size=37 Found 23 clusters in 0.09989442599999165s Re-Fitting with min_cluster_size=30 Found 31 clusters in 0.10044263299960221s Clusters cached: [19, 21, 23, 31, 56] ================================ Run params : {'4_clus__rkey': 'hdbscan', '4_clus__rbase_factor': 3} -------------------------------- Scoring params : {'4_clus__siter_stab': 2, '4_clus__sremove_stab': [0, 0.01, 0.03, 0.1, 0.25], '4_clus__smetric': 'euclidean', '4_clus__srandom_state': None} ================================ ----------- Scores ----------- 4_clus__nb_clust_0 : 8.0000 4_clus__silhouette_0 : 0.1678 4_clus__avg_word_couv_0 : 0.4404 4_clus__med_word_couv_0 : 0.4362 4_clus__avg_word_couv_minus_0 : 0.4153 4_clus__big_small_ratio_0 : 9.2214 4_clus__stab_clus_0 : 0.1000 4_clus__nb_clust_1 : 24.0000 4_clus__silhouette_1 : 0.1259 4_clus__avg_word_couv_1 : 0.5946 4_clus__med_word_couv_1 : 0.6053 4_clus__avg_word_couv_minus_1 : 0.5780 4_clus__big_small_ratio_1 : 37.9355 4_clus__stab_clus_1 : 0.0968 4_clus__avg_stab_avg : 0.0984 4_clus__avg_couv_avg : 0.5175 4_clus__clu_score : 0.3079 --------- Desc Scores --------- 4_clus__clus_eval_pos_0_det 0 visualizations, interaction techniques : s 743... 1 quantitative methods, discrete mathematics : s... 2 query, cluster computing : s 496 stb 0.00 + 0.... 3 optimization control, covariance matrix adapta... 4 logic science, verification : s 278 stb 0.20 +... 5 networking internet architecture, protocols : ... 6 natural language, computation language : s 152... 7 architectures, parallelism : s 140 stb 0.00 + ... dtype: object VALUE : 0.4404 4_clus__clus_eval_pos_1_det 0 information visualization, interfaces : s 695 ... 1 specification, programs : s 278 stb 0.00 + 0.4... 2 ontology, linked : s 157 stb 0.40 + 0.49 - 0.03 3 natural language processing, speech : s 152 st... 4 compiler, hardware : s 140 stb 0.00 + 0.54 - 0.03 5 combinatorics, discrete : s 125 stb 0.00 + 0.4... 6 vertices, minimum : s 117 stb 0.00 + 0.61 - 0.03 7 fluid, numerical simulations : s 101 stb 0.30 ... 8 training, automl : s 93 stb 0.00 + 0.41 - 0.03 9 biological, metabolism : s 89 stb 0.00 + 0.63 ... 10 covariance matrix adaptation evolution, functi... 11 secondary structure, genes : s 87 stb 0.00 + 0... 12 fault, cloud computing : s 80 stb 0.00 + 0.57 ... 13 stabilizing, population protocols : s 77 stb 0... 14 linear systems, factorization : s 72 stb 0.70 ... 15 internet architecture, routing : s 72 stb 0.00... 16 monte carlo search, multi armed : s 65 stb 0.0... 17 floating point, arithmetic : s 62 stb 0.90 + 0... 18 radio, resource allocation : s 58 stb 0.00 + 0... 19 queries, query answering : s 54 stb 0.00 + 0.7... 20 scientific workflows, personality : s 52 stb 0... 21 evolution strategies, noisy optimization : s 4... 22 evolutionary computation, genetic programming ... 23 belief propagation, condensed matter : s 42 st... 24 integer linear, chance constrained : s 38 stb ... 25 recommender systems, multi agent : s 36 stb 0.... 26 french language, signing : s 36 stb 0.00 + 0.6... 27 workers, social network : s 34 stb 0.00 + 0.47... 28 materialized views, engines : s 33 stb 0.00 + ... 29 evolutionary robotics, autonomous : s 33 stb 0... 30 mobile robots, cartesian : s 31 stb 0.40 + 0.8... dtype: object VALUE : 0.5946 --------- Raw Scores --------- ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score'] ================================ Run params : {} -------------------------------- Scoring params : {'6_pst__sname_list': ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score']} ================================ ----------- Scores ----------- 6_pst__final_score : 0.4592 --------- Desc Scores --------- 6_pst__final_score_det 0 2_nD__neighbors_articles_authors : 0.55 1 3_2D__neighbors_articles_authors : 0.52 2 4_clus__clu_score : 0.31 dtype: object VALUE : 0.4592 --------- Raw Scores --------- ================================ Run params : {'2_nD__rkey': 'bert', '2_nD__rnum_dims': 768, '2_nD__rnormalize': True, '2_nD__rfamily': 'all-MiniLM-L6-v2', '2_nD__rmax_length': 256} -------------------------------- Scoring params : {'2_nD__smin_score': 30, '2_nD__srecompute': True, '2_nD__ssample_size': None, '2_nD__sn_neighbors': 10, '2_nD__srandom_state': 42} ================================ ----------- Scores ----------- 2_nD__neighbors_articles_authors : 0.5509 --------- Desc Scores --------- 2_nD__neighbors_articles_authors_det 0 Céline Gicquel : 0.86 1 Loïc Paulevé : 0.79 2 Nikolaus Hansen : 0.76 3 Dimo Brockhoff : 0.75 4 Anne Auger : 0.68 5 Tobias Isenberg : 0.67 6 Paola Tubaro : 0.67 7 Yann Ponty : 0.66 8 Ioana Manolescu : 0.66 9 Isabelle Guyon : 0.66 10 Franck Cappello : 0.65 11 Marc Baboulin : 0.65 12 Cyril Furtlehner : 0.64 13 Nicolas Bredeche : 0.64 14 Sébastien Tixeuil : 0.62 15 Fatiha Saïs : 0.62 16 François Goasdoué : 0.62 17 Philippe Caillou : 0.62 18 Nathalie Pernelle : 0.62 19 Jean-Daniel Fekete : 0.60 20 Petra Isenberg : 0.59 21 Olivier Teytaud : 0.58 22 Albert Cohen : 0.57 23 Sarah Cohen-Boulakia : 0.56 24 Guillaume Melquiond : 0.56 25 Marc Schoenauer : 0.56 26 Guillaume Charpiat : 0.56 27 Sylvie Boldo : 0.56 28 Fatiha Zaidi : 0.55 29 Evelyne Lutton : 0.54 30 Raymond Ros : 0.54 31 Claude Marché : 0.53 32 Lonni Besançon : 0.52 33 Nathann Cohen : 0.52 34 Chantal Reynaud : 0.51 35 Michèle Sebag : 0.43 36 Steven Martin : 0.42 37 Michel Beaudouin-Lafon : 0.41 38 Balázs Kégl : 0.40 39 Olivier Chapuis : 0.40 40 Pierre Dragicevic : 0.39 41 Emmanuel Pietriga : 0.36 42 Alain Denise : 0.36 43 Wendy Mackay : 0.34 44 Caroline Appert : 0.34 45 Anastasia Bezerianos : 0.34 46 Johanne Cohen : 0.33 47 Wendy E. Mackay : 0.23 Name: 0, dtype: object VALUE : 0.5509 --------- Raw Scores --------- 2_nD__neighbors_articles_authors Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 Name: 0, dtype: float64 ================================ Run params : {'3_2D__rkey': 'umap', '3_2D__rmetric': 'euclidean', '3_2D__rn_neighbors': 10, '3_2D__rmin_dist': 0.5, '3_2D__rinit': 'random', '3_2D__rlearning_rate': 1.0, '3_2D__rn_epochs': None, '3_2D__rrandom_state': None} -------------------------------- Scoring params : {'3_2D__smin_score': 30, '3_2D__srecompute': True, '3_2D__ssample_size': None, '3_2D__sn_neighbors': 10, '3_2D__srandom_state': 42, '3_2D__smetric': 'euclidean'} ================================ ----------- Scores ----------- 3_2D__neighbors_articles_authors : 0.4772 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_mean : -0.0737 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_median : -0.0804 3_2D__trustworthiness_sklearn : 0.9084 --------- Desc Scores --------- 3_2D__neighbors_articles_authors_det 0 Loïc Paulevé : 0.76 1 Céline Gicquel : 0.74 2 Isabelle Guyon : 0.69 3 Sébastien Tixeuil : 0.69 4 Sarah Cohen-Boulakia : 0.66 5 Marc Baboulin : 0.66 6 Nikolaus Hansen : 0.64 7 Paola Tubaro : 0.63 8 Nicolas Bredeche : 0.62 9 Cyril Furtlehner : 0.62 10 Franck Cappello : 0.57 11 Dimo Brockhoff : 0.57 12 Olivier Teytaud : 0.56 13 Raymond Ros : 0.56 14 Ioana Manolescu : 0.56 15 Albert Cohen : 0.56 16 Yann Ponty : 0.55 17 Sylvie Boldo : 0.53 18 Anne Auger : 0.53 19 Philippe Caillou : 0.50 20 Jean-Daniel Fekete : 0.49 21 Marc Schoenauer : 0.49 22 Nathann Cohen : 0.47 23 Guillaume Charpiat : 0.47 24 Tobias Isenberg : 0.47 25 François Goasdoué : 0.47 26 Claude Marché : 0.45 27 Guillaume Melquiond : 0.45 28 Fatiha Saïs : 0.43 29 Lonni Besançon : 0.43 30 Petra Isenberg : 0.42 31 Chantal Reynaud : 0.42 32 Evelyne Lutton : 0.42 33 Fatiha Zaidi : 0.41 34 Steven Martin : 0.39 35 Michèle Sebag : 0.38 36 Nathalie Pernelle : 0.38 37 Olivier Chapuis : 0.35 38 Alain Denise : 0.35 39 Caroline Appert : 0.34 40 Balázs Kégl : 0.31 41 Anastasia Bezerianos : 0.31 42 Pierre Dragicevic : 0.31 43 Michel Beaudouin-Lafon : 0.30 44 Johanne Cohen : 0.27 45 Wendy Mackay : 0.26 46 Wendy E. Mackay : 0.25 47 Emmanuel Pietriga : 0.24 dtype: object VALUE : 0.4772 