Rasa test not showing results

Hi,

I followed this guide for evaluating my model.

I first split my data into training and test set, then evaluated some config files for finding the best one. When I found I wanted to test it on the test set, so I first trained a model, and then tested it using this command:

rasa test nlu -u train_test_split/test_data.md --model models/nlu-convert_default-20200212-153255.tar.gz -v

The test went fine and created the confmat and hist files, but the problem is, I need to know the values of the performance of the model (f1-score, accuracy, loss…), and I can’t find them either in the command line nor in the folder.

How can I solve it?

Thank you, Tiziano

Anyone please?

The rasa test nlu command should create a results folder with the following files:

  • confmat.png
  • hist.png
  • intent_report.json

and optionally also a CRFEntityExtractor_report.json if you have the component in your pipeline. The *_report files contain the f1-scores, accuracy, etc. How does your result folder look like? What version of Rasa are you using?

Yes I see these files, but as I said in no one of them it is reported the values of, for example, f1-score and loss. I would like to have some more results… The same for the Core testing. I’m only getting the plot and the results.json (which is the data used in the plot), but nothing else such as the errors that the model did, or so…

Those files should contain something like this

  "mood_great": {
    "precision": 1.0,
    "recall": 1.0,
    "f1-score": 1.0,
    "support": 8,
    "confused_with": {}
  },

So you should see an entry per intent for the intent_report.json and some overall metrics at the bottom of the file. Errors should be reported as well per default. E.g. you should see a file called intent_errors.json. If you want to see successes of the model you can switch that on via --successes.

What else do you want to see?

I do see the intent errors, but it doesn’t happen the same for the stories.

This are all the results I get: image

I would like to see for which stories it went wrong…

Anybody?

When executing rasa test or rasa test core you should see a file called failed_stories.md in the results folder. It should contain the incorrect predicted stories.

I agree that I should, but as I showed in the previous message, this is not what happens…

Can you execute rasa test --debug and paste the log output here? Thanks.

It’s hundreds and hundreds of lines, it doesn’t even fit in my command line history… how can I paste all the log here?

Below are the last few lines of the log:

