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  1. .gitignore +163 -0
  2. README.md +13 -8
  3. __init__.py +0 -0
  4. __pycache__/demo.cpython-310.pyc +0 -0
  5. best_models.csv +11 -0
  6. conda_installs.txt +13 -0
  7. data/continuous.csv +33 -0
  8. data/demand_forecasting_demo_data.csv +2573 -0
  9. data/demand_forecasting_demo_models.csv +11 -0
  10. data/fuzzy.csv +261 -0
  11. data/fuzzy_2.csv +37 -0
  12. data/resource.md +1 -0
  13. demo.py +121 -0
  14. environment.yml +222 -0
  15. forecast_result.csv +521 -0
  16. gr_app/GradioApp.py +162 -0
  17. gr_app/__init__.py +1 -0
  18. gr_app/__pycache__/GradioApp.cpython-310.pyc +0 -0
  19. gr_app/__pycache__/__init__.cpython-310.pyc +0 -0
  20. gr_app/__pycache__/args.cpython-310.pyc +0 -0
  21. gr_app/args.py +16 -0
  22. model.csv +11 -0
  23. notebooks/res.txt +0 -0
  24. notebooks/test.ipynb +828 -0
  25. src/__init__.py +0 -0
  26. src/__pycache__/__init__.cpython-310.pyc +0 -0
  27. src/__pycache__/__init__.cpython-311.pyc +0 -0
  28. src/__pycache__/avtive_models.cpython-310.pyc +0 -0
  29. src/__pycache__/main.cpython-310.pyc +0 -0
  30. src/__pycache__/main.cpython-311.pyc +0 -0
  31. src/avtive_models.py +19 -0
  32. src/forecast/Prophet.py +22 -0
  33. src/forecast/__init__.py +0 -0
  34. src/forecast/__pycache__/Prophet.cpython-310.pyc +0 -0
  35. src/forecast/__pycache__/Prophet.cpython-311.pyc +0 -0
  36. src/forecast/__pycache__/__init__.cpython-310.pyc +0 -0
  37. src/forecast/__pycache__/__init__.cpython-311.pyc +0 -0
  38. src/functions/__init__.py +0 -0
  39. src/functions/__pycache__/__init__.cpython-310.pyc +0 -0
  40. src/functions/__pycache__/__init__.cpython-311.pyc +0 -0
  41. src/functions/__pycache__/check_input.cpython-310.pyc +0 -0
  42. src/functions/__pycache__/check_input.cpython-311.pyc +0 -0
  43. src/functions/__pycache__/itmtt_scores.cpython-310.pyc +0 -0
  44. src/functions/__pycache__/mase.cpython-310.pyc +0 -0
  45. src/functions/__pycache__/order_qty_rmse.cpython-310.pyc +0 -0
  46. src/functions/check_input.py +5 -0
  47. src/functions/itmtt_scores.py +29 -0
  48. src/functions/mase.py +13 -0
  49. src/functions/order_qty_rmse.py +13 -0
  50. src/functions/sort_res.py +6 -0
.gitignore ADDED
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+ # Byte-compiled / optimized / DLL files
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+ __pycache__/
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+ *.py[cod]
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+ *$py.class
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+
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+ # C extensions
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+ *.so
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+
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+ # Distribution / packaging
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+ .Python
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+ build/
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+ develop-eggs/
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+ dist/
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+ downloads/
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+ eggs/
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+ .eggs/
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+ lib/
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+ lib64/
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+ wheels/
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+ share/python-wheels/
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+ *.egg-info/
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+ .installed.cfg
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+ *.egg
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+ MANIFEST
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+
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+ # PyInstaller
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+ # Usually these files are written by a python script from a template
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+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
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+ *.manifest
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+ *.spec
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+ *.log
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+ local_settings.py
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+ db.sqlite3
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+ db.sqlite3-journal
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+
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+ # Flask stuff:
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+ instance/
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+ .webassets-cache
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+ # Scrapy stuff:
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+ # Sphinx documentation
72
+ docs/_build/
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+
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+ # PyBuilder
75
+ .pybuilder/
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+ target/
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+
78
+ # Jupyter Notebook
79
+ .ipynb_checkpoints
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+
81
+ # IPython
82
+ profile_default/
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+ ipython_config.py
84
+
85
+ # pyenv
86
+ # For a library or package, you might want to ignore these files since the code is
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+ # intended to run in multiple environments; otherwise, check them in:
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+ # .python-version
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+
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+ # pipenv
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+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
93
+ # having no cross-platform support, pipenv may install dependencies that don't work, or not
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+ # install all needed dependencies.
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+ #Pipfile.lock
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+
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+ # poetry
98
+ # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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+ # This is especially recommended for binary packages to ensure reproducibility, and is more
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+ # commonly ignored for libraries.
101
+ # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
102
+ #poetry.lock
103
+
104
+ # pdm
105
+ # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
106
+ #pdm.lock
107
+ # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
108
+ # in version control.
109
+ # https://pdm.fming.dev/#use-with-ide
110
+ .pdm.toml
111
+
112
+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
113
+ __pypackages__/
114
+
115
+ # Celery stuff
116
+ celerybeat-schedule
117
+ celerybeat.pid
118
+
119
+ # SageMath parsed files
120
+ *.sage.py
121
+
122
+ # Environments
123
+ .env
124
+ .venv
125
+ env/
126
+ venv/
127
+ ENV/
128
+ env.bak/
129
+ venv.bak/
130
+
131
+ # Spyder project settings
132
+ .spyderproject
133
+ .spyproject
134
+
135
+ # Rope project settings
136
+ .ropeproject
137
+
138
+ # mkdocs documentation
139
+ /site
140
+
141
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142
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143
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144
+ dmypy.json
145
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146
+ # Pyre type checker
147
+ .pyre/
148
+
149
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150
+ .pytype/
151
+
152
+ # Cython debug symbols
153
+ cython_debug/
154
+
155
+ # PyCharm
156
+ # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
157
+ # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
158
+ # and can be added to the global gitignore or merged into this file. For a more nuclear
159
+ # option (not recommended) you can uncomment the following to ignore the entire idea folder.
160
+ #.idea/
161
+
162
+ .DS_Store
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+ */.DS_Store
README.md CHANGED
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  ---
2
- title: Demand Forecasting
3
- emoji: 🏆
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- colorTo: green
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- sdk_version: 3.47.1
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- app_file: app.py
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- pinned: false
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  ---
 
 
 
 
11
 
12
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
1
  ---
2
+ title: demand-forecasting
3
+ app_file: demo.py
 
 
4
  sdk: gradio
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+ sdk_version: 3.41.0
 
 
6
  ---
7
+ ### Update conda environment
8
+ ```sh
9
+ conda env update --file environment.yml --prune
10
+ ```
11
 
12
+ ### Add conda environment to ipykernel
13
+ ```sh
14
+ python -m ipykernel install --user --name demand-forecasting
15
+ ```
16
+
17
+ ### to run gradio app
__init__.py ADDED
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+ (if conda version of gradio doesn't work)
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+ pip install gradio
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data/demand_forecasting_demo_models.csv ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ sku,best_model,characteristic,RMSE
2
+ sku-0,fft_plus,continuous,20.29778313018444
3
+ sku-1,holt_winters_plus,continuous,48.49842843820416
4
+ sku-2,prophet_plus,fuzzy,39.28846310729568
5
+ sku-3,prophet_plus,fuzzy_transient,14.593201789242087
6
+ sku-4,prophet_plus,fuzzy,10.7747925198657
7
+ sku-5,prophet_plus,fuzzy,28.33012802382216
8
+ sku-6,ceif_plus,fuzzy,37.84242038358283
9
+ sku-7,holt_winters_plus,continuous,15.959770854065722
10