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_det 0 Nathalie Pernelle : -0.24 1 Tobias Isenberg : -0.20 2 Fatiha Saïs : -0.19 3 Dimo Brockhoff : -0.18 4 Petra Isenberg : -0.17 5 François Goasdoué : -0.15 6 Anne Auger : -0.15 7 Fatiha Zaidi : -0.14 8 Evelyne Lutton : -0.12 9 Céline Gicquel : -0.12 10 Emmanuel Pietriga : -0.12 11 Philippe Caillou : -0.12 12 Nikolaus Hansen : -0.12 13 Guillaume Melquiond : -0.12 14 Yann Ponty : -0.12 15 Michel Beaudouin-Lafon : -0.11 16 Jean-Daniel Fekete : -0.11 17 Ioana Manolescu : -0.10 18 Lonni Besançon : -0.09 19 Chantal Reynaud : -0.09 20 Balázs Kégl : -0.09 21 Guillaume Charpiat : -0.09 22 Wendy Mackay : -0.09 23 Pierre Dragicevic : -0.08 24 Franck Cappello : -0.08 25 Claude Marché : -0.08 26 Marc Schoenauer : -0.07 27 Johanne Cohen : -0.06 28 Michèle Sebag : -0.05 29 Olivier Chapuis : -0.05 30 Nathann Cohen : -0.05 31 Paola Tubaro : -0.05 32 Steven Martin : -0.03 33 Cyril Furtlehner : -0.03 34 Sylvie Boldo : -0.03 35 Anastasia Bezerianos : -0.02 36 Loïc Paulevé : -0.02 37 Nicolas Bredeche : -0.02 38 Olivier Teytaud : -0.01 39 Alain Denise : -0.01 40 Albert Cohen : -0.01 41 Caroline Appert : -0.00 42 Marc Baboulin : 0.01 43 Wendy E. Mackay : 0.02 44 Raymond Ros : 0.03 45 Isabelle Guyon : 0.03 46 Sébastien Tixeuil : 0.06 47 Sarah Cohen-Boulakia : 0.10 dtype: object VALUE : -0.0737 --------- Raw Scores --------- 3_2D__neighbors_articles_authors Sébastien Tixeuil 0.685106 Michèle Sebag 0.383942 Johanne Cohen 0.269767 Albert Cohen 0.556897 Wendy E. Mackay 0.245652 Philippe Caillou 0.500000 Alain Denise 0.347222 Jean-Daniel Fekete 0.493711 Emmanuel Pietriga 0.244444 Yann Ponty 0.546970 Marc Schoenauer 0.493525 Franck Cappello 0.573171 Caroline Appert 0.339130 Michel Beaudouin-Lafon 0.298765 Wendy Mackay 0.259574 Anne Auger 0.525316 Evelyne Lutton 0.416216 Pierre Dragicevic 0.309877 Ioana Manolescu 0.560976 Nikolaus Hansen 0.643210 Nicolas Bredeche 0.617647 Olivier Teytaud 0.563208 François Goasdoué 0.466038 Nathalie Pernelle 0.376471 Fatiha Saïs 0.431707 Sarah Cohen-Boulakia 0.663636 Claude Marché 0.451064 Chantal Reynaud 0.416667 Olivier Chapuis 0.350000 Steven Martin 0.387179 Fatiha Zaidi 0.406250 Balázs Kégl 0.313158 Paola Tubaro 0.625641 Raymond Ros 0.561765 Cyril Furtlehner 0.615385 Anastasia Bezerianos 0.311940 Sylvie Boldo 0.531429 Guillaume Melquiond 0.445455 Marc Baboulin 0.662222 Dimo Brockhoff 0.569231 Nathann Cohen 0.473171 Petra Isenberg 0.417757 Tobias Isenberg 0.467521 Loïc Paulevé 0.761905 Céline Gicquel 0.736842 Isabelle Guyon 0.687640 Guillaume Charpiat 0.470968 Lonni Besançon 0.430303 dtype: float64 3_2D__2_nD_3_2D_neighbors_articles_authors_dim 2_nD__neighbors_articles_authors \ Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 3_2D__neighbors_articles_authors scdec Sébastien Tixeuil 0.685106 0.061702 Michèle Sebag 0.383942 -0.048905 Johanne Cohen 0.269767 -0.055814 Albert Cohen 0.556897 -0.008621 Wendy E. Mackay 0.245652 0.015217 Philippe Caillou 0.500000 -0.118605 Alain Denise 0.347222 -0.011111 Jean-Daniel Fekete 0.493711 -0.107547 Emmanuel Pietriga 0.244444 -0.119048 Yann Ponty 0.546970 -0.116667 Marc Schoenauer 0.493525 -0.065468 Franck Cappello 0.573171 -0.078049 Caroline Appert 0.339130 -0.002174 Michel Beaudouin-Lafon 0.298765 -0.114815 Wendy Mackay 0.259574 -0.085106 Anne Auger 0.525316 -0.151899 Evelyne Lutton 0.416216 -0.124324 Pierre Dragicevic 0.309877 -0.082716 Ioana Manolescu 0.560976 -0.098780 Nikolaus Hansen 0.643210 -0.118519 Nicolas Bredeche 0.617647 -0.017647 Olivier Teytaud 0.563208 -0.012264 François Goasdoué 0.466038 -0.154717 Nathalie Pernelle 0.376471 -0.241176 Fatiha Saïs 0.431707 -0.190244 Sarah Cohen-Boulakia 0.663636 0.100000 Claude Marché 0.451064 -0.076596 Chantal Reynaud 0.416667 -0.093333 Olivier Chapuis 0.350000 -0.048077 Steven Martin 0.387179 -0.030769 Fatiha Zaidi 0.406250 -0.143750 Balázs Kégl 0.313158 -0.089474 Paola Tubaro 0.625641 -0.046154 Raymond Ros 0.561765 0.026471 Cyril Furtlehner 0.615385 -0.028205 Anastasia Bezerianos 0.311940 -0.023881 Sylvie Boldo 0.531429 -0.025714 Guillaume Melquiond 0.445455 -0.118182 Marc Baboulin 0.662222 0.011111 Dimo Brockhoff 0.569231 -0.182051 Nathann Cohen 0.473171 -0.046341 Petra Isenberg 0.417757 -0.174766 Tobias Isenberg 0.467521 -0.204274 Loïc Paulevé 0.761905 -0.023810 Céline Gicquel 0.736842 -0.121053 Isabelle Guyon 0.687640 0.031461 Guillaume Charpiat 0.470968 -0.087097 Lonni Besançon 0.430303 -0.093939 Nothing in cache, initial Fitting with min_cluster_size=15 Found 7 clusters in 0.3022271129998444s No need Re-Fitting with min_cluster_size=15 Clusters cached: [7] Nothing in cache, initial Fitting with min_cluster_size=15 Found 7 clusters in 0.11288243899980444s No need Re-Fitting with min_cluster_size=15 Clusters cached: [7] ================================ Run params : {'4_clus__rkey': 'hdbscan', '4_clus__rbase_factor': 3} -------------------------------- Scoring params : {'4_clus__siter_stab': 2, '4_clus__sremove_stab': [0, 0.01, 0.03, 0.1, 0.25], '4_clus__smetric': 'euclidean', '4_clus__srandom_state': None} ================================ ----------- Scores ----------- 4_clus__nb_clust_0 : 8.0000 4_clus__silhouette_0 : -0.2171 4_clus__avg_word_couv_0 : 0.6430 4_clus__med_word_couv_0 : 0.6818 4_clus__avg_word_couv_minus_0 : 0.6382 4_clus__big_small_ratio_0 : 252.5625 4_clus__stab_clus_0 : 0.0000 4_clus__nb_clust_1 : 24.0000 4_clus__silhouette_1 : -0.2171 4_clus__avg_word_couv_1 : 0.6226 4_clus__med_word_couv_1 : 0.6818 4_clus__avg_word_couv_minus_1 : 0.6161 4_clus__big_small_ratio_1 : 252.5625 4_clus__stab_clus_1 : 0.0000 4_clus__avg_stab_avg : 0.0000 4_clus__avg_couv_avg : 0.6328 4_clus__clu_score : 0.3164 --------- Desc Scores --------- 4_clus__clus_eval_pos_0_det 0 query, verification : s 4041 stb 0.00 + 0.09 -... 1 large hadron collider, astrophysics : s 44 stb... 2 french language, signing : s 35 stb 0.00 + 0.6... 3 evolutionary robotics, autonomous : s 33 stb 0... 4 mobile robots : s 18 stb 0.00 + 0.83 - 0.00 5 approximate bayesian, genetics : s 17 stb 0.00... 6 electric power, power grids : s 16 stb 0.00 + ... dtype: object VALUE : 0.6430 4_clus__clus_eval_pos_1_det 0 memory, quantitative methods : s 4041 stb 0.00... 1 energy physics, cosmic : s 44 stb 0.00 + 0.68 ... 2 motion capture, signs : s 35 stb 0.00 + 0.51 -... 3 robotics : s 33 stb 0.00 + 0.76 - 0.01 4 robots : s 18 stb 0.00 + 0.94 - 0.01 5 demographic, populations : s 17 stb 0.00 + 0.8... 6 network architecture, electricity : s 16 stb 0... dtype: object VALUE : 0.6226 --------- Raw Scores --------- ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score'] ================================ Run params : {} -------------------------------- Scoring params : {'6_pst__sname_list': ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score']} ================================ ----------- Scores ----------- 6_pst__final_score : 0.4482 --------- Desc Scores --------- 6_pst__final_score_det 0 2_nD__neighbors_articles_authors : 0.55 1 3_2D__neighbors_articles_authors : 0.48 2 4_clus__clu_score : 0.32 dtype: object VALUE : 0.4482 --------- Raw Scores --------- .. GENERATED FROM PYTHON SOURCE LINES 128-129 When the experiment is run, results of all runs is saved in `experiment.results`. We access the values corresponding to each run with `experiment.results.runs_`. .. GENERATED FROM PYTHON SOURCE LINES 129-147 .. code-block:: Python experiment.results.runs_[0].scores "" list(experiment.results.runs_[0].desc_scores.keys()) "" experiment.results.runs_[0].desc_scores "" list(experiment.results.runs_[0].raw_scores.keys()) "" experiment.results.runs_[0].raw_scores "" experiment.results.print_best(n=20) .. rst-class:: sphx-glr-script-out .. code-block:: none ------------------------ Best 20 results: ------------------------ 0_agscore id 2_nD__rkey 2_nD__rnum_dims \ 0 0.500707 5acf1c353d2c392a1729 bert 768 1 0.459228 da34d4237bc01ee70684 