2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=cmss10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 0.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=cmss10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmss10 ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf') with score of 0.050000.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=cmex10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 0.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=cmex10:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to cmex10 ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf') with score of 0.050000.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=DejaVu Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 0.33499999999999996
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 1.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=DejaVu Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=DejaVu Sans:style=italic:variant=normal:weight=normal:stretch=normal:size=10.0.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 1.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 10.434999999999999
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 10.15
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 1.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 0.15000000000000002
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 0.43499999999999994
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=DejaVu Sans:style=italic:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf') with score of 0.150000.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=DejaVu Sans:style=normal:variant=normal:weight=bold:stretch=normal:size=10.0.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.0
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.0
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.33499999999999996
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.0
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.0
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.0
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.0
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.0
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.0
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.0
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 0.0
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=DejaVu Sans:style=normal:variant=normal:weight=bold:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf') with score of 0.000000.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=DejaVu Sans Mono:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 0.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 1.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 0.33499999999999996
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 1.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=DejaVu Sans Mono:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans Mono ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf') with score of 0.050000.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=DejaVu Sans Display:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 0.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal bold normal>) = 11.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal bold normal>) = 10.335
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05
2020-02-24 10:08:49 DEBUG    matplotlib.font_manager  - findfont: Matching :family=DejaVu Sans Display:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans Display ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf') with score of 0.050000.
2020-02-24 10:08:49 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:49 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:08:49 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:49 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:08:49 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:49 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,
       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])
2020-02-24 10:08:49 DEBUG    matplotlib.backends.backend_pdf  - Embedding font /usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.
2020-02-24 10:08:49 DEBUG    matplotlib.backends.backend_pdf  - Writing TrueType font.
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,
       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:54 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,
       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,
       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:08:58 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,
       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,
       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:02 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,
       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])
2020-02-24 10:09:02 DEBUG    matplotlib.backends.backend_pdf  - Assigning font /b'F1' = '/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,
       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04])
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 652.0
2020-02-24 10:09:08 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03,
       2.e+04, 3.e+04, 4.e+04, 5.e+04, 6.e+04, 7.e+04, 8.e+04, 9.e+04])
2020-02-24 10:09:08 DEBUG    matplotlib.backends.backend_pdf  - Embedding font /usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.
2020-02-24 10:09:08 DEBUG    matplotlib.backends.backend_pdf  - Writing TrueType font.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/dialogue/stories.md' is 'unk'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/dialogue/stories_basic.md' is 'unk'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/dialogue/stories_extra.md' is 'unk'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/dialogue/stories_test.md' is 'unk'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/dialogue/stories_training.md' is 'unk'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/nlu.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.acquaintance.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.age.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.annoying.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.answer_my_question.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.bad.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.be_clever.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.beautiful.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.birth_date.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.boring.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.boss.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.busy.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.can_you_help.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.chatbot.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.clever.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.crazy.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.fired.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.funny.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.good.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.happy.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.hobby.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.hungry.