+ sku-8,prophet_plus,fuzzy,13.778467035419936
11
+ sku-9,prophet_plus,fuzzy,12.843706019437128
data/fuzzy.csv ADDED
@@ -0,0 +1,261 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ datetime,y
2
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3
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4
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5
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6
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7
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8
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9
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10
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11
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12
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13
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14
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15
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16
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17
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18
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19
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20
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21
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22
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23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
+ 2019-04-14,0
52
+ 2019-04-21,0
53
+ 2019-04-28,0
54
+ 2019-05-05,0
55
+ 2019-05-12,0
56
+ 2019-05-19,2
57
+ 2019-05-26,8
58
+ 2019-06-02,0
59
+ 2019-06-09,0
60
+ 2019-06-16,0
61
+ 2019-06-23,0
62
+ 2019-06-30,2
63
+ 2019-07-07,8
64
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65
+ 2019-07-21,10
66
+ 2019-07-28,0
67
+ 2019-08-04,12
68
+ 2019-08-11,2
69
+ 2019-08-18,5
70
+ 2019-08-25,0
71
+ 2019-09-01,7
72
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73
+ 2019-09-15,0
74
+ 2019-09-22,0
75
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76
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77
+ 2019-10-13,2
78
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79
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80
+ 2019-11-03,27
81
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82
+ 2019-11-17,12
83
+ 2019-11-24,9
84
+ 2019-12-01,22
85
+ 2019-12-08,4
86
+ 2019-12-15,0
87
+ 2019-12-22,0
88
+ 2019-12-29,22
89
+ 2020-01-05,0
90
+ 2020-01-12,5
91
+ 2020-01-19,4
92
+ 2020-01-26,9
93
+ 2020-02-02,10
94
+ 2020-02-09,8
95
+ 2020-02-16,5
96
+ 2020-02-23,0
97
+ 2020-03-01,30
98
+ 2020-03-08,0
99
+ 2020-03-15,10
100
+ 2020-03-22,8
101
+ 2020-03-29,16
102
+ 2020-04-05,10
103
+ 2020-04-12,0
104
+ 2020-04-19,3
105
+ 2020-04-26,10
106
+ 2020-05-03,0
107
+ 2020-05-10,0
108
+ 2020-05-17,4
109
+ 2020-05-24,2
110
+ 2020-05-31,4
111
+ 2020-06-07,11
112
+ 2020-06-14,10
113
+ 2020-06-21,5
114
+ 2020-06-28,10
115
+ 2020-07-05,2
116
+ 2020-07-12,11
117
+ 2020-07-19,3
118
+ 2020-07-26,44
119
+ 2020-08-02,0
120
+ 2020-08-09,0
121
+ 2020-08-16,0
122
+ 2020-08-23,140
123
+ 2020-08-30,40
124
+ 2020-09-06,0
125
+ 2020-09-13,0
126
+ 2020-09-20,24
127
+ 2020-09-27,12
128
+ 2020-10-04,2
129
+ 2020-10-11,3
130
+ 2020-10-18,13
131
+ 2020-10-25,13
132
+ 2020-11-01,14
133
+ 2020-11-08,3
134
+ 2020-11-15,10
135
+ 2020-11-22,20
136
+ 2020-11-29,0
137
+ 2020-12-06,0
138
+ 2020-12-13,0
139
+ 2020-12-20,0
140
+ 2020-12-27,0
141
+ 2021-01-03,0
142
+ 2021-01-10,0
143
+ 2021-01-17,0
144
+ 2021-01-24,0
145
+ 2021-01-31,0
146
+ 2021-02-07,0
147
+ 2021-02-14,60
148
+ 2021-02-21,0
149
+ 2021-02-28,0
150
+ 2021-03-07,0
151
+ 2021-03-14,0
152
+ 2021-03-21,10
153
+ 2021-03-28,0
154
+ 2021-04-04,0
155
+ 2021-04-11,0
156
+ 2021-04-18,0
157
+ 2021-04-25,30
158
+ 2021-05-02,9
159
+ 2021-05-09,7
160
+ 2021-05-16,0
161
+ 2021-05-23,3
162
+ 2021-05-30,5
163
+ 2021-06-06,3
164
+ 2021-06-13,15
165
+ 2021-06-20,10
166
+ 2021-06-27,32
167
+ 2021-07-04,0
168
+ 2021-07-11,10
169
+ 2021-07-18,10
170
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171
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172
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173
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174
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175
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176
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177
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178
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179
+ 2021-09-26,5
180
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181
+ 2021-10-10,24
182
+ 2021-10-17,18
183
+ 2021-10-24,6
184
+ 2021-10-31,7
185
+ 2021-11-07,8
186
+ 2021-11-14,25
187
+ 2021-11-21,10
188
+ 2021-11-28,10
189
+ 2021-12-05,2
190
+ 2021-12-12,2
191
+ 2021-12-19,0
192
+ 2021-12-26,0
193
+ 2022-01-02,2
194
+ 2022-01-09,4
195
+ 2022-01-16,3
196
+ 2022-01-23,10
197
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198
+ 2022-02-06,0
199
+ 2022-02-13,20
200
+ 2022-02-20,25
201
+ 2022-02-27,10
202
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203
+ 2022-03-13,10
204
+ 2022-03-20,7
205
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206
+ 2022-04-03,3
207
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208
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209
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210
+ 2022-05-01,0
211
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212
+ 2022-05-15,10
213
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214
+ 2022-05-29,9
215
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216
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218
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219
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220
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221
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222
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224
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225
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226
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228
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229
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230
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242
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250
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252
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254
+ 2023-03-05,13
255
+ 2023-03-12,10
256
+ 2023-03-19,0
257
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258
+ 2023-04-02,10
259
+ 2023-04-09,8
260
+ 2023-04-16,10
261
+ 2023-04-23,5
data/fuzzy_2.csv ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ datetime,y
2
+ 2020-01-31,50.0
3
+ 2020-02-29,0.0
4
+ 2020-03-31,300.0
5
+ 2020-04-30,0.0
6
+ 2020-05-31,500.0
7
+ 2020-06-30,1000.0
8
+ 2020-07-31,1000.0
9
+ 2020-08-31,500.0
10
+ 2020-09-30,0.0
11
+ 2020-10-31,1000.0
12
+ 2020-11-30,1000.0
13
+ 2020-12-31,500.0
14
+ 2021-01-31,525.0
15
+ 2021-02-28,750.0
16
+ 2021-03-31,250.0
17
+ 2021-04-30,0.0
18
+ 2021-05-31,0.0
19
+ 2021-06-30,975.0
20
+ 2021-07-31,975.0
21
+ 2021-08-31,1550.0
22
+ 2021-09-30,1309.0
23
+ 2021-10-31,2450.0
24
+ 2021-11-30,2360.0
25
+ 2021-12-31,3670.0
26
+ 2022-01-31,5530.0
27
+ 2022-02-28,2990.0
28
+ 2022-03-31,1050.0
29
+ 2022-04-30,2750.0
30
+ 2022-05-31,6124.0
31
+ 2022-06-30,3510.0
32
+ 2022-07-31,4000.0
33
+ 2022-08-31,3400.0
34
+ 2022-09-30,2500.0
35
+ 2022-10-31,3000.0
36
+ 2022-11-30,3800.0
37
+ 2022-12-31,2560.0
data/resource.md ADDED
@@ -0,0 +1 @@
 
 
1
+ test.csv came from SKU 8972413061 from CID016 data from Isuzu
demo.py ADDED
@@ -0,0 +1,121 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+
3
+ # from arguments import init_args
4
+ from gr_app.GradioApp import GradioApp
5
+ from gr_app import args
6
+
7
+ app = GradioApp()
8
+
9
+ demo = gr.Blocks(**args.block)
10
+
11
+ with demo:
12
+ warning = gr.Warning()
13
+ gr.Markdown('# Sentient.io - Demand Forecasting')
14
+ gr.Markdown('Demo for demand forecasting pipeline')
15
+
16
+ gr.Markdown('---')
17
+
18
+ gr.Markdown('# Step 1 - Load Data')
19
+ with gr.Row():
20
+ gr.Markdown('''
21
+ Use button "Load Demo Data" for a quick demo with pre-loaded data. For uploading your own data, please follow the below requirements.
22
+
23
+ ### Data Requirements:
24
+ - Time series data have to be in CSV format
25
+ - Data must contains datetime, y and sku columns.
26
+ - Multiple SKUs can put in to same CSV
27
+ - Time interval in data must be consistent
28
+ - Missing value have to be filled up
29
+ ''')
30
+
31
+ with gr.Column():
32
+ btn_load_data = gr.Button('Load Demo Data')
33
+
34
+ gr.Markdown('------ or ------',
35
+ elem_classes="demo_app_text_center")
36
+
37
+ file_upload_data = gr.File(**args.file_upload_data)
38
+
39
+ df_ts_data = gr.DataFrame(**args.df_ts_data)
40
+
41
+ gr.Markdown('---')
42
+
43
+ gr.Markdown('# Step 2 - Model Selection')
44
+
45
+ with gr.Row():
46
+ gr.Markdown('''
47
+ Train and evaluate model, identify data characteristics and select the best performing model. This step only need to run when the market regime shifted or when need to to re-select the model.
48
+
49
+ - Click "Use Demo Data" Button if the demo data set has been loaded in Step 1
50
+ - Else, directly proceed to model selection
51
+ - Only upload dataset if the model select process had been previously done, and you have save a copy of the CSV response.
52
+ ''')
53
+
54
+ with gr.Column():
55
+ btn_load_model_data = gr.Button('Use Demo Data')
56
+ btn_model_selection = gr.Button('Model Selection', variant='primary')
57
+ gr.Markdown('Upload previous model selection result (if have):')
58
+ file_upload_model_data = gr.File(**args.file_upload_model_data)
59
+
60
+ df_model_data = gr.DataFrame()
61
+ file_model_data = gr.File()
62
+
63
+ gr.Markdown('# Step 3 - Forecasting')
64
+
65
+ with gr.Row():
66
+ gr.Markdown(
67
+ 'This step only can be done when model selection process is completed.')
68
+
69
+ with gr.Column():
70
+ gr.Markdown('''
71
+ ### Forecast Horizon
72
+ Max horizon will be 20% of provided data range. The unit will be same as the time series data time interval.