bert 768 2 0.448154 a862f98f80e4912e4e08 bert 768 2_nD__rnormalize 2_nD__rfamily 2_nD__rmax_length 3_2D__rkey \ 0 True all-MiniLM-L6-v2 256 umap 1 True all-MiniLM-L6-v2 256 umap 2 True all-MiniLM-L6-v2 256 umap 3_2D__rmetric 3_2D__rn_neighbors 3_2D__rmin_dist 3_2D__rinit \ 0 euclidean 10 0.10 random 1 euclidean 10 0.25 random 2 euclidean 10 0.50 random 3_2D__rlearning_rate 3_2D__rn_epochs 3_2D__rrandom_state 4_clus__rkey \ 0 1.0 None None hdbscan 1 1.0 None None hdbscan 2 1.0 None None hdbscan 4_clus__rbase_factor 2_nD__smin_score 2_nD__srecompute \ 0 3 30 True 1 3 30 True 2 3 30 True 2_nD__ssample_size 2_nD__sn_neighbors 2_nD__srandom_state \ 0 None 10 42 1 None 10 42 2 None 10 42 3_2D__smin_score 3_2D__srecompute 3_2D__ssample_size 3_2D__sn_neighbors \ 0 30 True None 10 1 30 True None 10 2 30 True None 10 3_2D__srandom_state 3_2D__smetric 4_clus__siter_stab \ 0 42 euclidean 2 1 42 euclidean 2 2 42 euclidean 2 4_clus__sremove_stab 4_clus__smetric 4_clus__srandom_state \ 0 [0, 0.01, 0.03, 0.1, 0.25] euclidean None 1 [0, 0.01, 0.03, 0.1, 0.25] euclidean None 2 [0, 0.01, 0.03, 0.1, 0.25] euclidean None 6_pst__sname_list \ 0 [2_nD__neighbors_articles_authors, 3_2D__neigh... 1 [2_nD__neighbors_articles_authors, 3_2D__neigh... 2 [2_nD__neighbors_articles_authors, 3_2D__neigh... 2_nD__neighbors_articles_authors 3_2D__neighbors_articles_authors \ 0 0.550862 0.536061 1 0.550862 0.518877 2 0.550862 0.477201 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_mean \ 0 -0.014801 1 -0.031985 2 -0.073661 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_median \ 0 -0.011051 1 -0.044641 2 -0.080382 3_2D__trustworthiness_sklearn 4_clus__nb_clust_0 4_clus__silhouette_0 \ 0 0.948647 8 0.352386 1 0.938497 8 0.167785 2 0.908424 8 -0.217119 4_clus__avg_word_couv_0 4_clus__med_word_couv_0 \ 0 0.431710 0.448429 1 0.440386 0.436227 2 0.643001 0.681818 4_clus__avg_word_couv_minus_0 4_clus__big_small_ratio_0 \ 0 0.410497 4.514851 1 0.415253 9.221429 2 0.638169 252.562500 4_clus__stab_clus_0 4_clus__nb_clust_1 4_clus__silhouette_1 \ 0 0.4875 24 0.250664 1 0.1000 24 0.125918 2 0.0000 24 -0.217119 4_clus__avg_word_couv_1 4_clus__med_word_couv_1 \ 0 0.574919 0.533592 1 0.594620 0.605263 2 0.622592 0.681818 4_clus__avg_word_couv_minus_1 4_clus__big_small_ratio_1 \ 0 0.556723 35.366667 1 0.578013 37.935484 2 0.616136 252.562500 4_clus__stab_clus_1 4_clus__avg_stab_avg 4_clus__avg_couv_avg \ 0 0.166667 0.327083 0.503315 1 0.096774 0.098387 0.517503 2 0.000000 0.000000 0.632796 4_clus__clu_score 6_pst__final_score \ 0 0.415199 0.500707 1 0.307945 0.459228 2 0.316398 0.448154 dump active 0 experiment_pipeline_lisn/lisn/2022.11.15.1/0/m... True 1 experiment_pipeline_lisn/lisn/2022.11.15.1/0/m... True 2 experiment_pipeline_lisn/lisn/2022.11.15.1/0/m... True .. GENERATED FROM PYTHON SOURCE LINES 148-151 Let's see some of the results of the experiment from the file system. We will first check the contents of the `scores` directory. .. GENERATED FROM PYTHON SOURCE LINES 151-154 .. code-block:: Python # !ls $TOP_DIR/scores .. GENERATED FROM PYTHON SOURCE LINES 155-156 `6_pst__final_scores.csv` file contains the final scores for each set of parameters together with the parameter values. We had 3 runs, so there are 3 values. .. GENERATED FROM PYTHON SOURCE LINES 156-159 .. code-block:: Python # !cat $TOP_DIR/scores/6_pst__final_score.csv .. GENERATED FROM PYTHON SOURCE LINES 160-161 The `final_results.csv` displays each score calculated during the experiment in separate columns together with `rank` and an aggregated score `agscore`. This value is the same as the value in `6_post__final_score.csv` file. .. GENERATED FROM PYTHON SOURCE LINES 161-164 .. code-block:: Python # !cat $TOP_DIR/scores/final_results.csv .. GENERATED FROM PYTHON SOURCE LINES 165-166 The other files in the directory contains the single score calculated for all runs. For example `2_nD__neighbors_articles_authors.csv` file contains the `2_nD__neighbors_articles_authors` scores for 3 runs. .. GENERATED FROM PYTHON SOURCE LINES 166-169 .. code-block:: Python # !cat $TOP_DIR/scores/2_nD__neighbors_articles_authors.csv .. GENERATED FROM PYTHON SOURCE LINES 170-171 The files that contain `det` in theirs names, contains the neighbors and their scores used to calculate the score `2_nD__neighbors_articles_authors` for each run. .. GENERATED FROM PYTHON SOURCE LINES 171-174 .. code-block:: Python # !cat $TOP_DIR/scores/2_nD__neighbors_articles_authors_det.csv .. GENERATED FROM PYTHON SOURCE LINES 175-178 These files also reside in the hierarchical dataset directories generated during the run. For example `experiment_pipeline/lisn/2022.11.15.1/0/mat_articles__authors_4_teams_4_labs_4_words_10_0.05_None_None_5_4/bert_768_True_all-MiniLM-L6-v2_256/scores_2_nD__neighbors_articles_authors_det.csv` file, but only for the specific run together with hyperparameters and scorşng parameters. .. GENERATED FROM PYTHON SOURCE LINES 178-181 .. code-block:: Python # !cat $TOP_DIR/lisn/2022.11.15.1/0/mat_articles__authors_4_teams_4_labs_4_words_10_0.05_None_None_5_4/bert_768_True_all-MiniLM-L6-v2_256/scores_2_nD__neighbors_articles_authors_det.csv .. GENERATED FROM PYTHON SOURCE LINES 182-187 Actually for each set of parameters, the estimator generates a directory structure of the form: `top_dir / dataset / dataset_version / robustseed / dataset_column_parameters / projection_nd_key_dim / projection2D_key_n_neighbors_min_dist_metric_init_learning_rate_repulsion_strength / clustering_key_base_factor`. Each score calculated at a certain level in the directory structure is saved in that directory. .. GENERATED FROM PYTHON SOURCE LINES 189-190 Now, we will continue the experiment to run for 3 more set of parameters. .. GENERATED FROM PYTHON SOURCE LINES 190-193 .. code-block:: Python experiment.run(3) .. rst-class:: sphx-glr-script-out .. code-block:: none ================================ Run params : {'2_nD__rkey': 'bert', '2_nD__rnum_dims': 768, '2_nD__rnormalize': True, '2_nD__rfamily': 'all-MiniLM-L6-v2', '2_nD__rmax_length': 256} -------------------------------- Scoring params : {'2_nD__smin_score': 30, '2_nD__srecompute': True, '2_nD__ssample_size': None, '2_nD__sn_neighbors': 10, '2_nD__srandom_state': 42} ================================ ----------- Scores ----------- 2_nD__neighbors_articles_authors : 0.5509 --------- Desc Scores --------- 2_nD__neighbors_articles_authors_det 0 Céline Gicquel : 0.86 1 Loïc Paulevé : 0.79 2 Nikolaus Hansen : 0.76 3 Dimo Brockhoff : 0.75 4 Anne Auger : 0.68 5 Tobias Isenberg : 0.67 6 Paola Tubaro : 0.67 7 Yann Ponty : 0.66 8 Ioana Manolescu : 0.66 9 Isabelle Guyon : 0.66 10 Franck Cappello : 0.65 11 Marc Baboulin : 0.65 12 Cyril Furtlehner : 0.64 13 Nicolas Bredeche : 0.64 14 Sébastien Tixeuil : 0.62 15 Fatiha Saïs : 0.62 16 François Goasdoué : 0.62 17 Philippe Caillou : 0.62 18 Nathalie Pernelle : 0.62 19 Jean-Daniel Fekete : 0.60 20 Petra Isenberg : 0.59 21 Olivier Teytaud : 0.58 22 Albert Cohen : 0.57 23 Sarah Cohen-Boulakia : 0.56 24 Guillaume Melquiond : 0.56 25 Marc Schoenauer : 0.56 26 Guillaume Charpiat : 0.56 27 Sylvie Boldo : 0.56 28 Fatiha Zaidi : 0.55 29 Evelyne Lutton : 0.54 30 Raymond Ros : 0.54 31 Claude Marché : 0.53 32 Lonni Besançon : 0.52 33 Nathann Cohen : 0.52 34 Chantal Reynaud : 0.51 35 Michèle Sebag : 0.43 36 Steven Martin : 0.42 37 Michel Beaudouin-Lafon : 0.41 38 Balázs Kégl : 0.40 39 Olivier Chapuis : 0.40 40 Pierre Dragicevic : 0.39 41 Emmanuel Pietriga : 0.36 42 Alain Denise : 0.36 43 Wendy Mackay : 0.34 44 Caroline Appert : 0.34 45 Anastasia Bezerianos : 0.34 46 Johanne Cohen : 0.33 47 Wendy E. Mackay : 0.23 Name: 0, dtype: object VALUE : 0.5509 --------- Raw Scores --------- 2_nD__neighbors_articles_authors Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 Name: 0, dtype: float64 ================================ Run params : {'3_2D__rkey': 'umap', '3_2D__rmetric': 'euclidean', '3_2D__rn_neighbors': 20, '3_2D__rmin_dist': 0.1, '3_2D__rinit': 'random', '3_2D__rlearning_rate': 1.0, '3_2D__rn_epochs': None, '3_2D__rrandom_state': None} -------------------------------- Scoring params : {'3_2D__smin_score': 30, '3_2D__srecompute': True, '3_2D__ssample_size': None, '3_2D__sn_neighbors': 10, '3_2D__srandom_state': 42, '3_2D__smetric': 'euclidean'} ================================ ----------- Scores ----------- 3_2D__neighbors_articles_authors : 0.5220 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_mean : -0.0289 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_median : -0.0334 3_2D__trustworthiness_sklearn : 0.9410 --------- Desc Scores --------- 3_2D__neighbors_articles_authors_det 0 Loïc Paulevé : 0.79 1 Paola Tubaro : 0.78 2 Céline Gicquel : 0.77 3 Isabelle Guyon : 0.72 4 Nikolaus Hansen : 0.72 5 Sarah Cohen-Boulakia : 0.69 6 Franck Cappello : 0.68 7 Nicolas Bredeche : 0.64 8 Yann Ponty : 0.64 9 Sébastien Tixeuil : 0.64 10 Tobias Isenberg : 0.63 11 Fatiha Saïs : 0.62 12 Dimo Brockhoff : 0.61 13 Marc Baboulin : 0.60 14 Cyril Furtlehner : 0.60 15 Ioana Manolescu : 0.59 16 Olivier Teytaud : 0.59 17 Philippe Caillou : 0.58 18 Albert Cohen : 0.57 19 Raymond Ros : 0.56 20 Marc Schoenauer : 0.56 21 Chantal Reynaud : 0.55 22 François Goasdoué : 0.55 23 Guillaume Charpiat : 0.55 24 Nathalie Pernelle : 0.54 25 Sylvie Boldo : 0.53 26 Anne Auger : 0.53 27 Jean-Daniel Fekete : 0.52 28 Nathann Cohen : 0.51 29 Lonni Besançon : 0.51 30 Petra Isenberg : 0.49 31 Fatiha Zaidi : 0.48 32 Claude Marché : 0.47 33 Evelyne Lutton : 0.45 34 Michèle Sebag : 0.44 35 Guillaume Melquiond : 0.42 36 Steven Martin : 0.38 37 Michel Beaudouin-Lafon : 0.38 38 Alain Denise : 0.36 39 Olivier Chapuis : 0.35 40 Balázs Kégl : 0.34 41 Caroline Appert : 0.34 42 Anastasia Bezerianos : 0.34 43 Wendy Mackay : 0.31 44 Pierre Dragicevic : 0.31 45 Emmanuel Pietriga : 0.30 46 Wendy E. Mackay : 0.26 47 Johanne Cohen : 0.25 dtype: object VALUE : 0.5220 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_det 0 Guillaume Melquiond : -0.15 1 Anne Auger : -0.15 2 Dimo Brockhoff : -0.14 3 Petra Isenberg : -0.10 4 Evelyne Lutton : -0.09 5 Céline Gicquel : -0.09 6 Pierre Dragicevic : -0.09 7 Jean-Daniel Fekete : -0.08 8 Nathalie Pernelle : -0.07 9 Johanne Cohen : -0.07 10 Ioana Manolescu : -0.07 11 François Goasdoué : -0.07 12 Fatiha Zaidi : -0.07 13 Emmanuel Pietriga : -0.06 14 Balázs Kégl : -0.06 15 Claude Marché : -0.06 16 Marc Baboulin : -0.05 17 Nikolaus Hansen : -0.05 18 Olivier Chapuis : -0.04 19 Cyril Furtlehner : -0.04 20 Tobias Isenberg : -0.04 21 Michel Beaudouin-Lafon : -0.04 22 Steven Martin : -0.04 23 Philippe Caillou : -0.03 24 Wendy Mackay : -0.03 25 Sylvie Boldo : -0.02 26 Yann Ponty : -0.02 27 Lonni Besançon : -0.02 28 Guillaume Charpiat : -0.01 29 Nathann Cohen : -0.01 30 Caroline Appert : -0.00 31 Fatiha Saïs : 0.00 32 Marc Schoenauer : 0.00 33 Alain Denise : 0.00 34 Anastasia Bezerianos : 0.00 35 Albert Cohen : 0.00 36 Loïc Paulevé : 0.00 37 Michèle Sebag : 0.01 38 Nicolas Bredeche : 0.01 39 Olivier Teytaud : 0.01 40 Sébastien Tixeuil : 0.02 41 Wendy E. Mackay : 0.03 42 Raymond Ros : 0.03 43 Franck Cappello : 0.03 44 Chantal Reynaud : 0.04 45 Isabelle Guyon : 0.06 46 Paola Tubaro : 0.11 47 Sarah Cohen-Boulakia : 0.13 dtype: object VALUE : -0.0289 --------- Raw Scores --------- 3_2D__neighbors_articles_authors Sébastien Tixeuil 0.640426 Michèle Sebag 0.437956 Johanne Cohen 0.253488 Albert Cohen 0.568966 Wendy E. Mackay 0.256522 Philippe Caillou 0.583721 Alain Denise 0.361111 Jean-Daniel Fekete 0.522013 Emmanuel Pietriga 0.300000 Yann Ponty 0.643939 Marc Schoenauer 0.559712 Franck Cappello 0.680488 Caroline Appert 0.339130 Michel Beaudouin-Lafon 0.375309 Wendy Mackay 0.312766 Anne Auger 0.529114 Evelyne Lutton 0.445946 Pierre Dragicevic 0.306173 Ioana Manolescu 0.591463 Nikolaus Hansen 0.716049 Nicolas Bredeche 0.644118 Olivier Teytaud 0.587736 François Goasdoué 0.552830 Nathalie Pernelle 0.544118 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.690909 Claude Marché 0.465957 Chantal Reynaud 0.553333 Olivier Chapuis 0.353846 Steven Martin 0.382051 Fatiha Zaidi 0.484375 Balázs Kégl 0.339474 Paola Tubaro 0.784615 Raymond Ros 0.561765 Cyril Furtlehner 0.600000 Anastasia Bezerianos 0.338806 Sylvie Boldo 0.534286 Guillaume Melquiond 0.415152 Marc Baboulin 0.604444 Dimo Brockhoff 0.610256 Nathann Cohen 0.512195 Petra Isenberg 0.487850 Tobias Isenberg 0.629915 Loïc Paulevé 0.790476 Céline Gicquel 0.768421 Isabelle Guyon 0.719101 Guillaume Charpiat 0.545161 Lonni Besançon 0.509091 dtype: float64 3_2D__2_nD_3_2D_neighbors_articles_authors_dim 2_nD__neighbors_articles_authors \ Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 3_2D__neighbors_articles_authors scdec Sébastien Tixeuil 0.640426 0.017021 Michèle Sebag 0.437956 0.005109 Johanne Cohen 0.253488 -0.072093 Albert Cohen 0.568966 0.003448 Wendy E. Mackay 0.256522 0.026087 Philippe Caillou 0.583721 -0.034884 Alain Denise 0.361111 0.002778 Jean-Daniel Fekete 0.522013 -0.079245 Emmanuel Pietriga 0.300000 -0.063492 Yann Ponty 0.643939 -0.019697 Marc Schoenauer 0.559712 0.000719 Franck Cappello 0.680488 0.029268 Caroline Appert 0.339130 -0.002174 Michel Beaudouin-Lafon 0.375309 -0.038272 Wendy Mackay 0.312766 -0.031915 Anne Auger 0.529114 -0.148101 Evelyne Lutton 0.445946 -0.094595 Pierre Dragicevic 0.306173 -0.086420 Ioana Manolescu 0.591463 -0.068293 Nikolaus Hansen 0.716049 -0.045679 Nicolas Bredeche 0.644118 0.008824 Olivier Teytaud 0.587736 0.012264 François Goasdoué 0.552830 -0.067925 Nathalie Pernelle 0.544118 -0.073529 Fatiha Saïs 0.621951 0.000000 Sarah Cohen-Boulakia 0.690909 0.127273 Claude Marché 0.465957 -0.061702 Chantal Reynaud 0.553333 0.043333 Olivier Chapuis 0.353846 -0.044231 Steven Martin 0.382051 -0.035897 Fatiha Zaidi 0.484375 -0.065625 Balázs Kégl 0.339474 -0.063158 Paola Tubaro 0.784615 0.112821 Raymond Ros 0.561765 0.026471 Cyril Furtlehner 0.600000 -0.043590 Anastasia Bezerianos 0.338806 0.002985 Sylvie Boldo 0.534286 -0.022857 Guillaume Melquiond 0.415152 -0.148485 Marc Baboulin 0.604444 -0.046667 Dimo Brockhoff 0.610256 -0.141026 Nathann Cohen 0.512195 -0.007317 Petra Isenberg 0.487850 -0.104673 Tobias Isenberg 0.629915 -0.041880 Loïc Paulevé 0.790476 0.004762 Céline Gicquel 0.768421 -0.089474 Isabelle Guyon 0.719101 0.062921 Guillaume Charpiat 0.545161 -0.012903 Lonni Besançon 0.509091 -0.015152 Nothing in cache, initial Fitting with min_cluster_size=15 Found 61 clusters in 0.25722732500025813s Max Fitting with min_cluster_size=30 Found 27 clusters in 0.1010039620000498s Max Fitting with min_cluster_size=60 Found 15 clusters in 0.09871437099991454s Max Fitting with min_cluster_size=120 Found 11 clusters in 0.0962613040001088s Max Fitting with min_cluster_size=240 Found 4 clusters in 0.09473326699981044s Midpoint Fitting with min_cluster_size=180 Found 9 clusters in 0.09300725299999613s Midpoint Fitting with min_cluster_size=210 Found 5 clusters in 0.09343649799984632s Midpoint Fitting with min_cluster_size=195 Found 9 clusters in 0.09285305200000948s Midpoint Fitting with min_cluster_size=202 Found 9 clusters in 0.11084757000026002s No need Re-Fitting with min_cluster_size=202 Clusters cached: [4, 5, 9, 9, 9, 11, 15, 27, 61] Nothing in cache, initial Fitting with min_cluster_size=15 Found 61 clusters in 0.10352430000011736s Max Fitting with min_cluster_size=30 Found 27 clusters in 0.10104787399995985s Max Fitting with min_cluster_size=60 Found 15 clusters in 0.0988867040000514s Midpoint Fitting with min_cluster_size=45 Found 18 clusters in 0.10105969500000356s Midpoint Fitting with min_cluster_size=37 Found 19 clusters in 0.10129343100015831s Re-Fitting with min_cluster_size=30 Found 27 clusters in 0.10129774400002134s Clusters cached: [15, 18, 19, 27, 61] ================================ Run params : {'4_clus__rkey': 'hdbscan', '4_clus__rbase_factor': 3} -------------------------------- Scoring params : {'4_clus__siter_stab': 2, '4_clus__sremove_stab': [0, 0.01, 0.03, 0.1, 0.25], '4_clus__smetric': 'euclidean', '4_clus__srandom_state': None} ================================ ----------- Scores ----------- 4_clus__nb_clust_0 : 8.0000 4_clus__silhouette_0 : 0.3710 4_clus__avg_word_couv_0 : 0.4423 4_clus__med_word_couv_0 : 0.4916 4_clus__avg_word_couv_minus_0 : 0.4208 4_clus__big_small_ratio_0 : 3.4880 4_clus__stab_clus_0 : 0.5111 4_clus__nb_clust_1 : 24.0000 4_clus__silhouette_1 : 0.2395 4_clus__avg_word_couv_1 : 0.6312 4_clus__med_word_couv_1 : 0.6190 4_clus__avg_word_couv_minus_1 : 0.6108 4_clus__big_small_ratio_1 : 29.1000 4_clus__stab_clus_1 : 0.0593 4_clus__avg_stab_avg : 0.2852 4_clus__avg_couv_avg : 0.5367 4_clus__clu_score : 0.4110 --------- Desc Scores --------- 4_clus__clus_eval_pos_0_det 0 visualizations, human interaction : s 725 stb ... 