jjerphan

\documentclass[a4paper,10pt]{report}
\usepackage[francais]{babel} % Package babel pour le français
\usepackage[T1]{fontenc}    % Package pour les accentuations
\usepackage[utf8]{inputenc} % Français
\usepackage{lmodern}        % Pour avoir de bonnes polices en pdf
\usepackage{graphicx}       % Indispensable pour les figures
\usepackage{epstopdf}       % Utile pour les figures, résout une erreur
\usepackage{amsmath}        % Environnement pour les maths, permet du mettre du texte dans les équations
\usepackage{textcomp}
\usepackage{geometry}       % Utilisé pour les marges

jjerphan

[2019-05-22 13:46:14,970] [22/MainThread] [INFO] [root] Building Gridsearcher for grid: {'reg_alpha': [0.0], 'colsample_bylevel': [1.0], 'learning_rate': [0.2, 0.4, 0.8], 'max_delta_step': [0.0], 'booster': ['gbtree'], 'colsample_bytree': [1.0], 'min_child_weight': [0.0], 'subsample': [1.0], 'reg_lambda': [1.0, 3.0, 5.0], 'objective': ['reg:linear'], 'max_depth': [3, 4, 5, 6, 7, 8], 'gamma': [0.0]}
[2019-05-22 13:46:14,971] [22/MainThread] [INFO] [root] Fitting 3 folds for each of 54 candidates, totalling 162 fits
[2019-05-22 13:46:14,974] [22/MainThread] [INFO] [root] Performing GridSearch with CVInterruptWatcherThread
[2019-05-22 13:46:14,974] [22/MainThread] [INFO] [root] Performing GridSearch with Dask
[2019-05-22 13:46:14,974] [22/MainThread] [INFO] [root] Creating Dask client
[2019-05-22 13:46:15,063] [22/MainThread] [INFO] [root] Client.__init__ called from get_best_estimator
[2019-05-22 13:46:15,064] [22/MainThread] [INFO] [root] Client.start called from __init__
[2019-05-22 13:46:15,068] [22/IO loop]

jjerphan

Showing logs of joblib-dask-deadlock-test-server-5796bf7779-6rnlc
[2019-05-23 15:58:21,433] [9/MainThread] [INFO] [root] Launching a server
[2019-05-23 15:58:21,701] [10/MainThread] [INFO] [root] Joblib 0.13.2 imported
[2019-05-23 15:58:22,291] [10/MainThread] [DEBUG] [asyncio] Using selector: EpollSelector
/usr/local/lib/python3.6/site-packages/bokeh/themes/theme.py:94: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
  json = yaml.load(f)
[2019-05-23 15:58:23,109] [10/MainThread] [INFO] [root] setup_log() called
[2019-05-23 15:58:23,109] [10/MainThread] [INFO] [root] Installing debugging signal handler
[2019-05-23 15:58:23,110] [10/MainThread] [INFO] [root] Started reproducible example
[2019-05-23 15:58:23,176] [10/MainThread] [INFO] [root] Dask Scheduler: starting on port 8786

jjerphan

Showing logs of joblib-dask-deadlock-test-server-5796bf7779-4dkw2
[2019-05-27 12:17:25,463] [8/MainThread] [INFO] [root] Launching a server
[2019-05-27 12:17:25,789] [9/MainThread] [INFO] [root] Joblib 0.13.2 imported
[2019-05-27 12:17:25,939] [9/MainThread] [DEBUG] [asyncio] Using selector: EpollSelector
[2019-05-27 12:17:26,152] [9/MainThread] [INFO] [root] Distributed 1.28.0+11.g715c05c imported
[2019-05-27 12:17:26,152] [9/MainThread] [INFO] [root] Git revision: 715c05c2449c55345821f9aecb52d62f6d0d4a3b
[2019-05-27 12:17:26,153] [9/MainThread] [INFO] [joblib.dask] _dask import (logger)
[2019-05-27 12:17:26,153] [9/MainThread] [INFO] [root] _dask import (logging)
/usr/local/lib/python3.6/site-packages/bokeh/themes/theme.py:94: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
  json = yaml.load(f)