73
+ ''')
74
+ slider_forecast_horizon = gr.Slider(**args.slider_forecast_horizon)
75
+
76
+ btn_forecast = gr.Button("Forecast", variant='primary')
77
+
78
+ df_forecast = gr.DataFrame(**args.df_forecast)
79
+ file_forecast = gr.File()
80
+
81
+ # ============= #
82
+ # = Functions = #
83
+ # ============= #
84
+
85
+ btn_load_data.click(
86
+ app.btn_load_data__click,
87
+ [],
88
+ [df_ts_data, df_model_data, file_model_data, slider_forecast_horizon])
89
+
90
+ file_upload_data.upload(
91
+ app.file_upload_data__upload,
92
+ [file_upload_data],
93
+ [df_ts_data, df_model_data, file_model_data, slider_forecast_horizon])
94
+
95
+ file_upload_model_data.upload(
96
+ app.file_upload_model_data__upload,
97
+ [file_upload_model_data],
98
+ [df_model_data, file_model_data]
99
+ )
100
+
101
+ btn_load_model_data.click(
102
+ app.btn_load_model_data__click,
103
+ [], [df_model_data, file_model_data]
104
+ )
105
+
106
+ btn_model_selection.click(
107
+ app.btn_model_selection__click,
108
+ [], [df_model_data, file_model_data])
109
+
110
+ btn_forecast.click(
111
+ app.btn_forecast__click,
112
+ [], [df_forecast, file_forecast]
113
+ )
114
+
115
+ slider_forecast_horizon.change(
116
+ app.slider_forecast_horizon__update,
117
+ [slider_forecast_horizon],
118
+ [])
119
+
120
+
121
+ demo.launch()
environment.yml ADDED
@@ -0,0 +1,222 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: demand-forecasting
2
+ channels:
3
+ - plotly
4
+ - conda-forge
5
+ - anaconda
6
+ - defaults
7
+ dependencies:
8
+ - aiofiles=22.1.0
9
+ - aiosqlite=0.18.0
10
+ - anyio=3.5.0
11
+ # - appnope=0.1.2
12
+ - argon2-cffi=21.3.0
13
+ - argon2-cffi-bindings=21.2.0
14
+ - asttokens=2.0.5
15
+ - attrs=22.1.0
16
+ - babel=2.11.0
17
+ - backcall=0.2.0
18
+ - beautifulsoup4=4.12.2
19
+ - blas=1.0
20
+ - bleach=4.1.0
21
+ - bottleneck=1.3.5
22
+ - brotli=1.0.9
23
+ - brotli-bin=1.0.9
24
+ - brotlipy=0.7.0
25
+ - bzip2=1.0.8
26
+ - ca-certificates=2022.4.26
27
+ - cctools_osx-arm64=949.0.1
28
+ - certifi=2022.6.15
29
+ - cffi=1.15.1
30
+ - charset-normalizer=2.0.4
31
+ - clang=14.0.6
32
+ - clang-14=14.0.6
33
+ - clang_osx-arm64=14.0.6
34
+ - clangxx=14.0.6
35
+ - clangxx_osx-arm64=14.0.6
36
+ - cmdstan=2.31.0
37
+ - cmdstanpy=1.1.0
38
+ - comm=0.1.2
39
+ - compiler-rt=14.0.6
40
+ - compiler-rt_osx-arm64=14.0.6
41
+ - convertdate=2.3.2
42
+ - cryptography=41.0.3
43
+ - cycler=0.11.0
44
+ - debugpy=1.6.7
45
+ - decorator=5.1.1
46
+ - defusedxml=0.7.1
47
+ - entrypoints=0.4
48
+ - ephem=4.1.2
49
+ - exceptiongroup=1.0.4
50
+ - executing=0.8.3
51
+ - freetype=2.12.1
52
+ - giflib=5.2.1
53
+ - holidays=0.29
54
+ - icu=73.1
55
+ - idna=3.4
56
+ - importlib_resources=5.2.0
57
+ - ipykernel=6.25.0
58
+ - ipython=8.15.0
59
+ - ipython_genutils=0.2.0
60
+ - jedi=0.18.1
61
+ - jinja2=3.1.2
62
+ - joblib=1.3.2
63
+ - jpeg=9e
64
+ - json5=0.9.6
65
+ - jsonschema=4.17.3
66
+ - jupyter_client=7.4.9
67
+ - jupyter_core=5.3.0
68
+ - jupyter_events=0.6.3
69
+ - jupyter_server=1.23.4
70
+ - jupyter_server_fileid=0.9.0
71
+ - jupyter_server_ydoc=0.8.0
72
+ - jupyter_ydoc=0.2.4
73
+ - jupyterlab=3.6.3
74
+ - jupyterlab_pygments=0.1.2
75
+ - jupyterlab_server=2.22.0
76
+ - lcms2=2.12
77
+ - ld64_osx-arm64=530
78
+ - ldid=2.1.5
79
+ - lerc=3.0
80
+ - libbrotlicommon=1.0.9
81
+ - libbrotlidec=1.0.9
82
+ - libbrotlienc=1.0.9
83
+ - libclang-cpp14=14.0.6
84
+ - libcxx=14.0.6
85
+ - libdeflate=1.17
86
+ - libffi=3.4.4
87
+ # - libgfortran=5.0.0
88
+ - libgfortran5=11.3.0
89
+ - libiconv=1.16
90
+ - libllvm14=14.0.6
91
+ - libopenblas=0.3.21
92
+ - libpng=1.6.39
93
+ - libsodium=1.0.18
94
+ - libtiff=4.5.1
95
+ - libwebp=1.3.2
96
+ - libwebp-base=1.3.2
97
+ - libxml2=2.10.4
98
+ - libxslt=1.1.37
99
+ - llvm-openmp=14.0.6
100
+ - llvm-tools=14.0.6
101
+ - lunarcalendar=0.0.9
102
+ - lxml=4.9.3
103
+ - lz4-c=1.9.4
104
+ - make=4.3
105
+ - markupsafe=2.1.1
106
+ - matplotlib=3.7.2
107
+ - matplotlib-base=3.7.2
108
+ - matplotlib-inline=0.1.6
109
+ - mistune=0.8.4
110
+ - munkres=1.1.4
111
+ - nbclassic=0.5.5
112
+ - nbclient=0.5.13
113
+ - nbconvert=6.5.4
114
+ - nbformat=5.9.2
115
+ - ncurses=6.4
116
+ - nest-asyncio=1.5.6
117
+ - notebook=6.5.4
118
+ - notebook-shim=0.2.2
119
+ - numexpr=2.8.4
120
+ - numpy=1.25.2
121
+ - numpy-base=1.25.2
122
+ - openssl=3.1.3
123
+ - packaging=23.1
124
+ - pandocfilters=1.5.0
125
+ - parso=0.8.3
126
+ - pexpect=4.8.0
127
+ - pickleshare=0.7.5
128
+ - pip=23.2.1
129
+ - platformdirs=3.10.0
130
+ - plotly=5.16.1
131
+ - prometheus_client=0.14.1
132
+ - prompt-toolkit=3.0.36
133
+ - prophet=1.1.4
134
+ - psutil=5.9.0
135
+ - ptyprocess=0.7.0
136
+ - pure_eval=0.2.2
137
+ - pycparser=2.21
138
+ - pygments=2.15.1
139
+ - pymeeus=0.5.11
140
+ - pyopenssl=23.2.0
141
+ - pyparsing=3.0.9
142
+ - pyrsistent=0.18.0
143
+ - pysocks=1.7.1
144
+ - python=3.10.12
145
+ - python-dateutil=2.8.2
146
+ - python-dotenv=1.0.0
147
+ - python-fastjsonschema=2.16.2
148
+ - python-json-logger=2.0.7
149
+ - pytz=2023.3.post1
150
+ - pyyaml=6.0
151
+ - pyzmq=23.2.0
152
+ - readline=8.2
153
+ - requests=2.31.0
154
+ - rfc3339-validator=0.1.4
155
+ - rfc3986-validator=0.1.1
156
+ - scikit-learn=1.3.0
157
+ - scipy=1.11.1
158
+ - seaborn=0.11.2
159
+ - send2trash=1.8.0
160
+ - setuptools=68.0.0
161
+ - six=1.16.0
162
+ - sniffio=1.2.0
163
+ - soupsieve=2.4
164
+ - sqlite=3.41.2
165
+ - stack_data=0.2.0
166
+ - tapi=1100.0.11
167
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forecast_result.csv ADDED
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gr_app/GradioApp.py ADDED
@@ -0,0 +1,162 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pandas as pd
2
+ import math
3
+ from src.main import DemandForecasting
4
+
5
+ import gradio as gr
6
+
7
+
8
+ class GradioApp():
9
+ def __init__(self):
10
+ self.forecaster = DemandForecasting()
11
+
12
+ self.ts_data = None # Time series data for model training and forecasting
13
+ self.model_data = None
14
+ self.skus = None
15
+
16
+ self.forecast_horizon = 1
17
+
18
+ def __set_ts_data(self, path):
19
+ self.ts_data = pd.read_csv(path)
20
+ self.skus = self.ts_data['sku'].unique()
21
+
22
+ self.model_data = pd.DataFrame(
23
+ {
24
+ 'sku': self.skus,
25
+ 'best_model': '',
26
+ 'characteristic': '',
27
+ 'RMSE': ''
28
+ }
29
+ )
30
+
31
+ def __set_model(self, model_df):
32
+ if (self.skus is None):
33
+ raise gr.Error(
34
+ 'Incorrect SKUs, time series data must be loaded and SKUs must match.')
35
+ if (set(self.skus) - set(model_df['sku']) != set()):
36
+ raise gr.Error(
37
+ 'SKUs in provided model select data does not match SKUs in timeseries data.'