1 networking internet architecture, cluster comp... 2 query, ontology : s 415 stb 0.00 + 0.49 - 0.02 3 logic science, verification : s 375 stb 0.70 +... 4 optimization control, covariance matrix adapta... 5 quantitative methods, bioinformatics : s 301 s... 6 neural networks, image : s 292 stb 0.00 + 0.35... 7 architectures, parallelism : s 225 stb 0.70 + ... 8 natural language, french : s 209 stb 0.00 + 0.... dtype: object VALUE : 0.4423 4_clus__clus_eval_pos_1_det 0 information visualization, interaction techniq... 1 programs, specification : s 375 stb 0.00 + 0.4... 2 training, image processing : s 292 stb 0.00 + ... 3 natural language processing, computation langu... 4 ontologies, linked : s 163 stb 0.00 + 0.43 - 0.03 5 compiler, hardware : s 140 stb 0.00 + 0.56 - 0.03 6 queries, resource description framework : s 13... 7 networking internet, stabilizing : s 137 stb 0... 8 fluid, numerical simulations : s 130 stb 0.90 ... 9 vertices, minimum : s 116 stb 0.00 + 0.61 - 0.03 10 biological, metabolic : s 105 stb 0.20 + 0.62 ... 11 secondary structure, sequence : s 100 stb 0.00... 12 covariance matrix adaptation evolution, benchm... 13 combinatorics, finite : s 81 stb 0.00 + 0.52 -... 14 fault, cloud computing : s 80 stb 0.00 + 0.59 ... 15 mixed integer linear, stochastic : s 78 stb 0.... 16 evolution strategies, noisy optimization : s 7... 17 radio, resource allocation : s 53 stb 0.00 + 0... 18 humanities social sciences, social networks : ... 19 linear systems, factorization : s 46 stb 0.00 ... 20 agent : s 41 stb 0.00 + 0.78 - 0.01 21 monte carlo search, games : s 40 stb 0.00 + 0.... 22 french language, signing : s 34 stb 0.00 + 0.6... 23 multi armed, bandit : s 32 stb 0.00 + 0.94 - 0.02 24 protein : s 31 stb 0.00 + 0.87 - 0.01 25 scientific workflows, workflow : s 30 stb 0.00... 26 evolutionary robotics, autonomous : s 30 stb 0... dtype: object VALUE : 0.6312 --------- Raw Scores --------- ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score'] ================================ Run params : {} -------------------------------- Scoring params : {'6_pst__sname_list': ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score']} ================================ ----------- Scores ----------- 6_pst__final_score : 0.4946 --------- Desc Scores --------- 6_pst__final_score_det 0 2_nD__neighbors_articles_authors : 0.55 1 3_2D__neighbors_articles_authors : 0.52 2 4_clus__clu_score : 0.41 dtype: object VALUE : 0.4946 --------- Raw Scores --------- ================================ Run params : {'2_nD__rkey': 'bert', '2_nD__rnum_dims': 768, '2_nD__rnormalize': True, '2_nD__rfamily': 'all-MiniLM-L6-v2', '2_nD__rmax_length': 256} -------------------------------- Scoring params : {'2_nD__smin_score': 30, '2_nD__srecompute': True, '2_nD__ssample_size': None, '2_nD__sn_neighbors': 10, '2_nD__srandom_state': 42} ================================ ----------- Scores ----------- 2_nD__neighbors_articles_authors : 0.5509 --------- Desc Scores --------- 2_nD__neighbors_articles_authors_det 0 Céline Gicquel : 0.86 1 Loïc Paulevé : 0.79 2 Nikolaus Hansen : 0.76 3 Dimo Brockhoff : 0.75 4 Anne Auger : 0.68 5 Tobias Isenberg : 0.67 6 Paola Tubaro : 0.67 7 Yann Ponty : 0.66 8 Ioana Manolescu : 0.66 9 Isabelle Guyon : 0.66 10 Franck Cappello : 0.65 11 Marc Baboulin : 0.65 12 Cyril Furtlehner : 0.64 13 Nicolas Bredeche : 0.64 14 Sébastien Tixeuil : 0.62 15 Fatiha Saïs : 0.62 16 François Goasdoué : 0.62 17 Philippe Caillou : 0.62 18 Nathalie Pernelle : 0.62 19 Jean-Daniel Fekete : 0.60 20 Petra Isenberg : 0.59 21 Olivier Teytaud : 0.58 22 Albert Cohen : 0.57 23 Sarah Cohen-Boulakia : 0.56 24 Guillaume Melquiond : 0.56 25 Marc Schoenauer : 0.56 26 Guillaume Charpiat : 0.56 27 Sylvie Boldo : 0.56 28 Fatiha Zaidi : 0.55 29 Evelyne Lutton : 0.54 30 Raymond Ros : 0.54 31 Claude Marché : 0.53 32 Lonni Besançon : 0.52 33 Nathann Cohen : 0.52 34 Chantal Reynaud : 0.51 35 Michèle Sebag : 0.43 36 Steven Martin : 0.42 37 Michel Beaudouin-Lafon : 0.41 38 Balázs Kégl : 0.40 39 Olivier Chapuis : 0.40 40 Pierre Dragicevic : 0.39 41 Emmanuel Pietriga : 0.36 42 Alain Denise : 0.36 43 Wendy Mackay : 0.34 44 Caroline Appert : 0.34 45 Anastasia Bezerianos : 0.34 46 Johanne Cohen : 0.33 47 Wendy E. Mackay : 0.23 Name: 0, dtype: object VALUE : 0.5509 --------- Raw Scores --------- 2_nD__neighbors_articles_authors Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 Name: 0, dtype: float64 ================================ Run params : {'3_2D__rkey': 'umap', '3_2D__rmetric': 'euclidean', '3_2D__rn_neighbors': 20, '3_2D__rmin_dist': 0.25, '3_2D__rinit': 'random', '3_2D__rlearning_rate': 1.0, '3_2D__rn_epochs': None, '3_2D__rrandom_state': None} -------------------------------- Scoring params : {'3_2D__smin_score': 30, '3_2D__srecompute': True, '3_2D__ssample_size': None, '3_2D__sn_neighbors': 10, '3_2D__srandom_state': 42, '3_2D__smetric': 'euclidean'} ================================ ----------- Scores ----------- 3_2D__neighbors_articles_authors : 0.5002 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_mean : -0.0506 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_median : -0.0525 3_2D__trustworthiness_sklearn : 0.9295 --------- Desc Scores --------- 3_2D__neighbors_articles_authors_det 0 Céline Gicquel : 0.82 1 Paola Tubaro : 0.76 2 Loïc Paulevé : 0.72 3 Sarah Cohen-Boulakia : 0.67 4 Nikolaus Hansen : 0.65 5 Isabelle Guyon : 0.64 6 Franck Cappello : 0.64 7 Dimo Brockhoff : 0.64 8 Sébastien Tixeuil : 0.63 9 Cyril Furtlehner : 0.61 10 Philippe Caillou : 0.60 11 Nicolas Bredeche : 0.60 12 Yann Ponty : 0.59 13 Ioana Manolescu : 0.59 14 Marc Baboulin : 0.59 15 Fatiha Saïs : 0.58 16 Tobias Isenberg : 0.58 17 Olivier Teytaud : 0.57 18 Albert Cohen : 0.56 19 Nathalie Pernelle : 0.54 20 Nathann Cohen : 0.54 21 Anne Auger : 0.54 22 Raymond Ros : 0.53 23 François Goasdoué : 0.52 24 Jean-Daniel Fekete : 0.51 25 Guillaume Charpiat : 0.51 26 Marc Schoenauer : 0.51 27 Sylvie Boldo : 0.49 28 Claude Marché : 0.49 29 Fatiha Zaidi : 0.49 30 Petra Isenberg : 0.45 31 Chantal Reynaud : 0.45 32 Lonni Besançon : 0.44 33 Guillaume Melquiond : 0.44 34 Michèle Sebag : 0.42 35 Steven Martin : 0.38 36 Michel Beaudouin-Lafon : 0.37 37 Olivier Chapuis : 0.37 38 Evelyne Lutton : 0.36 39 Pierre Dragicevic : 0.34 40 Wendy Mackay : 0.34 41 Anastasia Bezerianos : 0.32 42 Balázs Kégl : 0.32 43 Alain Denise : 0.30 44 Caroline Appert : 0.27 45 Emmanuel Pietriga : 0.27 46 Wendy E. Mackay : 0.26 47 Johanne Cohen : 0.24 dtype: object VALUE : 0.5002 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_det 0 Evelyne Lutton : -0.18 1 Anne Auger : -0.14 2 Petra Isenberg : -0.14 3 Guillaume Melquiond : -0.13 4 Dimo Brockhoff : -0.12 5 Nikolaus Hansen : -0.11 6 François Goasdoué : -0.10 7 Emmanuel Pietriga : -0.10 8 Tobias Isenberg : -0.09 9 Balázs Kégl : -0.09 10 Jean-Daniel Fekete : -0.09 11 Lonni Besançon : -0.08 12 Johanne Cohen : -0.08 13 Caroline