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.marry_user.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.my_friend.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.name.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.occupation.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.origin.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.ready.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.real.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.residence.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.right.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.sure.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.talk_to_me.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.there.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/agent.what_can_do.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/appraisal.bad.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/appraisal.good.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/appraisal.no_problem.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/appraisal.thank_you.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/appraisal.welcome.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/appraisal.well_done.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/confirmation.cancel.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/confirmation.no.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/confirmation.yes.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/dialog.hold_on.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/dialog.hug.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/dialog.i_do_not_care.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/dialog.sorry.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/dialog.what_do_you_mean.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/dialog.wrong.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/emotions.ha_ha.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/emotions.wow.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/greetings.bye.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/greetings.goodevening.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/greetings.goodmorning.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/greetings.goodnight.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/greetings.hello.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/greetings.how_are_you.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/greetings.nice_to_meet_you.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/greetings.nice_to_see_you.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/greetings.nice_to_talk_to_you.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/greetings.whatsup.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.angry.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.back.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.bored.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.busy.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.can_not_sleep.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.does_not_want_to_talk.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.excited.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.going_to_bed.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.good.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.happy.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.has_birthday.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.here.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.joking.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.likes_agent.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.lonely.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.looks_like.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.loves_agent.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.misses_agent.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.needs_advice.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.sad.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.sleepy.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.testing_agent.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.tired.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.waits.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.wants_to_see_agent_again.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.wants_to_talk.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.nlu.training_data.loading  - Training data format of 'data/nlu/smalltalk/user.will_be_back.md' is 'md'.
2020-02-24 10:09:09 DEBUG    rasa.model  - Extracted model to '/tmp/tmpucmn1ob4'.
2020-02-24 10:09:09 INFO     absl  - Using /tmp/tfhub_modules to cache modules.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/963a7a83964f4719a4b9ca735dfa0690_agent.acquaintance.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/1442bfe71c54467cb04cfb10078cf3a4_agent.age.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/d16830a2da09450186c2864fa8c195f0_agent.annoying.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/741c320392dd4bf1ab082226e4bf28c5_agent.answer_my_question.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/7b9b758329554fcab81d2123df6ac45f_agent.bad.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/e6089ddd6b634f4da054b09dbe22c9a0_agent.be_clever.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/a6366bff828b417488c0d35bd97005da_agent.beautiful.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/0777936f25ce43ffb261da43085b0dff_agent.birth_date.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/9673ecb3e83b4bd09fd79a21ca8ebeff_agent.boring.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/03106926e4bc47228888c4576c0d04b1_agent.boss.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/6f66406652ae4820bcfce66a63459f35_agent.busy.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/048ba04552a14b738be0069d46a12c35_agent.can_you_help.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/80a8b3c160724b96bc6e66bc1c2a6607_agent.chatbot.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/747d596fc992419da53d39e89a5ba44f_agent.clever.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/395ff84008264088af05faa7879334b7_agent.crazy.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/4517b3d28265476f8ef9390bc21f4036_agent.fired.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/7b3c61ecb660416980bfb5eb30028b26_agent.funny.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/80fb9cc62d094c3694fe3eb5af4406b9_agent.good.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/96033c1c4cbb473f8c2adf669ab87a32_agent.happy.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/9c05f139c60148d998ccdfb8e8825696_agent.hobby.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/555fc9f8c8cf4227a9c3065f9b8105b6_agent.hungry.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/401449c02d2a4140930977a948798a5b_agent.marry_user.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/19291c19435e49eaa910f0f82f65d69d_agent.my_friend.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/fe6f95405b4e4f76aeb69053e058645d_agent.name.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/05f75923dd2f4bf2a36a456f49bdc1cf_agent.occupation.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/a68e561ad1eb42358c3db82d418ce222_agent.origin.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/1dec57b672b94d9e9198a1fc4a874ad8_agent.ready.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/528a44acbb0c4edc88df92a7605057d7_agent.real.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/13fd78c6981f40199db1340f0135e47d_agent.residence.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/5d69e4410b4b46e7b19934d9af1f4a83_agent.right.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/8040e627fb8e44f1a231d4ed0ef5dd25_agent.sure.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/11f4513a4ee240cd90db2505b78ef3fe_agent.talk_to_me.md' is 'md'.