38
+ )
39
+
40
+ self.model_data = model_df
41
+
42
+ def btn_load_data__click(self):
43
+ print('btn_load_data__click')
44
+ self.__set_ts_data('./data/demand_forecasting_demo_data.csv')
45
+
46
+ return (self.update__df_ts_data(),
47
+ self.update__df_model_data(),
48
+ self.update__file_model_data(),
49
+ self.update__slider_forecast_horizon())
50
+
51
+ def file_upload_data__upload(self, file):
52
+ self.__set_ts_data(file.name)
53
+
54
+ return (self.update__df_ts_data(),
55
+ self.update__df_model_data(),
56
+ self.update__file_model_data(),
57
+ self.update__slider_forecast_horizon())
58
+
59
+ def file_upload_model_data__upload(self, file):
60
+ model_df = pd.read_csv(file.name)
61
+ self.__set_model(model_df)
62
+
63
+ return (self.update__df_model_data(),
64
+ self.update__file_model_data())
65
+
66
+ def btn_load_model_data__click(self):
67
+
68
+ model_df = pd.read_csv(
69
+ './data/demand_forecasting_demo_models.csv')
70
+ self.__set_model(model_df)
71
+
72
+ return (self.update__df_model_data(),
73
+ self.update__file_model_data())
74
+
75
+ def btn_model_selection__click(self):
76
+ print('btn_model_selection__click')
77
+ for sku in self.skus:
78
+ print('Selecting model ', sku)
79
+ data = self.ts_data[self.ts_data['sku'] == sku]
80
+
81
+ # ----------------- #
82
+ # Feature Selection #
83
+ # ----------------- #
84
+ res = self.forecaster.forecast(
85
+ data, 0, model='all', run_test=True)
86
+
87
+ self.model_data.loc[self.model_data['sku'] ==
88
+ sku, 'characteristic'] = res['characteristic']
89
+
90
+ self.model_data.loc[self.model_data['sku'] ==
91
+ sku, 'best_model'] = res['forecast'][0]['model']
92
+ self.model_data.loc[self.model_data['sku'] ==
93
+ sku, 'RMSE'] = math.round(res['forecast'][0]['RMSE'], 2)
94
+
95
+ return (self.update__df_model_data(),
96
+ self.update__file_model_data())
97
+
98
+ def slider_forecast_horizon__update(self, slider):
99
+ # print('slider_forecast_horizon__update ', slider)
100
+ self.forecast_horizon = slider
101
+
102
+ def btn_forecast__click(self):
103
+ # ----------- #
104
+ # Forecasting #
105
+ # ----------- #
106
+ forecasts = []
107
+ for sku in self.skus:
108
+ print('Forecasting ', sku)
109
+ data = self.ts_data[self.ts_data['sku'] == sku]
110
+
111
+ # Drop sku column first, for now the pipeline doesn't take this column
112
+ data = data.drop('sku', axis=1)
113
+
114
+ model_data = self.model_data[self.model_data['sku'] == sku]
115
+ print(model_data)
116
+ model = model_data['best_model'].tolist()[0]
117
+ characteristic = model_data['characteristic'].tolist()[0]
118
+
119
+ # ----------------- #
120
+ # Feature Selection #
121
+ # ----------------- #
122
+ print(model, characteristic)
123
+ res = self.forecaster.forecast(
124
+ data, self.forecast_horizon, model=model, run_test=False, characteristic=characteristic)
125
+ forecast = pd.DataFrame(
126
+ res['forecast'][0]['forecast'], columns=['datetime', 'y'])
127
+ forecast['sku'] = sku
128
+ forecasts.append(forecast)
129
+
130
+ self.forecast = pd.concat(forecasts)
131
+
132
+ return (self.update__df_forecast(),
133
+ self.update__file_forecast())
134
+
135
+ # ======== #
136
+ # Updaters #
137
+ # ======== #
138
+
139
+ def update__file_model_data(self):
140
+ self.model_data.to_csv('./best_models.csv', index=False)
141
+ return gr.File.update(value='./best_models.csv')
142
+
143
+ def update__df_model_data(self):
144
+ return gr.DataFrame.update(value=self.model_data)
145
+
146
+ def update__df_ts_data(self):
147
+ return gr.DataFrame.update(value=self.ts_data)
148
+
149
+ def update__slider_forecast_horizon(self):
150
+ sku = self.skus[0]
151
+
152
+ max_horizon = int(
153
+ self.ts_data[self.ts_data['sku'] == sku].shape[0] * 0.2)
154
+
155
+ return gr.Slider.update(maximum=max_horizon)
156
+
157
+ def update__df_forecast(self):
158
+ return gr.DataFrame.update(self.forecast)
159
+
160
+ def update__file_forecast(self):
161
+ self.forecast.to_csv('./forecast_result.csv', index=False)
162
+ return gr.File.update(value='./forecast_result.csv')
gr_app/__init__.py ADDED
@@ -0,0 +1 @@
 
 
1
+
gr_app/__pycache__/GradioApp.cpython-310.pyc ADDED
Binary file (5.04 kB). View file
 
gr_app/__pycache__/__init__.cpython-310.pyc ADDED
Binary file (172 Bytes). View file
 
gr_app/__pycache__/args.cpython-310.pyc ADDED
Binary file (533 Bytes). View file
 
gr_app/args.py ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ block = {
2
+ 'css':
3
+ '''
4
+ .demo_app_group {padding: 1rem !important; color:red}
5
+
6
+ .demo_app_text_center {text-align: center}
7
+ '''
8
+ }
9
+
10
+ df_ts_data = {'height': 200}
11
+ df_forecast = {'height': 200}
12
+
13
+ file_upload_data = {'height': 80}
14
+ file_upload_model_data = {'height': 80}
15
+
16
+ slider_forecast_horizon = {'label': '', 'minimum': 1, 'step': 1, 'interactive':True}
model.csv ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ,sku,best_model,characteristic,RMSE
2
+ 0,sku-0,fft_plus,continuous,20.29778313018444
3
+ 1,sku-1,holt_winters_plus,continuous,48.49842843820416
4
+ 2,sku-2,prophet_plus,fuzzy,39.28846310729568
5
+ 3,sku-3,prophet_plus,fuzzy_transient,14.593201789242087
6
+ 4,sku-4,prophet_plus,fuzzy,10.7747925198657
7
+ 5,sku-5,prophet_plus,fuzzy,28.33012802382216
8
+ 6,sku-6,ceif_plus,fuzzy,37.84242038358283
9
+ 7,sku-7,holt_winters_plus,continuous,15.959770854065722
10
+ 8,sku-8,prophet_plus,fuzzy,13.778467035419936
11
+ 9,sku-9,prophet_plus,fuzzy,12.843706019437128
notebooks/res.txt ADDED
File without changes
notebooks/test.ipynb ADDED
@@ -0,0 +1,828 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 1,
6
+ "metadata": {},
7
+ "outputs": [
8
+ {
9
+ "name": "stderr",
10
+ "output_type": "stream",
11
+ "text": [
12
+ "/Users/qiaozhang/miniconda3/envs/demand-forecasting/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
13
+ " from .autonotebook import tqdm as notebook_tqdm\n"
14
+ ]
15
+ },
16
+ {
17
+ "name": "stdout",
18
+ "output_type": "stream",
19
+ "text": [
20
+ "apikey still available, logged in\n"
21
+ ]
22
+ }
23
+ ],
24
+ "source": [
25
+ "# To call functions outside of this folder\n",
26
+ "import sys \n",
27
+ "sys.path.insert(0, '..')\n",
28
+ "\n",
29
+ "# Load libraries \n",
30
+ "import pandas as pd\n",
31
+ "import json\n",
32
+ "import matplotlib.pyplot as plt\n",
33
+ "\n",
34
+ "# Load main demand forecasting class\n",
35
+ "from src.main import DemandForecasting\n",
36
+ "\n",
37
+ "df = DemandForecasting()"
38
+ ]
39
+ },
40
+ {
41
+ "cell_type": "code",
42
+ "execution_count": 2,
43
+ "metadata": {},
44
+ "outputs": [],
45
+ "source": [
46
+ "ts = pd.read_csv('../data/fuzzy.csv')"
47
+ ]
48
+ },
49
+ {
50
+ "cell_type": "code",
51
+ "execution_count": 3,
52
+ "metadata": {},
53
+ "outputs": [
54
+ {
55
+ "name": "stdout",
56
+ "output_type": "stream",
57