Appert : -0.08 14 Nathalie Pernelle : -0.07 15 Yann Ponty : -0.07 16 Ioana Manolescu : -0.07 17 Marc Baboulin : -0.06 18 Loïc Paulevé : -0.06 19 Sylvie Boldo : -0.06 20 Fatiha Zaidi : -0.06 21 Alain Denise : -0.06 22 Chantal Reynaud : -0.06 23 Marc Schoenauer : -0.05 24 Pierre Dragicevic : -0.05 25 Guillaume Charpiat : -0.05 26 Michel Beaudouin-Lafon : -0.05 27 Fatiha Saïs : -0.04 28 Claude Marché : -0.04 29 Nicolas Bredeche : -0.04 30 Céline Gicquel : -0.03 31 Steven Martin : -0.03 32 Cyril Furtlehner : -0.03 33 Olivier Chapuis : -0.03 34 Philippe Caillou : -0.02 35 Michèle Sebag : -0.02 36 Anastasia Bezerianos : -0.02 37 Isabelle Guyon : -0.02 38 Franck Cappello : -0.01 39 Albert Cohen : -0.01 40 Wendy Mackay : -0.00 41 Raymond Ros : -0.00 42 Olivier Teytaud : -0.00 43 Sébastien Tixeuil : 0.01 44 Nathann Cohen : 0.02 45 Wendy E. Mackay : 0.03 46 Paola Tubaro : 0.09 47 Sarah Cohen-Boulakia : 0.10 dtype: object VALUE : -0.0506 --------- Raw Scores --------- 3_2D__neighbors_articles_authors Sébastien Tixeuil 0.634043 Michèle Sebag 0.415328 Johanne Cohen 0.244186 Albert Cohen 0.560345 Wendy E. Mackay 0.256522 Philippe Caillou 0.597674 Alain Denise 0.297222 Jean-Daniel Fekete 0.514465 Emmanuel Pietriga 0.265079 Yann Ponty 0.590909 Marc Schoenauer 0.505755 Franck Cappello 0.636585 Caroline Appert 0.265217 Michel Beaudouin-Lafon 0.367901 Wendy Mackay 0.340426 Anne Auger 0.535443 Evelyne Lutton 0.356757 Pierre Dragicevic 0.340741 Ioana Manolescu 0.587805 Nikolaus Hansen 0.646914 Nicolas Bredeche 0.597059 Olivier Teytaud 0.572642 François Goasdoué 0.516981 Nathalie Pernelle 0.544118 Fatiha Saïs 0.580488 Sarah Cohen-Boulakia 0.666667 Claude Marché 0.489362 Chantal Reynaud 0.450000 Olivier Chapuis 0.367308 Steven Martin 0.384615 Fatiha Zaidi 0.487500 Balázs Kégl 0.315789 Paola Tubaro 0.758974 Raymond Ros 0.532353 Cyril Furtlehner 0.612821 Anastasia Bezerianos 0.319403 Sylvie Boldo 0.494286 Guillaume Melquiond 0.436364 Marc Baboulin 0.586667 Dimo Brockhoff 0.635897 Nathann Cohen 0.541463 Petra Isenberg 0.453271 Tobias Isenberg 0.576923 Loïc Paulevé 0.721429 Céline Gicquel 0.823684 Isabelle Guyon 0.640449 Guillaume Charpiat 0.506452 Lonni Besançon 0.439394 dtype: float64 3_2D__2_nD_3_2D_neighbors_articles_authors_dim 2_nD__neighbors_articles_authors \ Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 3_2D__neighbors_articles_authors scdec Sébastien Tixeuil 0.634043 0.010638 Michèle Sebag 0.415328 -0.017518 Johanne Cohen 0.244186 -0.081395 Albert Cohen 0.560345 -0.005172 Wendy E. Mackay 0.256522 0.026087 Philippe Caillou 0.597674 -0.020930 Alain Denise 0.297222 -0.061111 Jean-Daniel Fekete 0.514465 -0.086792 Emmanuel Pietriga 0.265079 -0.098413 Yann Ponty 0.590909 -0.072727 Marc Schoenauer 0.505755 -0.053237 Franck Cappello 0.636585 -0.014634 Caroline Appert 0.265217 -0.076087 Michel Beaudouin-Lafon 0.367901 -0.045679 Wendy Mackay 0.340426 -0.004255 Anne Auger 0.535443 -0.141772 Evelyne Lutton 0.356757 -0.183784 Pierre Dragicevic 0.340741 -0.051852 Ioana Manolescu 0.587805 -0.071951 Nikolaus Hansen 0.646914 -0.114815 Nicolas Bredeche 0.597059 -0.038235 Olivier Teytaud 0.572642 -0.002830 François Goasdoué 0.516981 -0.103774 Nathalie Pernelle 0.544118 -0.073529 Fatiha Saïs 0.580488 -0.041463 Sarah Cohen-Boulakia 0.666667 0.103030 Claude Marché 0.489362 -0.038298 Chantal Reynaud 0.450000 -0.060000 Olivier Chapuis 0.367308 -0.030769 Steven Martin 0.384615 -0.033333 Fatiha Zaidi 0.487500 -0.062500 Balázs Kégl 0.315789 -0.086842 Paola Tubaro 0.758974 0.087179 Raymond Ros 0.532353 -0.002941 Cyril Furtlehner 0.612821 -0.030769 Anastasia Bezerianos 0.319403 -0.016418 Sylvie Boldo 0.494286 -0.062857 Guillaume Melquiond 0.436364 -0.127273 Marc Baboulin 0.586667 -0.064444 Dimo Brockhoff 0.635897 -0.115385 Nathann Cohen 0.541463 0.021951 Petra Isenberg 0.453271 -0.139252 Tobias Isenberg 0.576923 -0.094872 Loïc Paulevé 0.721429 -0.064286 Céline Gicquel 0.823684 -0.034211 Isabelle Guyon 0.640449 -0.015730 Guillaume Charpiat 0.506452 -0.051613 Lonni Besançon 0.439394 -0.084848 Nothing in cache, initial Fitting with min_cluster_size=15 Found 42 clusters in 0.2711373510001067s Max Fitting with min_cluster_size=30 Found 3 clusters in 0.10500004599998647s Midpoint Fitting with min_cluster_size=22 Found 4 clusters in 0.10679338699992513s Re-Fitting with min_cluster_size=15 Found 42 clusters in 0.10345919800010961s Clusters cached: [3, 4, 42] Nothing in cache, initial Fitting with min_cluster_size=15 Found 42 clusters in 0.10357241900010195s Max Fitting with min_cluster_size=30 Found 3 clusters in 0.10556750900013867s Midpoint Fitting with min_cluster_size=22 Found 4 clusters in 0.10724184199989395s Re-Fitting with min_cluster_size=15 Found 42 clusters in 0.10214239300012196s Clusters cached: [3, 4, 42] ================================ Run params : {'4_clus__rkey': 'hdbscan', '4_clus__rbase_factor': 3} -------------------------------- Scoring params : {'4_clus__siter_stab': 2, '4_clus__sremove_stab': [0, 0.01, 0.03, 0.1, 0.25], '4_clus__smetric': 'euclidean', '4_clus__srandom_state': None} ================================ ----------- Scores ----------- 4_clus__nb_clust_0 : 8.0000 4_clus__silhouette_0 : 0.1325 4_clus__avg_word_couv_0 : 0.6856 4_clus__med_word_couv_0 : 0.6707 4_clus__avg_word_couv_minus_0 : 0.6721 4_clus__big_small_ratio_0 : 72.2667 4_clus__stab_clus_0 : 0.0238 4_clus__nb_clust_1 : 24.0000 4_clus__silhouette_1 : 0.1325 4_clus__avg_word_couv_1 : 0.5883 4_clus__med_word_couv_1 : 0.6047 4_clus__avg_word_couv_minus_1 : 0.5757 4_clus__big_small_ratio_1 : 72.2667 4_clus__stab_clus_1 : 0.0000 4_clus__avg_stab_avg : 0.0119 4_clus__avg_couv_avg : 0.6370 4_clus__clu_score : 0.3244 --------- Desc Scores --------- 4_clus__clus_eval_pos_0_det 0 visualizations, interaction techniques : s 645... 1 query, ontology : s 252 stb 0.30 + 0.62 - 0.04 2 logic science, verification : s 230 stb 0.40 +... 3 architectures, parallelism : s 222 stb 0.20 + ... 4 networking internet architecture, simulation r... 5 natural language, computation language : s 145... 6 fluid, numerical simulations : s 108 stb 0.00 ... 7 vertices, planar : s 107 stb 0.00 + 0.58 - 0.02 8 secondary structure, bioinformatics : s 106 st... 9 biological, metabolic : s 92 stb 0.00 + 0.66 -... 10 cluster computing, fault : s 82 stb 0.00 + 0.8... 11 neural networks, automl : s 82 stb 0.00 + 0.40... 12 stabilizing, population protocols : s 69 stb 0... 13 humanities social sciences, agent : s 64 stb 0... 14 function evaluations, black optimization : s 5... 15 floating point, arithmetic : s 55 stb 0.00 + 0... 16 optimization control, sphere function : s 54 s... 17 combinatorics, permutations : s 53 stb 0.00 + ... 18 mixed integer linear, numerical results : s 49... 19 monte carlo search, games : s 38 stb 0.00 + 0.... 20 french language, signing : s 35 stb 0.00 + 0.6... 21 astrophysics, machine learning challenge : s 3... 22 evolutionary robotics, autonomous : s 32 stb 0... 23 covariance matrix adaptation : s 32 stb 0.00 +... 24 random generation, cellular automata : s 30 st... 25 authors, reviewing : s 30 stb 0.00 + 0.37 - 0.02 26 belief propagation algorithm, condensed matter... 27 discrete event systems, diagnosis : s 28 stb 0... 28 hypermedia, learners : s 26 stb 0.00 + 0.46 - ... 29 multi armed : s 23 stb 0.00 + 0.78 - 0.00 30 quantum : s 22 stb 0.00 + 1.00 - 0.00 31 scientific workflows, scientific workflow : s ... 32 mobile robots : s 20 stb 0.00 + 0.80 - 0.00 33 protein : s 20 stb 0.00 + 1.00 - 0.01 34 treatment, drugs : s 20 stb 0.00 + 0.65 - 0.01 35 twitter, information networks : s 17 stb 0.00 ... 36 electric power, power grids : s 16 stb 0.00 + ... 37 approximate bayesian, genetics : s 16 stb 0.00... 38 omega, occupy : s 15 stb 0.00 + 0.53 - 0.00 39 inconsistency : s 15 stb 0.00 + 0.80 - 0.00 40 image classification, convolutional : s 15 stb... 41 evolutionary computation, genetic algorithms :... dtype: object VALUE : 0.6856 4_clus__clus_eval_pos_1_det 0 information visualization, display : s 645 stb... 1 queries, ontologies : s 252 stb 0.00 + 0.49 - ... 