2020-02-24 10:09:17 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/bb575c6fcecd487b91258f558e1e8d23_agent.there.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/c984eef37e4d42449e491f309a74483e_agent.what_can_do.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/bc10854fe292499fae47b75ba8e10c0d_appraisal.bad.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/a953bc0c7c0544db950ae42447754993_appraisal.good.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/506b822765e54f51a0c0c2c5f18c5ddc_appraisal.no_problem.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/1827fdde628448ad960019724be5ae6b_appraisal.thank_you.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/89836f76ef43459292a11bb4a99a4678_appraisal.welcome.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/d99793e9b06746729e07500e9d8243b2_appraisal.well_done.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/2b6297cb39a1483090be372d10b1b9d4_confirmation.cancel.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/bc6ee961cd6d46009ac285f2e7f30773_confirmation.no.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/f6a24ff9ab904837b3a9414cc7efe3fd_confirmation.yes.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/81c58c16f050446389c835c088dea9cc_dialog.hold_on.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/32bb99f68b6544bb9189962451804359_dialog.hug.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/67a5717632a54648b7a9c04bfe54920f_dialog.i_do_not_care.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/d1239360c23a4b4f8f884cf72ae4e85c_dialog.sorry.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/4507fe31e2ca47458aefde94df0a4af1_dialog.what_do_you_mean.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/b2247b0919ad4c82993b95a2600a8c6b_dialog.wrong.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/ef6d9ade21874666b99824dbc58ae8ea_emotions.ha_ha.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/c010f69e51d94e819495602f4d0646d8_emotions.wow.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/1a13d2e4cc674f43ab5995b81e92ef2c_greetings.bye.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/91f0c39a766a4e7bb5f12d77fb196b17_greetings.goodevening.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/6d6814d4c1dc437ab09bc3c3e2c076ae_greetings.goodmorning.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/00435c6d7488412287ff70a84d9728bd_greetings.goodnight.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/513885ad7a8d49c0b621077332525ee1_greetings.hello.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/0c77bea8689a4492972a4c79f618bbdb_greetings.how_are_you.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/02b22ce5495f4b6aacdfd28e3682f1a4_greetings.nice_to_meet_you.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/dba4dfd3f81c40de8b6c4df42f1de58d_greetings.nice_to_see_you.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/eff5525128d349a1997755f01ae2a928_greetings.nice_to_talk_to_you.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/537d4d0c29854573a98d87f7ca399472_greetings.whatsup.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/9d9ae95206504394b266cdcaedca9d67_nlu.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/8f06fc3daa5745448fd226e6522d8c1e_user.angry.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/7e0a3eedcdc346c8abdd708a068ae3e7_user.back.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/663e80b0484d43be970c3e4c7dedb3f9_user.bored.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/21c39a43303d43dcb187427eabbd0394_user.busy.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/23609ddc99d44674a4dcfea301637301_user.can_not_sleep.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/df3fd4cec5ce427da10fc8939421b7eb_user.does_not_want_to_talk.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/0cdf631161e34261a1f8800c135d4baa_user.excited.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/8eb3ce7845b946898d7ab582b2aa014b_user.going_to_bed.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/83b70f9cc5eb4e2bb9080ea23d4ebe23_user.good.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/b966525d40a343518ac29c8c85986669_user.happy.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/23820ec18454462c978863954d00c689_user.has_birthday.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/5d7007e64fcf436a9c9b9894deed14a7_user.here.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/9da48924d3dd455b919258f03fce3ae1_user.joking.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/476c1275935f45e99aa9da9411b0a6d9_user.likes_agent.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/f8d0051b78404bd5b1587089876a0d3d_user.lonely.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/5bf2735082e94d38a9ffc1cc578df5d6_user.looks_like.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/aff0b4d3c2a34cff9d2cd5d512ca9d5a_user.loves_agent.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/1614f49559da470684476b2bed04912e_user.misses_agent.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/b9ee9684a9a24ccba39d01255bd8c31f_user.needs_advice.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/0385c388aaeb4d009c5499b1a1e1c277_user.sad.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/26949dc8c4224db18cfbe31bd01b3feb_user.sleepy.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/d73a615cb4524cc9804749d8c5c4e6bd_user.testing_agent.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/ebc13cba2ff840c8bc2bb4ef4ee1ef2e_user.tired.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/f19f41c26faa4cc5a1cc568c5464a0ea_user.waits.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/34acb7c27e0e4ea6ba931f683bb9bcd4_user.wants_to_see_agent_again.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/c2a79fd5491a4c73b7f70429f4fae537_user.wants_to_talk.md' is 'md'.
2020-02-24 10:09:18 DEBUG    rasa.nlu.training_data.loading  - Training data format of '/tmp/tmpxng80ty7/323955e4576e416584012f4aa99ae3d0_user.will_be_back.md' is 'md'.
2020-02-24 10:09:18 INFO     rasa.nlu.test  - Running model for predictions:
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2449/2449 [00:43<00:00, 56.62it/s]
2020-02-24 10:10:01 INFO     rasa.nlu.test  - Intent evaluation results:
2020-02-24 10:10:01 INFO     rasa.nlu.test  - Intent Evaluation: Only considering those 2449 examples that have a defined intent out of 2449 examples
2020-02-24 10:10:01 INFO     rasa.nlu.test  - Classification report saved to results/intent_report.json.
2020-02-24 10:10:01 INFO     rasa.nlu.test  - Incorrect intent predictions saved to results/intent_errors.json.
2020-02-24 10:10:01 DEBUG    rasa.nlu.test  -