+ "text": [
58
+ "Start profiling, note, predictability been disabled\n",
59
+ "Change point detection\n",
60
+ "callindg model: prophet_plus\n",
61
+ "has_idsc_model\n"
62
+ ]
63
+ }
64
+ ],
65
+ "source": [
66
+ "# Step 1 - evaluate RMSE\n",
67
+ "# res = df.forecast(ts, 10, model='all', run_test=False, characteristic='fuzzy')\n",
68
+ "\n",
69
+ "# Step 2 - forecast\n",
70
+ "res = df.forecast(ts, 30, model='prophet_plus', characteristic='fuzzy')"
71
+ ]
72
+ },
73
+ {
74
+ "cell_type": "code",
75
+ "execution_count": 4,
76
+ "metadata": {},
77
+ "outputs": [
78
+ {
79
+ "data": {
80
+ "text/plain": [
81
+ "{'characteristic': 'fuzzy',\n",
82
+ " 'forecast': [{'model': 'prophet_plus',\n",
83
+ " 'forecast': {'datetime': DatetimeIndex(['2023-04-23', '2023-04-30', '2023-05-07', '2023-05-14',\n",
84
+ " '2023-05-21', '2023-05-28', '2023-06-04', '2023-06-11',\n",
85
+ " '2023-06-18', '2023-06-25', '2023-07-02', '2023-07-09',\n",
86
+ " '2023-07-16', '2023-07-23', '2023-07-30', '2023-08-06',\n",
87
+ " '2023-08-13', '2023-08-20', '2023-08-27', '2023-09-03',\n",
88
+ " '2023-09-10', '2023-09-17', '2023-09-24', '2023-10-01',\n",
89
+ " '2023-10-08', '2023-10-15', '2023-10-22', '2023-10-29',\n",
90
+ " '2023-11-05', '2023-11-12'],\n",
91
+ " dtype='datetime64[ns]', freq='W-SUN'),\n",
92
+ " 'y': dict_values([0, 18, 12, 10, 12, 0, 11, 0, 11, 13, 0, 18, 12, 10, 12, 0, 11, 0, 11, 13, 0, 18, 12, 10, 12, 0, 12, 0, 11, 13])},\n",
93
+ " 'raw': {'prediction_result': {'predicted_value': {'2023-04-24': 0,\n",
94
+ " '2023-04-25': 18,\n",
95
+ " '2023-04-26': 12,\n",
96
+ " '2023-04-27': 10,\n",
97
+ " '2023-04-28': 12,\n",
98
+ " '2023-04-29': 0,\n",
99
+ " '2023-04-30': 11,\n",
100
+ " '2023-05-01': 0,\n",
101
+ " '2023-05-02': 11,\n",
102
+ " '2023-05-03': 13,\n",
103
+ " '2023-05-04': 0,\n",
104
+ " '2023-05-05': 18,\n",
105
+ " '2023-05-06': 12,\n",
106
+ " '2023-05-07': 10,\n",
107
+ " '2023-05-08': 12,\n",
108
+ " '2023-05-09': 0,\n",
109
+ " '2023-05-10': 11,\n",
110
+ " '2023-05-11': 0,\n",
111
+ " '2023-05-12': 11,\n",
112
+ " '2023-05-13': 13,\n",
113
+ " '2023-05-14': 0,\n",
114
+ " '2023-05-15': 18,\n",
115
+ " '2023-05-16': 12,\n",
116
+ " '2023-05-17': 10,\n",
117
+ " '2023-05-18': 12,\n",
118
+ " '2023-05-19': 0,\n",
119
+ " '2023-05-20': 12,\n",
120
+ " '2023-05-21': 0,\n",
121
+ " '2023-05-22': 11,\n",
122
+ " '2023-05-23': 13},\n",
123
+ " 'interval_metrics': {'interval_rmse': 1.5164425186469757,\n",
124
+ " 'interval_mae': 0.9669727996291655,\n",
125
+ " 'interval_smape': 1.732136035567733},\n",
126
+ " 'quantity_metrics': {'quantity_rmse': 15.549524453161835,\n",
127
+ " 'quantity_mae': 9.35978138752326,\n",
128
+ " 'quantity_smape': 0.6888599339319311}},\n",
129
+ " 'request_timestamp': '2023-10-15 19:09:48',\n",
130
+ " 'engine_code': 'Foretell_Pred_Prophet_Intermittent'}}]}"
131
+ ]
132
+ },
133
+ "execution_count": 4,
134
+ "metadata": {},
135
+ "output_type": "execute_result"
136
+ }
137
+ ],
138
+ "source": [
139
+ "res"
140
+ ]
141
+ },
142
+ {
143
+ "cell_type": "code",
144
+ "execution_count": 5,
145
+ "metadata": {},
146
+ "outputs": [
147
+ {
148
+ "data": {
149
+ "text/html": [
150
+ "<div>\n",
151
+ "<style scoped>\n",
152
+ " .dataframe tbody tr th:only-of-type {\n",
153
+ " vertical-align: middle;\n",
154
+ " }\n",
155
+ "\n",
156
+ " .dataframe tbody tr th {\n",
157
+ " vertical-align: top;\n",
158
+ " }\n",
159
+ "\n",
160
+ " .dataframe thead th {\n",
161
+ " text-align: right;\n",
162
+ " }\n",
163
+ "</style>\n",
164
+ "<table border=\"1\" class=\"dataframe\">\n",
165
+ " <thead>\n",
166
+ " <tr style=\"text-align: right;\">\n",
167
+ " <th></th>\n",
168
+ " <th>datetime</th>\n",
169
+ " <th>y</th>\n",
170
+ " </tr>\n",
171
+ " </thead>\n",
172
+ " <tbody>\n",
173
+ " <tr>\n",
174
+ " <th>0</th>\n",
175
+ " <td>2023-04-23</td>\n",
176
+ " <td>0</td>\n",
177
+ " </tr>\n",
178
+ " <tr>\n",
179
+ " <th>1</th>\n",
180
+ " <td>2023-04-30</td>\n",
181
+ " <td>18</td>\n",
182
+ " </tr>\n",
183
+ " <tr>\n",
184
+ " <th>2</th>\n",
185
+ " <td>2023-05-07</td>\n",
186
+ " <td>12</td>\n",
187
+ " </tr>\n",
188
+ " <tr>\n",
189
+ " <th>3</th>\n",
190
+ " <td>2023-05-14</td>\n",
191
+ " <td>10</td>\n",
192
+ " </tr>\n",
193
+ " <tr>\n",
194
+ " <th>4</th>\n",
195
+ " <td>2023-05-21</td>\n",
196
+ " <td>12</td>\n",
197
+ " </tr>\n",
198
+ " <tr>\n",
199
+ " <th>5</th>\n",
200
+ " <td>2023-05-28</td>\n",
201
+ " <td>0</td>\n",
202
+ " </tr>\n",
203
+ " <tr>\n",
204
+ " <th>6</th>\n",
205
+ " <td>2023-06-04</td>\n",
206
+ " <td>11</td>\n",
207
+ " </tr>\n",
208
+ " <tr>\n",
209
+ " <th>7</th>\n",
210
+ " <td>2023-06-11</td>\n",
211
+ " <td>0</td>\n",
212
+ " </tr>\n",
213
+ " <tr>\n",
214
+ " <th>8</th>\n",
215
+ " <td>2023-06-18</td>\n",
216
+ " <td>11</td>\n",
217
+ " </tr>\n",
218
+ " <tr>\n",
219
+ " <th>9</th>\n",
220
+ " <td>2023-06-25</td>\n",
221
+ " <td>13</td>\n",
222
+ " </tr>\n",
223
+ " <tr>\n",
224
+ " <th>10</th>\n",
225
+ " <td>2023-07-02</td>\n",
226
+ " <td>0</td>\n",
227
+ " </tr>\n",
228
+ " <tr>\n",
229
+ " <th>11</th>\n",
230
+ " <td>2023-07-09</td>\n",
231
+ " <td>18</td>\n",
232
+ " </tr>\n",
233
+ " <tr>\n",
234
+ " <th>12</th>\n",
235
+ " <td>2023-07-16</td>\n",
236
+ " <td>12</td>\n",
237
+ " </tr>\n",
238
+ " <tr>\n",
239
+ " <th>13</th>\n",
240
+ " <td>2023-07-23</td>\n",
241
+ " <td>10</td>\n",
242
+ " </tr>\n",
243
+ " <tr>\n",
244
+ " <th>14</th>\n",
245
+ " <td>2023-07-30</td>\n",
246
+ " <td>12</td>\n",
247
+ " </tr>\n",
248
+ " <tr>\n",
249
+ " <th>15</th>\n",
250
+ " <td>2023-08-06</td>\n",
251
+ " <td>0</td>\n",
252
+ " </tr>\n",
253
+ " <tr>\n",
254
+ " <th>16</th>\n",
255
+ " <td>2023-08-13</td>\n",
256
+ " <td>11</td>\n",
257
+ " </tr>\n",
258
+ " <tr>\n",
259
+ " <th>17</th>\n",
260
+ " <td>2023-08-20</td>\n",
261
+ " <td>0</td>\n",
262
+ " </tr>\n",
263
+ " <tr>\n",
264
+ " <th>18</th>\n",
265
+ " <td>2023-08-27</td>\n",
266
+ " <td>11</td>\n",
267
+ " </tr>\n",
268
+ " <tr>\n",
269
+ " <th>19</th>\n",
270
+ " <td>2023-09-03</td>\n",
271
+ " <td>13</td>\n",
272
+ " </tr>\n",
273
+ " <tr>\n",
274
+ " <th>20</th>\n",
275
+ " <td>2023-09-10</td>\n",
276
+ " <td>0</td>\n",
277
+ " </tr>\n",
278
+ " <tr>\n",
279
+ " <th>21</th>\n",
280
+ " <td>2023-09-17</td>\n",
281
+ " <td>18</td>\n",
282
+ " </tr>\n",
283
+ " <tr>\n",
284
+ " <th>22</th>\n",
285
+ " <td>2023-09-24</td>\n",
286
+ " <td>12</td>\n",
287
+ " </tr>\n",
288
+ " <tr>\n",
289
+ " <th>23</th>\n",
290
+ " <td>2023-10-01</td>\n",
291
+ " <td>10</td>\n",
292
+ " </tr>\n",
293
+ " <tr>\n",
294
+ " <th>24</th>\n",
295
+ " <td>2023-10-08</td>\n",
296
+ " <td>12</td>\n",
297
+ " </tr>\n",
298
+ " <tr>\n",
299
+ " <th>25</th>\n",
300
+ " <td>2023-10-15</td>\n",
301
+ " <td>0</td>\n",
302
+ " </tr>\n",
303
+ " <tr>\n",
304
+ " <th>26</th>\n",
305
+ " <td>2023-10-22</td>\n",