jjerphan

Showing logs of joblib-dask-deadlock-test-server-5796bf7779-5pxcz
[2019-05-31 11:50:20,035] [8/MainThread] [INFO] [root] Launching a server
[2019-05-31 11:50:20,771] [9/MainThread] [INFO] [root] Distributed 1.28.0+15.g6ea010b imported
[2019-05-31 11:50:20,772] [9/MainThread] [INFO] [root] Git revision: 6ea010bcf21db7445bd26286966f59a7e75ab390
/usr/local/lib/python3.6/site-packages/bokeh/themes/theme.py:94: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
  json = yaml.load(f)
[2019-05-31 11:50:21,488] [9/MainThread] [INFO] [root] Joblib 0.13.2 imported
[2019-05-31 11:50:21,818] [9/MainThread] [INFO] [root] setup_log() called
[2019-05-31 11:50:21,818] [9/MainThread] [INFO] [root] Installing debugging signal handler
[2019-05-31 11:50:21,819] [9/MainThread] [INFO] [root] Started reproducible example

jjerphan

import numpy as np
import itertools
import time
import seaborn as sns
import gc

import pandas as pd
from matplotlib import pyplot as plt
from sklearn.calibration import CalibratedClassifierCV
from sklearn.datasets import make_classification

jjerphan

ERROR 2020-07-29 11:54:39,805 slot 7664 139953135314752 setDirty called
ERROR 2020-07-29 11:54:39,806 slot 7664 139953135314752 self.stype.isConfigured
ERROR 2020-07-29 11:54:39,806 slot 7664 139953135314752 Number of Downstream_slots: 0
ERROR 2020-07-29 11:54:39,806 slot 7664 139953135314752 OpExportSlot.Input []:  {_ready : True, NOTREADY : None, shape : (511, 512, 512, 3), axistags : z y x c, original_axistags : z y x c, dtype : <class 'numpy.float32'>, drange : (0.0, 1.0), has_mask : None, _dirty : False, ideal_blockshape : [0, 0, 0, 3], display_mode : 'grayscale', channel_names : ['Gaussian Smoothing (σ=0.3) [0]', 'Gaussian Smoothing (σ=0.3) [1]', 'Gaussian Smoothing (σ=0.3) [2]', 'Gaussian Smoothing (σ=0.3) [3]', 'Gaussian Smoothing (σ=0.7) [0]', 'Gaussian Smoothing (σ=0.7) [1]', 'Gaussian Smoothing (σ=0.7) [2]', 'Gaussian Smoothing (σ=0.7) [3]', 'Gaussian Smoothing (σ=1.0) [0]', 'Gaussian Smoothing (σ=1.0) [1]', 'Gaussian Smoothing (σ=1.0) [2]', 'Gaussian Smoothing (σ=1.0) [3]', 'Gaussian Smoothing (σ=

jjerphan

import gc
import time

import numpy as np
import pandas as pd
from scipy import linalg

"""
A simple benchmark to know the performances of setting `check_finite`
to `False` for `linalg.cholesky`

jjerphan

    @pytest.mark.parametrize('nu', [1.6, 2.5, 3.9])
    @pytest.mark.parametrize('loc', [25.0, 10, 35])
    @pytest.mark.parametrize('scale', [13, 5, 20])
    def test_fit(self, nu, loc, scale):
        # Regression test for gh-13396 (21/27 cases failed previously)
        # The first tuple of the parameters' values is discussed in gh-10908
        N = 100
        samples = stats.nakagami.rvs(size=N, nu=nu, loc=loc,
                                     scale=scale, random_state=1337)
        nu_est, loc_est, scale_est = stats.nakagami.fit(samples)

jjerphan

import gc
import itertools
import time

import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt

from sklearn.datasets import make_classification