These intent examples could not be classified correctly:
[{'text': 'what are you upto?', 'intent': 'agent.acquaintance', 'intent_prediction': {'name': 'greetings.whatsup', 'confidence': 0.5551027655601501}}, {'text': 'brilliant', 'intent': 'agent.clever', 'intent_prediction': {'name': 'appraisal.good', 'confidence': 0.4388422966003418}}, {'text': 'speak with me', 'intent': 'agent.talk_to_me', 'intent_prediction': {'name': 'user.wants_to_talk', 'confidence': 0.5329943895339966}}, {'text': 'what can you do for me?', 'intent': 'agent.what_can_do', 'intent_prediction': {'name': 'agent.acquaintance', 'confidence': 0.7208907604217529}}, {'text': 'good', 'intent': 'appraisal.good', 'intent_prediction': {'name': 'user.good', 'confidence': 0.49830812215805054}}, {'text': 'nice', 'intent': 'appraisal.good', 'intent_prediction': {'name': 'agent.good', 'confidence': 0.6217567324638367}}, {'text': 'amazing', 'intent': 'appraisal.good', 'intent_prediction': {'name': 'appraisal.well_done', 'confidence': 0.4023098647594452}}, {'text': 'marvelous', 'intent': 'appraisal.good', 'intent_prediction': {'name': 'appraisal.well_done', 'confidence': 0.50665682554245}}, {'text': 'splendid', 'intent': 'appraisal.good', 'intent_prediction': {'name': 'appraisal.well_done', 'confidence': 0.48590222001075745}}, {'text': 'so nice of you', 'intent': 'appraisal.thank_you', 'intent_prediction': {'name': 'appraisal.good', 'confidence': 0.5358421206474304}}, {'text': 'fantastic', 'intent': 'appraisal.well_done', 'intent_prediction': {'name': 'appraisal.good', 'confidence': 0.4823855757713318}}, {'text': 'brilliant', 'intent': 'appraisal.well_done', 'intent_prediction': {'name': 'appraisal.good', 'confidence': 0.4388422966003418}}, {'text': 'coool', 'intent': 'confirmation.yes', 'intent_prediction': {'name': 'agent.good', 'confidence': 0.7931346297264099}}, {'text': 'amazing', 'intent': 'emotions.wow', 'intent_prediction': {'name': 'appraisal.well_done', 'confidence': 0.4023098647594452}}, {'text': "I'm glad to see you", 'intent': 'greetings.nice_to_see_you', 'intent_prediction': {'name': 'user.happy', 'confidence': 0.5028986930847168}}, {'text': "It's boring", 'intent': 'user.bored', 'intent_prediction': {'name': 'agent.boring', 'confidence': 0.630631685256958}}, {'text': "It's good", 'intent': 'user.good', 'intent_prediction': {'name': 'appraisal.good', 'confidence': 0.5598580837249756}}, {'text': "I'm here", 'intent': 'user.here', 'intent_prediction': {'name': 'user.back', 'confidence': 0.6181103587150574}}]
2020-02-24 10:10:01 DEBUG    matplotlib.colorbar  - locator: <matplotlib.colorbar._ColorbarLogLocator object at 0x7fa618f60b00>
2020-02-24 10:10:01 DEBUG    matplotlib.colorbar  - Using auto colorbar locator on colorbar
2020-02-24 10:10:01 DEBUG    matplotlib.colorbar  - locator: <matplotlib.colorbar._ColorbarLogLocator object at 0x7fa618f60b00>
2020-02-24 10:10:01 DEBUG    matplotlib.colorbar  - Setting pcolormesh
2020-02-24 10:10:01 INFO     rasa.nlu.test  - Confusion matrix, without normalization:
[[22  0  0 ...  0  0  0]
 [ 0 21  0 ...  0  0  0]
 [ 0  0 20 ...  0  0  0]
 ...
 [ 0  0  0 ... 62  0  0]
 [ 0  0  0 ...  0  7  0]
 [ 0  0  0 ...  0  0 19]]
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:10 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:15 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:19 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:23 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - ticklocs array([1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - vmin 1.0 vmax 88.0
2020-02-24 10:10:30 DEBUG    matplotlib.ticker  - ticklocs array([2.e-01, 3.e-01, 4.e-01, 5.e-01, 6.e-01, 7.e-01, 8.e-01, 9.e-01,
       2.e+00, 3.e+00, 4.e+00, 5.e+00, 6.e+00, 7.e+00, 8.e+00, 9.e+00,
       2.e+01, 3.e+01, 4.e+01, 5.e+01, 6.e+01, 7.e+01, 8.e+01, 9.e+01,
       2.e+02, 3.e+02, 4.e+02, 5.e+02, 6.e+02, 7.e+02, 8.e+02, 9.e+02,
       2.e+03, 3.e+03, 4.e+03, 5.e+03, 6.e+03, 7.e+03, 8.e+03, 9.e+03])

Strange that it is showing so many debug messages from matplotlib. Can you maybe store your log output to a file an upload the file here? E.g. rasa test --debug > log.out 2>&1.

Here you are log.out (1.6 MB)