306
+ " <td>12</td>\n",
307
+ " </tr>\n",
308
+ " <tr>\n",
309
+ " <th>27</th>\n",
310
+ " <td>2023-10-29</td>\n",
311
+ " <td>0</td>\n",
312
+ " </tr>\n",
313
+ " <tr>\n",
314
+ " <th>28</th>\n",
315
+ " <td>2023-11-05</td>\n",
316
+ " <td>11</td>\n",
317
+ " </tr>\n",
318
+ " <tr>\n",
319
+ " <th>29</th>\n",
320
+ " <td>2023-11-12</td>\n",
321
+ " <td>13</td>\n",
322
+ " </tr>\n",
323
+ " </tbody>\n",
324
+ "</table>\n",
325
+ "</div>"
326
+ ],
327
+ "text/plain": [
328
+ " datetime y\n",
329
+ "0 2023-04-23 0\n",
330
+ "1 2023-04-30 18\n",
331
+ "2 2023-05-07 12\n",
332
+ "3 2023-05-14 10\n",
333
+ "4 2023-05-21 12\n",
334
+ "5 2023-05-28 0\n",
335
+ "6 2023-06-04 11\n",
336
+ "7 2023-06-11 0\n",
337
+ "8 2023-06-18 11\n",
338
+ "9 2023-06-25 13\n",
339
+ "10 2023-07-02 0\n",
340
+ "11 2023-07-09 18\n",
341
+ "12 2023-07-16 12\n",
342
+ "13 2023-07-23 10\n",
343
+ "14 2023-07-30 12\n",
344
+ "15 2023-08-06 0\n",
345
+ "16 2023-08-13 11\n",
346
+ "17 2023-08-20 0\n",
347
+ "18 2023-08-27 11\n",
348
+ "19 2023-09-03 13\n",
349
+ "20 2023-09-10 0\n",
350
+ "21 2023-09-17 18\n",
351
+ "22 2023-09-24 12\n",
352
+ "23 2023-10-01 10\n",
353
+ "24 2023-10-08 12\n",
354
+ "25 2023-10-15 0\n",
355
+ "26 2023-10-22 12\n",
356
+ "27 2023-10-29 0\n",
357
+ "28 2023-11-05 11\n",
358
+ "29 2023-11-12 13"
359
+ ]
360
+ },
361
+ "execution_count": 5,
362
+ "metadata": {},
363
+ "output_type": "execute_result"
364
+ }
365
+ ],
366
+ "source": [
367
+ "pd.DataFrame(res['forecast'][0]['forecast'], columns=['datetime', 'y'])"
368
+ ]
369
+ },
370
+ {
371
+ "cell_type": "code",
372
+ "execution_count": 7,
373
+ "metadata": {},
374
+ "outputs": [
375
+ {
376
+ "name": "stdout",
377
+ "output_type": "stream",
378
+ "text": [
379
+ "prophet_plus\n"
380
+ ]
381
+ },
382
+ {
383
+ "ename": "KeyError",
384
+ "evalue": "'interm_scores'",
385
+ "output_type": "error",
386
+ "traceback": [
387
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
388
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
389
+ "Cell \u001b[0;32mIn[7], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[39mprint\u001b[39m(r[\u001b[39m'\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m 3\u001b[0m \u001b[39m# print(r['RMSE'])\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[39m# print(r['order_quantity_RMSE'])\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[39mprint\u001b[39m(r[\u001b[39m'\u001b[39;49m\u001b[39minterm_scores\u001b[39;49m\u001b[39m'\u001b[39;49m])\n\u001b[1;32m 6\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m________\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 7\u001b[0m r[\u001b[39m'\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mplot(title\u001b[39m=\u001b[39mr[\u001b[39m'\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m-test\u001b[39m\u001b[39m'\u001b[39m)\n",
390
+ "\u001b[0;31mKeyError\u001b[0m: 'interm_scores'"
391
+ ]
392
+ }
393
+ ],
394
+ "source": [
395
+ "for r in res['forecast']:\n",
396
+ " print(r['model'])\n",
397
+ " # print(r['RMSE'])\n",
398
+ " # print(r['order_quantity_RMSE'])\n",
399
+ " print(r['interm_scores'])\n",
400
+ " print('________')\n",
401
+ " r['test'].plot(title=r['model'] + '-test')"
402
+ ]
403
+ },
404
+ {
405
+ "cell_type": "code",
406
+ "execution_count": 10,
407
+ "metadata": {},
408
+ "outputs": [
409
+ {
410
+ "ename": "IndexError",
411
+ "evalue": "list index out of range",
412
+ "output_type": "error",
413
+ "traceback": [
414
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
415
+ "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
416
+ "\u001b[1;32m/Users/qiaozhang/Desktop/sentient-dev/snr_demand-forecasting/notebooks/test.ipynb Cell 10\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/qiaozhang/Desktop/sentient-dev/snr_demand-forecasting/notebooks/test.ipynb#X12sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m res[\u001b[39m4\u001b[39;49m][\u001b[39m'\u001b[39m\u001b[39mtest_raw\u001b[39m\u001b[39m'\u001b[39m]\n",
417
+ "\u001b[0;31mIndexError\u001b[0m: list index out of range"
418
+ ]
419
+ }
420
+ ],
421
+ "source": [
422
+ "res[4]['test_raw']"
423
+ ]
424
+ },
425
+ {
426
+ "cell_type": "code",
427
+ "execution_count": null,
428
+ "metadata": {},
429
+ "outputs": [
430
+ {
431
+ "name": "stderr",
432
+ "output_type": "stream",
433
+ "text": [
434
+ "11:17:02 - cmdstanpy - INFO - Chain [1] start processing\n",
435
+ "11:17:02 - cmdstanpy - INFO - Chain [1] done processing\n"
436
+ ]
437
+ },
438
+ {
439
+ "name": "stdout",
440
+ "output_type": "stream",
441
+ "text": [
442
+ "callindg model: prophet\n",
443
+ " ds trend yhat_lower yhat_upper trend_lower trend_upper \\\n",
444
+ "260 2023-04-30 8.921487 -7.908574 21.012119 8.921487 8.921487 \n",
445
+ "261 2023-05-07 8.931226 -6.567967 22.604438 8.931226 8.931226 \n",
446
+ "262 2023-05-14 8.940965 -8.081720 21.526347 8.940905 8.941007 \n",
447
+ "263 2023-05-21 8.950703 -9.985601 19.493650 8.950543 8.950831 \n",
448
+ "264 2023-05-28 8.960442 -8.813073 20.526393 8.960129 8.960702 \n",
449
+ "265 2023-06-04 8.970180 -6.966844 23.563550 8.969667 8.970604 \n",
450
+ "266 2023-06-11 8.979919 -4.608789 25.118377 8.979231 8.980524 \n",
451
+ "267 2023-06-18 8.989657 -4.180691 25.485273 8.988761 8.990488 \n",
452
+ "268 2023-06-25 8.999396 -4.686704 24.961761 8.998250 9.000445 \n",
453
+ "269 2023-07-02 9.009134 -5.880469 23.125865 9.007781 9.010505 \n",
454
+ "270 2023-07-09 9.018873 -5.090580 23.769587 9.017264 9.020526 \n",
455
+ "271 2023-07-16 9.028611 -4.761036 25.748781 9.026645 9.030525 \n",
456
+ "272 2023-07-23 9.038350 -4.907963 24.736967 9.036085 9.040552 \n",
457
+ "273 2023-07-30 9.048088 -4.570258 23.745148 9.045508 9.050722 \n",
458
+ "274 2023-08-06 9.057827 -5.827376 22.932313 9.054935 9.060731 \n",
459
+ "275 2023-08-13 9.067565 -4.220147 24.956558 9.064331 9.070780 \n",
460
+ "276 2023-08-20 9.077304 -0.864349 29.034652 9.073628 9.081006 \n",
461
+ "277 2023-08-27 9.087042 3.470909 32.343086 9.082870 9.091054 \n",
462
+ "278 2023-09-03 9.096781 1.198728 30.637736 9.092259 9.101139 \n",
463
+ "279 2023-09-10 9.106520 -1.561115 27.531066 9.101419 9.111209 \n",
464
+ "280 2023-09-17 9.116258 -6.184726 24.202682 9.110719 9.121436 \n",
465
+ "281 2023-09-24 9.125997 -4.579644 23.752222 9.120080 9.131616 \n",
466
+ "282 2023-10-01 9.135735 -5.178492 25.012735 9.129285 9.141805 \n",
467
+ "283 2023-10-08 9.145474 -5.666373 22.611290 9.138581 9.152031 \n",
468
+ "284 2023-10-15 9.155212 -6.963614 21.842661 9.147855 9.162345 \n",
469
+ "285 2023-10-22 9.164951 -6.763386 23.055701 9.157131 9.172489 \n",
470
+ "286 2023-10-29 9.174689 -5.170320 24.787756 9.166426 9.182695 \n",
471
+ "287 2023-11-05 9.184428 -2.337336 27.885403 9.175632 9.192803 \n",
472
+ "288 2023-11-12 9.194166 -1.331529 29.762405 9.184876 9.203052 \n",
473