2 specification, deductive : s 230 stb 0.00 + 0.... 3 compiler, linear systems : s 222 stb 0.00 + 0.... 4 networking internet, cloud radio access networ... 5 natural language processing, machine translati... 6 mechanics, flows : s 108 stb 0.00 + 0.57 - 0.02 7 vertex, edges : s 107 stb 0.00 + 0.53 - 0.02 8 secondary, sequence : s 106 stb 0.00 + 0.66 - ... 9 biology, metabolism : s 92 stb 0.00 + 0.59 - 0.02 10 image processing, hyper parameter : s 82 stb 0... 11 fault tolerance, cloud computing : s 82 stb 0.... 12 protocols, stabilizing algorithm : s 69 stb 0.... 13 social sciences, multiagent systems : s 64 stb... 14 benchmarking, dimension search space : s 57 st... 15 floating point arithmetic, floating : s 55 stb... 16 evolution strategies, noisy optimization : s 5... 17 combinatorial, permutation : s 53 stb 0.00 + 0... 18 integer linear, chance constrained : s 49 stb ... 19 monte carlo, partially observable : s 38 stb 0... 20 motion capture, signs : s 35 stb 0.00 + 0.51 -... 21 cosmic, learning challenge : s 33 stb 0.00 + 0... 22 covariance matrix adaptation evolution, covari... 23 robotics, robotic : s 32 stb 0.00 + 0.94 - 0.01 24 members, reviewers : s 30 stb 0.00 + 0.30 - 0.01 25 words, cellular : s 30 stb 0.00 + 0.63 - 0.03 26 belief propagation, boltzmann machine : s 30 s... 27 distributed discrete event systems, faults : s... 28 forgetting, experimenting : s 26 stb 0.00 + 0.... 29 multi armed bandit, multi armed bandits : s 23... 30 quantum physics, circuit : s 22 stb 0.00 + 0.7... 31 workflows : s 22 stb 0.00 + 0.82 - 0.01 32 protein protein, protein complexes : s 20 stb ... 33 patients, clinical : s 20 stb 0.00 + 0.65 - 0.01 34 robots : s 20 stb 0.00 + 0.95 - 0.01 35 tweets, social media : s 17 stb 0.00 + 0.47 - ... 36 network architecture, electricity : s 16 stb 0... 37 demographic, populations : s 16 stb 0.00 + 0.8... 38 inconsistent, description logic : s 15 stb 0.0... 39 experimentally, young : s 15 stb 0.00 + 0.07 -... 40 evolutionary algorithm, coevolution : s 15 stb... 41 monotone, experimentation : s 15 stb 0.00 + 0.... dtype: object VALUE : 0.5883 --------- Raw Scores --------- ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score'] ================================ Run params : {} -------------------------------- Scoring params : {'6_pst__sname_list': ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score']} ================================ ----------- Scores ----------- 6_pst__final_score : 0.4585 --------- Desc Scores --------- 6_pst__final_score_det 0 2_nD__neighbors_articles_authors : 0.55 1 3_2D__neighbors_articles_authors : 0.50 2 4_clus__clu_score : 0.32 dtype: object VALUE : 0.4585 --------- Raw Scores --------- ================================ Run params : {'2_nD__rkey': 'bert', '2_nD__rnum_dims': 768, '2_nD__rnormalize': True, '2_nD__rfamily': 'all-MiniLM-L6-v2', '2_nD__rmax_length': 256} -------------------------------- Scoring params : {'2_nD__smin_score': 30, '2_nD__srecompute': True, '2_nD__ssample_size': None, '2_nD__sn_neighbors': 10, '2_nD__srandom_state': 42} ================================ ----------- Scores ----------- 2_nD__neighbors_articles_authors : 0.5509 --------- Desc Scores --------- 2_nD__neighbors_articles_authors_det 0 Céline Gicquel : 0.86 1 Loïc Paulevé : 0.79 2 Nikolaus Hansen : 0.76 3 Dimo Brockhoff : 0.75 4 Anne Auger : 0.68 5 Tobias Isenberg : 0.67 6 Paola Tubaro : 0.67 7 Yann Ponty : 0.66 8 Ioana Manolescu : 0.66 9 Isabelle Guyon : 0.66 10 Franck Cappello : 0.65 11 Marc Baboulin : 0.65 12 Cyril Furtlehner : 0.64 13 Nicolas Bredeche : 0.64 14 Sébastien Tixeuil : 0.62 15 Fatiha Saïs : 0.62 16 François Goasdoué : 0.62 17 Philippe Caillou : 0.62 18 Nathalie Pernelle : 0.62 19 Jean-Daniel Fekete : 0.60 20 Petra Isenberg : 0.59 21 Olivier Teytaud : 0.58 22 Albert Cohen : 0.57 23 Sarah Cohen-Boulakia : 0.56 24 Guillaume Melquiond : 0.56 25 Marc Schoenauer : 0.56 26 Guillaume Charpiat : 0.56 27 Sylvie Boldo : 0.56 28 Fatiha Zaidi : 0.55 29 Evelyne Lutton : 0.54 30 Raymond Ros : 0.54 31 Claude Marché : 0.53 32 Lonni Besançon : 0.52 33 Nathann Cohen : 0.52 34 Chantal Reynaud : 0.51 35 Michèle Sebag : 0.43 36 Steven Martin : 0.42 37 Michel Beaudouin-Lafon : 0.41 38 Balázs Kégl : 0.40 39 Olivier Chapuis : 0.40 40 Pierre Dragicevic : 0.39 41 Emmanuel Pietriga : 0.36 42 Alain Denise : 0.36 43 Wendy Mackay : 0.34 44 Caroline Appert : 0.34 45 Anastasia Bezerianos : 0.34 46 Johanne Cohen : 0.33 47 Wendy E. Mackay : 0.23 Name: 0, dtype: object VALUE : 0.5509 --------- Raw Scores --------- 2_nD__neighbors_articles_authors Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 Name: 0, dtype: float64 ================================ Run params : {'3_2D__rkey': 'umap', '3_2D__rmetric': 'euclidean', '3_2D__rn_neighbors': 20, '3_2D__rmin_dist': 0.5, '3_2D__rinit': 'random', '3_2D__rlearning_rate': 1.0, '3_2D__rn_epochs': None, '3_2D__rrandom_state': None} -------------------------------- Scoring params : {'3_2D__smin_score': 30, '3_2D__srecompute': True, '3_2D__ssample_size': None, '3_2D__sn_neighbors': 10, '3_2D__srandom_state': 42, '3_2D__smetric': 'euclidean'} ================================ ----------- Scores ----------- 3_2D__neighbors_articles_authors : 0.4478 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_mean : -0.1031 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_median : -0.0998 3_2D__trustworthiness_sklearn : 0.9004 --------- Desc Scores --------- 3_2D__neighbors_articles_authors_det 0 Loïc Paulevé : 0.80 1 Isabelle Guyon : 0.69 2 Nicolas Bredeche : 0.64 3 Paola Tubaro : 0.63 4 Céline Gicquel : 0.63 5 Nikolaus Hansen : 0.62 6 Dimo Brockhoff : 0.62 7 Yann Ponty : 0.58 8 Sébastien Tixeuil : 0.57 9 Ioana Manolescu : 0.56 10 Olivier Teytaud : 0.56 11 Sarah Cohen-Boulakia : 0.55 12 Sylvie Boldo : 0.53 13 Raymond Ros : 0.53 14 François Goasdoué : 0.53 15 Marc Baboulin : 0.52 16 Cyril Furtlehner : 0.52 17 Anne Auger : 0.51 18 Fatiha Saïs : 0.50 19 Marc Schoenauer : 0.49 20 Nathann Cohen : 0.48 21 Albert Cohen : 0.47 22 Jean-Daniel Fekete : 0.46 23 Claude Marché : 0.46 24 Guillaume Charpiat : 0.43 25 Tobias Isenberg : 0.43 26 Nathalie Pernelle : 0.42 27 Franck Cappello : 0.42 28 Petra Isenberg : 0.41 29 Chantal Reynaud : 0.40 30 Guillaume Melquiond : 0.40 31 Lonni Besançon : 0.39 32 Fatiha Zaidi : 0.37 33 Philippe Caillou : 0.37 34 Michèle Sebag : 0.35 35 Steven Martin : 0.34 36 Olivier Chapuis : 0.32 37 Michel Beaudouin-Lafon : 0.31 38 Wendy Mackay : 0.30 39 Anastasia Bezerianos : 0.29 40 Caroline Appert : 0.29 41 Pierre Dragicevic : 0.28 42 Balázs Kégl : 0.27 43 Evelyne Lutton : 0.27 44 Alain Denise : 0.26 45 Wendy E. Mackay : 0.26 46 Emmanuel Pietriga : 0.25 47 Johanne Cohen : 0.20 dtype: object VALUE : 0.4478 3_2D__2_nD_3_2D_neighbors_articles_authors_dim_det 0 Evelyne Lutton : -0.27 1 Philippe Caillou : -0.25 2 Tobias Isenberg : -0.25 3 Franck Cappello : -0.23 4 Céline Gicquel : -0.23 5 Nathalie Pernelle : -0.19 6 Fatiha Zaidi : -0.18 7 Petra Isenberg : -0.18 8 Anne Auger : -0.17 9 Guillaume Melquiond : -0.16 10 Jean-Daniel Fekete : -0.14 11 Nikolaus Hansen : -0.14 12 Dimo Brockhoff : -0.14 13 Lonni Besançon : -0.13 14 Johanne Cohen : -0.13 15 Guillaume Charpiat : -0.13 16 Balázs Kégl : -0.13 17 Marc Baboulin : -0.13 18 Cyril Furtlehner : -0.13 19 Fatiha Saïs : -0.12 20 Emmanuel Pietriga : -0.12 21 Chantal Reynaud : -0.11 22 Pierre Dragicevic : -0.11 23 Michel Beaudouin-Lafon : -0.10 24 Alain Denise : -0.10 25 Albert Cohen : -0.10 26 Ioana Manolescu : -0.10 27 François Goasdoué : -0.09 28 Yann Ponty : -0.08 29 Olivier Chapuis : -0.08 30 Steven Martin : -0.08 31 Michèle Sebag : -0.08 32 Claude Marché : -0.07 33 Marc Schoenauer : -0.07 34 Caroline Appert : -0.05 35 Sébastien Tixeuil : -0.05 36 Wendy Mackay : -0.05 37 Anastasia Bezerianos : -0.04 38 Nathann Cohen : -0.04 39 Paola Tubaro : -0.04 40 Sylvie Boldo : -0.02 41 Olivier Teytaud : -0.02 42 Sarah Cohen-Boulakia : -0.01 43 Raymond Ros : -0.00 44 Nicolas Bredeche : 0.00 45 Loïc Paulevé : 0.02 46 Wendy E. Mackay : 0.03 47 Isabelle Guyon : 0.04 dtype: object VALUE : -0.1031 --------- Raw Scores --------- 3_2D__neighbors_articles_authors Sébastien Tixeuil 0.570213 Michèle Sebag 0.354745 Johanne Cohen 0.195349 Albert Cohen 0.468966 Wendy E. Mackay 0.258696 Philippe Caillou 0.369767 Alain Denise 0.261111 Jean-Daniel Fekete 0.461635 Emmanuel Pietriga 0.247619 Yann Ponty 0.580303 Marc Schoenauer 0.491367 Franck Cappello 0.421951 Caroline Appert 0.286957 Michel Beaudouin-Lafon 0.311111 Wendy Mackay 0.295745 Anne Auger 0.505063 Evelyne Lutton 0.267568 Pierre Dragicevic 0.282716 Ioana Manolescu 0.563415 Nikolaus Hansen 0.623457 Nicolas Bredeche 0.635294 Olivier Teytaud 0.558491 François Goasdoué 0.528302 Nathalie Pernelle 0.423529 Fatiha Saïs 0.504878 Sarah Cohen-Boulakia 0.551515 Claude Marché 0.457447 Chantal Reynaud 0.400000 Olivier Chapuis 0.315385 Steven Martin 0.338462 Fatiha Zaidi 0.371875 Balázs Kégl 0.273684 Paola Tubaro 0.633333 Raymond Ros 0.532353 Cyril Furtlehner 0.517949 Anastasia Bezerianos 0.291045 Sylvie Boldo 0.534286 Guillaume Melquiond 0.400000 Marc Baboulin 0.522222 Dimo Brockhoff 0.615385 Nathann Cohen 0.480488 Petra Isenberg 0.414953 Tobias Isenberg 0.425641 Loïc Paulevé 0.802381 Céline Gicquel 0.631579 Isabelle Guyon 0.692135 Guillaume Charpiat 0.429032 Lonni Besançon 0.393939 dtype: float64 3_2D__2_nD_3_2D_neighbors_articles_authors_dim 2_nD__neighbors_articles_authors \ Sébastien Tixeuil 0.623404 Michèle Sebag 0.432847 Johanne Cohen 0.325581 Albert Cohen 0.565517 Wendy E. Mackay 0.230435 Philippe Caillou 0.618605 Alain Denise 0.358333 Jean-Daniel Fekete 0.601258 Emmanuel Pietriga 0.363492 Yann Ponty 0.663636 Marc Schoenauer 0.558993 Franck Cappello 0.651220 Caroline Appert 0.341304 Michel Beaudouin-Lafon 0.413580 Wendy Mackay 0.344681 Anne Auger 0.677215 Evelyne Lutton 0.540541 Pierre Dragicevic 0.392593 Ioana Manolescu 0.659756 Nikolaus Hansen 0.761728 Nicolas Bredeche 0.635294 Olivier Teytaud 0.575472 François Goasdoué 0.620755 Nathalie Pernelle 0.617647 Fatiha Saïs 0.621951 Sarah Cohen-Boulakia 0.563636 Claude Marché 0.527660 Chantal Reynaud 0.510000 Olivier Chapuis 0.398077 Steven Martin 0.417949 Fatiha Zaidi 0.550000 Balázs Kégl 0.402632 Paola Tubaro 0.671795 Raymond Ros 0.535294 Cyril Furtlehner 0.643590 Anastasia Bezerianos 0.335821 Sylvie Boldo 0.557143 Guillaume Melquiond 0.563636 Marc Baboulin 0.651111 Dimo Brockhoff 0.751282 Nathann Cohen 0.519512 Petra Isenberg 0.592523 Tobias Isenberg 0.671795 Loïc Paulevé 0.785714 Céline Gicquel 0.857895 Isabelle Guyon 0.656180 Guillaume Charpiat 0.558065 Lonni Besançon 0.524242 3_2D__neighbors_articles_authors scdec Sébastien Tixeuil 0.570213 -0.053191 Michèle Sebag 0.354745 -0.078102 Johanne Cohen 0.195349 -0.130233 Albert Cohen 0.468966 -0.096552 Wendy E. Mackay 0.258696 0.028261 Philippe Caillou 0.369767 -0.248837 Alain Denise 0.261111 -0.097222 Jean-Daniel Fekete 0.461635 -0.139623 Emmanuel Pietriga 0.247619 -0.115873 Yann Ponty 0.580303 -0.083333 Marc Schoenauer 0.491367 -0.067626 Franck Cappello 0.421951 -0.229268 Caroline Appert 0.286957 -0.054348 Michel Beaudouin-Lafon 0.311111 -0.102469 Wendy Mackay 0.295745 -0.048936 Anne Auger 0.505063 -0.172152 Evelyne Lutton 0.267568 -0.272973 Pierre Dragicevic 0.282716 -0.109877 Ioana Manolescu 0.563415 -0.096341 Nikolaus Hansen 0.623457 -0.138272 Nicolas Bredeche 0.635294 0.000000 Olivier Teytaud 0.558491 -0.016981 François Goasdoué 0.528302 -0.092453 Nathalie Pernelle 0.423529 -0.194118 Fatiha Saïs 0.504878 -0.117073 Sarah Cohen-Boulakia 0.551515 -0.012121 Claude Marché 0.457447 -0.070213 Chantal Reynaud 0.400000 -0.110000 Olivier Chapuis 0.315385 -0.082692 Steven Martin 0.338462 -0.079487 Fatiha Zaidi 0.371875 -0.178125 Balázs Kégl 0.273684 -0.128947 Paola Tubaro 0.633333 -0.038462 Raymond Ros 0.532353 -0.002941 Cyril Furtlehner 0.517949 -0.125641 Anastasia Bezerianos 0.291045 -0.044776 Sylvie Boldo 0.534286 -0.022857 Guillaume Melquiond 0.400000 -0.163636 Marc Baboulin 0.522222 -0.128889 Dimo Brockhoff 0.615385 -0.135897 Nathann Cohen 0.480488 -0.039024 Petra Isenberg 0.414953 -0.177570 Tobias Isenberg 0.425641 -0.246154 Loïc Paulevé 0.802381 0.016667 Céline Gicquel 0.631579 -0.226316 Isabelle Guyon 0.692135 0.035955 Guillaume Charpiat 0.429032 -0.129032 Lonni Besançon 0.393939 -0.130303 Nothing in cache, initial Fitting with min_cluster_size=15 Found 4 clusters in 0.29130253199991785s No need Re-Fitting with min_cluster_size=15 Clusters cached: [4] Nothing in cache, initial Fitting with min_cluster_size=15 Found 4 clusters in 0.10657290900007865s No need Re-Fitting with min_cluster_size=15 Clusters cached: [4] ================================ Run params : {'4_clus__rkey': 'hdbscan', '4_clus__rbase_factor': 3} -------------------------------- Scoring params : {'4_clus__siter_stab': 2, '4_clus__sremove_stab': [0, 0.01, 0.03, 0.1, 0.25], '4_clus__smetric': 'euclidean', '4_clus__srandom_state': None} ================================ ----------- Scores ----------- 4_clus__nb_clust_0 : 8.0000 4_clus__silhouette_0 : -0.0674 4_clus__avg_word_couv_0 : 0.5945 4_clus__med_word_couv_0 : 0.7148 4_clus__avg_word_couv_minus_0 : 0.5878 4_clus__big_small_ratio_0 : 204.0500 4_clus__stab_clus_0 : 0.0000 4_clus__nb_clust_1 : 24.0000 4_clus__silhouette_1 : -0.0674 4_clus__avg_word_couv_1 : 0.5816 4_clus__med_word_couv_1 : 0.6955 4_clus__avg_word_couv_minus_1 : 0.5751 4_clus__big_small_ratio_1 : 204.0500 4_clus__stab_clus_1 : 0.0000 4_clus__avg_stab_avg : 0.0000 4_clus__avg_couv_avg : 0.5881 4_clus__clu_score : 0.2940 --------- Desc Scores --------- 4_clus__clus_eval_pos_0_det 0 logic science, cluster computing : s 4081 stb ... 1 fluid, numerical simulations : s 108 stb 0.00 ... 2 energy physics, machine learning challenge : s... 3 astrophysics : s 20 stb 0.00 + 0.80 - 0.00 dtype: object VALUE : 0.5945 4_clus__clus_eval_pos_1_det 0 discrete, queries : s 4081 stb 0.00 + 0.09 - 0.00 1 mechanics, flows : s 108 stb 0.00 + 0.56 - 0.02 2 large hadron collider, learning challenge : s ... 3 cosmic, sciences universe : s 20 stb 0.00 + 0.... dtype: object VALUE : 0.5816 --------- Raw Scores --------- ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score'] ================================ Run params : {} -------------------------------- Scoring params : {'6_pst__sname_list': ['2_nD__neighbors_articles_authors', '3_2D__neighbors_articles_authors', '4_clus__clu_score']} ================================ ----------- Scores ----------- 6_pst__final_score : 0.4309 --------- Desc Scores --------- 6_pst__final_score_det 0 2_nD__neighbors_articles_authors : 0.55 1 3_2D__neighbors_articles_authors : 0.45 2 4_clus__clu_score : 0.29 dtype: object VALUE : 0.4309 --------- Raw Scores --------- <cartodata.model_selection.utils.Results object at 0x7efd56e60c70> .. GENERATED FROM PYTHON SOURCE LINES 194-195 We can see that we have run the first 6 parameter sets in the dataframe. .. GENERATED FROM PYTHON SOURCE LINES 195-197 .. code-block:: Python len(experiment.results.runs_) .. rst-class:: sphx-glr-script-out .. code-block:: none 6 .. rst-class:: sphx-glr-timing **Total running time of the script:** (28 minutes 16.209 seconds) .. _sphx_glr_download_auto_examples_experiment_pipeline_lisn.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: experiment_pipeline_lisn.ipynb <experiment_pipeline_lisn.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: experiment_pipeline_lisn.py <experiment_pipeline_lisn.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: experiment_pipeline_lisn.zip <experiment_pipeline_lisn.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_