+ "289 2023-11-19 9.203905 -2.375162 27.729052 9.194147 9.213316 \n",
474
+ "\n",
475
+ " additive_terms additive_terms_lower additive_terms_upper yearly \\\n",
476
+ "260 -2.521643 -2.521643 -2.521643 -2.521643 \n",
477
+ "261 -1.628433 -1.628433 -1.628433 -1.628433 \n",
478
+ "262 -2.544671 -2.544671 -2.544671 -2.544671 \n",
479
+ "263 -3.980016 -3.980016 -3.980016 -3.980016 \n",
480
+ "264 -3.655285 -3.655285 -3.655285 -3.655285 \n",
481
+ "265 -1.186016 -1.186016 -1.186016 -1.186016 \n",
482
+ "266 1.337140 1.337140 1.337140 1.337140 \n",
483
+ "267 1.789455 1.789455 1.789455 1.789455 \n",
484
+ "268 0.451573 0.451573 0.451573 0.451573 \n",
485
+ "269 -0.531397 -0.531397 -0.531397 -0.531397 \n",
486
+ "270 0.094160 0.094160 0.094160 0.094160 \n",
487
+ "271 1.226812 1.226812 1.226812 1.226812 \n",
488
+ "272 1.064831 1.064831 1.064831 1.064831 \n",
489
+ "273 -0.269867 -0.269867 -0.269867 -0.269867 \n",
490
+ "274 -0.559179 -0.559179 -0.559179 -0.559179 \n",
491
+ "275 1.853473 1.853473 1.853473 1.853473 \n",
492
+ "276 5.774049 5.774049 5.774049 5.774049 \n",
493
+ "277 8.102311 8.102311 8.102311 8.102311 \n",
494
+ "278 7.022347 7.022347 7.022347 7.022347 \n",
495
+ "279 3.785691 3.785691 3.785691 3.785691 \n",
496
+ "280 1.145548 1.145548 1.145548 1.145548 \n",
497
+ "281 0.423666 0.423666 0.423666 0.423666 \n",
498
+ "282 0.590499 0.590499 0.590499 0.590499 \n",
499
+ "283 0.116618 0.116618 0.116618 0.116618 \n",
500
+ "284 -0.921952 -0.921952 -0.921952 -0.921952 \n",
501
+ "285 -1.023065 -1.023065 -1.023065 -1.023065 \n",
502
+ "286 0.705503 0.705503 0.705503 0.705503 \n",
503
+ "287 3.244306 3.244306 3.244306 3.244306 \n",
504
+ "288 4.575861 4.575861 4.575861 4.575861 \n",
505
+ "289 3.595682 3.595682 3.595682 3.595682 \n",
506
+ "\n",
507
+ " yearly_lower yearly_upper multiplicative_terms \\\n",
508
+ "260 -2.521643 -2.521643 0.0 \n",
509
+ "261 -1.628433 -1.628433 0.0 \n",
510
+ "262 -2.544671 -2.544671 0.0 \n",
511
+ "263 -3.980016 -3.980016 0.0 \n",
512
+ "264 -3.655285 -3.655285 0.0 \n",
513
+ "265 -1.186016 -1.186016 0.0 \n",
514
+ "266 1.337140 1.337140 0.0 \n",
515
+ "267 1.789455 1.789455 0.0 \n",
516
+ "268 0.451573 0.451573 0.0 \n",
517
+ "269 -0.531397 -0.531397 0.0 \n",
518
+ "270 0.094160 0.094160 0.0 \n",
519
+ "271 1.226812 1.226812 0.0 \n",
520
+ "272 1.064831 1.064831 0.0 \n",
521
+ "273 -0.269867 -0.269867 0.0 \n",
522
+ "274 -0.559179 -0.559179 0.0 \n",
523
+ "275 1.853473 1.853473 0.0 \n",
524
+ "276 5.774049 5.774049 0.0 \n",
525
+ "277 8.102311 8.102311 0.0 \n",
526
+ "278 7.022347 7.022347 0.0 \n",
527
+ "279 3.785691 3.785691 0.0 \n",
528
+ "280 1.145548 1.145548 0.0 \n",
529
+ "281 0.423666 0.423666 0.0 \n",
530
+ "282 0.590499 0.590499 0.0 \n",
531
+ "283 0.116618 0.116618 0.0 \n",
532
+ "284 -0.921952 -0.921952 0.0 \n",
533
+ "285 -1.023065 -1.023065 0.0 \n",
534
+ "286 0.705503 0.705503 0.0 \n",
535
+ "287 3.244306 3.244306 0.0 \n",
536
+ "288 4.575861 4.575861 0.0 \n",
537
+ "289 3.595682 3.595682 0.0 \n",
538
+ "\n",
539
+ " multiplicative_terms_lower multiplicative_terms_upper yhat \n",
540
+ "260 0.0 0.0 6.399845 \n",
541
+ "261 0.0 0.0 7.302793 \n",
542
+ "262 0.0 0.0 6.396294 \n",
543
+ "263 0.0 0.0 4.970687 \n",
544
+ "264 0.0 0.0 5.305157 \n",
545
+ "265 0.0 0.0 7.784164 \n",
546
+ "266 0.0 0.0 10.317059 \n",
547
+ "267 0.0 0.0 10.779112 \n",
548
+ "268 0.0 0.0 9.450969 \n",
549
+ "269 0.0 0.0 8.477737 \n",
550
+ "270 0.0 0.0 9.113033 \n",
551
+ "271 0.0 0.0 10.255423 \n",
552
+ "272 0.0 0.0 10.103181 \n",
553
+ "273 0.0 0.0 8.778221 \n",
554
+ "274 0.0 0.0 8.498648 \n",
555
+ "275 0.0 0.0 10.921038 \n",
556
+ "276 0.0 0.0 14.851353 \n",
557
+ "277 0.0 0.0 17.189353 \n",
558
+ "278 0.0 0.0 16.119128 \n",
559
+ "279 0.0 0.0 12.892211 \n",
560
+ "280 0.0 0.0 10.261806 \n",
561
+ "281 0.0 0.0 9.549662 \n",
562
+ "282 0.0 0.0 9.726234 \n",
563
+ "283 0.0 0.0 9.262091 \n",
564
+ "284 0.0 0.0 8.233260 \n",
565
+ "285 0.0 0.0 8.141886 \n",
566
+ "286 0.0 0.0 9.880192 \n",
567
+ "287 0.0 0.0 12.428733 \n",
568
+ "288 0.0 0.0 13.770027 \n",
569
+ "289 0.0 0.0 12.799587 \n"
570
+ ]
571
+ }
572
+ ],
573
+ "source": [
574
+ "# Step 2 - forecast\n",
575
+ "res = df.forecast(ts, 30, model='prophet')"
576
+ ]
577
+ },
578
+ {
579
+ "cell_type": "code",
580
+ "execution_count": null,
581
+ "metadata": {},
582
+ "outputs": [
583
+ {
584
+ "data": {
585
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
603
+ " <th></th>\n",
604
+ " <th>y</th>\n",
605
+ " </tr>\n",
606
+ " </thead>\n",
607
+ " <tbody>\n",
608
+ " <tr>\n",
609
+ " <th>2023-04-23</th>\n",
610
+ " <td>6.399845</td>\n",
611
+ " </tr>\n",
612
+ " <tr>\n",
613
+ " <th>2023-04-30</th>\n",
614
+ " <td>7.302793</td>\n",
615
+ " </tr>\n",
616
+ " <tr>\n",
617
+ " <th>2023-05-07</th>\n",
618
+ " <td>6.396294</td>\n",
619
+ " </tr>\n",
620
+ " <tr>\n",
621
+ " <th>2023-05-14</th>\n",
622
+ " <td>4.970687</td>\n",
623
+ " </tr>\n",
624
+ " <tr>\n",
625
+ " <th>2023-05-21</th>\n",
626
+ " <td>5.305157</td>\n",
627
+ " </tr>\n",
628
+ " <tr>\n",
629
+ " <th>2023-05-28</th>\n",
630
+ " <td>7.784164</td>\n",
631
+ " </tr>\n",
632
+ " <tr>\n",
633
+ " <th>2023-06-04</th>\n",
634
+ " <td>10.317059</td>\n",
635
+ " </tr>\n",
636
+ " <tr>\n",
637
+ " <th>2023-06-11</th>\n",
638
+ " <td>10.779112</td>\n",
639
+ " </tr>\n",
640
+ " <tr>\n",
641
+ " <th>2023-06-18</th>\n",
642
+ " <td>9.450969</td>\n",
643
+ " </tr>\n",
644
+ " <tr>\n",
645
+ " <th>2023-06-25</th>\n",
646
+ " <td>8.477737</td>\n",
647
+ " </tr>\n",
648
+ " <tr>\n",
649
+ " <th>2023-07-02</th>\n",
650
+ " <td>9.113033</td>\n",
651
+ " </tr>\n",
652
+ " <tr>\n",
653
+ " <th>2023-07-09</th>\n",
654
+ " <td>10.255423</td>\n",
655
+ " </tr>\n",
656
+ " <tr>\n",
657
+ " <th>2023-07-16</th>\n",
658
+ " <td>10.103181</td>\n",
659
+ " </tr>\n",
660
+ " <tr>\n",
661
+ " <th>2023-07-23</th>\n",
662
+ " <td>8.778221</td>\n",
663
+ " </tr>\n",
664
+ " <tr>\n",
665
+ " <th>2023-07-30</th>\n",
666
+ " <td>8.498648</td>\n",
667
+ " </tr>\n",
668
+ " <tr>\n",
669
+ " <th>2023-08-06</th>\n",
670
+ " <td>10.921038</td>\n",
671
+ " </tr>\n",
672
+ " <tr>\n",
673
+ " <th>2023-08-13</th>\n",
674
+ " <td>14.851353</td>\n",
675
+ " </tr>\n",
676
+ " <tr>\n",
677
+ " <th>2023-08-20</th>\n",
678
+ " <td>17.189353</td>\n",
679
+ " </tr>\n",
680
+ " <tr>\n",
681
+ " <th>2023-08-27</th>\n",
682
+ " <td>16.119128</td>\n",
683
+ " </tr>\n",
684
+ " <tr>\n",
685
+ " <th>2023-09-03</th>\n",
686
+ " <td>12.892211</td>\n",
687
+ " </tr>\n",
688
+ " <tr>\n",
689
+ " <th>2023-09-10</th>\n",
690
+ " <td>10.261806</td>\n",
691
+ " </tr>\n",
692
+ " <tr>\n",
693
+ " <th>2023-09-17</th>\n",
694
+ " <td>9.549662</td>\n",
695
+ " </tr>\n",
696
+ " <tr>\n",
697
+ " <th>2023-09-24</th>\n",
698
+ " <td>9.726234</td>\n",
699
+ " </tr>\n",
700
+ " <tr>\n",
701
+ " <th>2023-10-01</th>\n",
702
+ " <td>9.262091</td>\n",
703
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704
+ " <tr>\n",
705
+ " <th>2023-10-08</th>\n",
706
+ " <td>8.233260</td>\n",
707
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708
+ " <tr>\n",
709
+ " <th>2023-10-15</th>\n",
710
+ " <td>8.141886</td>\n",
711
+ " </tr>\n",
712
+ " <tr>\n",
713
+ " <th>2023-10-22</th>\n",
714
+ " <td>9.880192</td>\n",
715
+ " </tr>\n",
716
+ " <tr>\n",
717
+ " <th>2023-10-29</th>\n",
718
+ " <td>12.428733</td>\n",
719
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720
+ " <tr>\n",
721
+ " <th>2023-11-05</th>\n",
722
+ " <td>13.770027</td>\n",
723
+ " </tr>\n",
724
+ " <tr>\n",
725
+ " <th>2023-11-12</th>\n",
726
+ " <td>12.799587</td>\n",
727
+ " </tr>\n",
728
+ " </tbody>\n",
729
+ "</table>\n",
730
+ "</div>"
731
+ ],
732
+ "text/plain": [
733
+ " y\n",
734
+ "2023-04-23 6.399845\n",
735
+ "2023-04-30 7.302793\n",
736
+ "2023-05-07 6.396294\n",
737
+ "2023-05-14 4.970687\n",
738
+ "2023-05-21 5.305157\n",
739
+ "2023-05-28 7.784164\n",
740
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741
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742
+ "2023-06-18 9.450969\n",
743
+ "2023-06-25 8.477737\n",
744
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745
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746
+ "2023-07-16 10.103181\n",
747
+ "2023-07-23 8.778221\n",
748
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749
+ "2023-08-06 10.921038\n",
750
+ "2023-08-13 14.851353\n",
751
+ "2023-08-20 17.189353\n",
752
+ "2023-08-27 16.119128\n",
753
+ "2023-09-03 12.892211\n",
754
+ "2023-09-10 10.261806\n",
755
+ "2023-09-17 9.549662\n",
756
+ "2023-09-24 9.726234\n",
757
+ "2023-10-01 9.262091\n",
758
+ "2023-10-08 8.233260\n",
759
+ "2023-10-15 8.141886\n",
760
+ "2023-10-22 9.880192\n",
761
+ "2023-10-29 12.428733\n",
762
+ "2023-11-05 13.770027\n",
763
+ "2023-11-12 12.799587"
764
+ ]
765
+ },
766
+ "execution_count": 17,
767
+ "metadata": {},
768
+ "output_type": "execute_result"
769
+ }
770
+ ],
771
+ "source": [
772
+ "res[0]['forecast']"
773
+ ]
774
+ },
775
+ {
776
+ "cell_type": "code",
777
+ "execution_count": null,
778
+ "metadata": {},
779
+ "outputs": [
780
+ {
781
+ "data": {
782
+ "text/plain": [
783
+ "<Axes: title={'center': 'forecasted'}>"
784
+ ]
785
+ },
786
+ "execution_count": 18,
787
+ "metadata": {},
788
+ "output_type": "execute_result"
789
+ },
790
+ {
791
+ "data": {
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+ "image/png": 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",
793
+ "text/plain": [
794
+ "<Figure size 640x480 with 1 Axes>"
795
+ ]
796
+ },
797
+ "metadata": {},
798
+ "output_type": "display_data"
799
+ }
800
+ ],
801
+ "source": [
802
+ "res[0]['forecast'].plot(title='forecasted')"
803
+ ]
804
+ }
805
+ ],
806
+ "metadata": {
807
+ "kernelspec": {
808
+ "display_name": "demand-forecasting",
809
+ "language": "python",
810
+ "name": "python3"
811
+ },
812
+ "language_info": {
813
+ "codemirror_mode": {
814
+ "name": "ipython",
815
+ "version": 3
816
+ },
817
+ "file_extension": ".py",
818
+ "mimetype": "text/x-python",
819
+ "name": "python",
820
+ "nbconvert_exporter": "python",
821
+ "pygments_lexer": "ipython3",
822
+ "version": "3.10.12"
823
+ },
824
+ "orig_nbformat": 4
825
+ },
826
+ "nbformat": 4,
827
+ "nbformat_minor": 2
828
+ }
src/__init__.py ADDED
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src/avtive_models.py ADDED
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1
+ '''
2
+ 'ceif_plus' : 2023 Sep. currently the model is under review, so not recommended to use this mode - by idsc
3
+ '''
4
+
5
+ active_models = {
6
+ 'intermittent':
7
+ [
8
+ 'prophet_plus',
9
+ 'ceif_plus'
10
+ ],
11
+ 'continuous':
12
+ [
13
+ 'fft_plus',
14
+ 'holt_winters_plus',
15
+ 'auto_arima_plus',
16
+ 'prophet',
17
+ 'prophet_plus',
18
+ ]
19
+ }
src/forecast/Prophet.py ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pandas as pd
2
+ from prophet import Prophet
3
+ import numpy as np
4
+
5
+
6
+ class ProphetWrapper():
7
+ def __init__(self):
8
+ pass
9
+
10
+ def forecast(self, ts, n_predict, freq=None):
11
+ model = Prophet()
12
+ train = ts.rename(columns={'datetime': 'ds'})
13
+
14
+ model.fit(train)
15
+
16
+ future = model.make_future_dataframe(periods=n_predict, freq=freq)
17
+
18
+ forecasted = model.predict(future)
19
+
20
+ print(forecasted[-n_predict:])
21
+
22
+ return forecasted[-n_predict:]
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src/functions/__init__.py ADDED
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src/functions/check_input.py ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ import pandas as pd
2
+
3
+ def check_input(df):
4
+ pd.infer_freq(df)
5
+ return
src/functions/itmtt_scores.py ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ def interm_scores(grdt_sr:list, pred_sr:list):
2
+ ## this function calculates
3
+ ## • Quantity score
4
+ ## • Quantity rate score
5
+ ## • Timing score
6
+ #print(grdt_sr, pred_sr)
7
+ lgrdt = len(grdt_sr)
8
+ assert lgrdt == len(pred_sr)
9
+ cnt_01match = 0
10
+ grdt_value = 0
11
+ pred_value = 0
12
+ cnt_grdt1 = 0
13
+ cnt_pred1 = 0
14
+ for i in range(lgrdt):
15
+
16
+ if (grdt_sr[i]==0 and pred_sr[i]==0) or (grdt_sr[i]> 0 and pred_sr[i]>0):
17
+ cnt_01match += 1
18
+
19
+ cnt_grdt1 += 1 if grdt_sr[i]>0 else 0
20
+ cnt_pred1 += 1 if pred_sr[i]>0 else 0
21
+ grdt_value += grdt_sr[i]
22
+ pred_value += pred_sr[i]
23
+ #print("Calculating:\nQuantity score, Quantity rate score, Timing score")
24
+ if cnt_grdt1 == 0 and cnt_pred1 == 0: # this indicate grdt_value=pred_value=0
25
+ return 1.0, 1.0, 1.0*cnt_01match / lgrdt
26
+ else:
27
+ return 1.0*min(cnt_grdt1, cnt_pred1)/max(cnt_grdt1, cnt_pred1),\
28
+ 1.0*min(grdt_value, pred_value)/max(grdt_value, pred_value),\
29
+ 1.0*cnt_01match / lgrdt
src/functions/mase.py ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy
2
+
3
+
4
+ def MASE(Actual, Predicted):
5
+ '''
6
+ Mean Absolute Scaled Error (MASE)
7
+ '''
8
+ values = []
9
+ for i in range(1, len(Actual)):
10
+ values.append(abs(Actual[i] - Predicted[i]) /
11
+ (abs(Actual[i] - Actual[i - 1]) / (len(Actual) - 1)))
12
+
13
+ return numpy.mean(values)
src/functions/order_qty_rmse.py ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from sklearn.metrics import mean_squared_error
3
+ import numpy as np
4
+
5
+
6
+ def order_qty_rmse(actual, predicted):
7
+ actu = []
8
+ pred = []
9
+ for i, a in enumerate(actual):
10
+ if not a == 0:
11
+ actu.append(actual[i])
12
+ pred.append(predicted[i])
13
+ return np.sqrt(mean_squared_error(actu, pred))
src/functions/sort_res.py ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ def sort_res_by_rmse(res):
2
+ pass
3
+
4
+
5
+ def sort_res_by_(res):
6
+ pass