conda env

Dockerfile

@@ -8,5 +8,6 @@ RUN pip3 install pandas
 RUN pip3 install numpy
 RUN pip3 install sklearn
 RUN pip3 install tensorflow
+RUN pip3 install matplotlib
 COPY ./steam-200k.csv ./
 COPY ./biblioteki_dl.py ./

Jenkinsfile

@@ -37,5 +37,10 @@ pipeline {
 				archiveArtifacts 'data.csv'
 			}
 		}
+		stage('Stage 2') {
+			steps {
+				build job: 's444386-training/training-evaluation/'
+			}
+		}
 	}    
 }

MLproject

@@ -0,0 +1,11 @@
+name: MLflow
+
+
+docker_env:
+  image: szymonjadczak/mlflow:latest
+
+entry_points:
+  main:
+    parameters:
+      epochs: {type: float, default: 3}
+    command: "python ./biblioteki_dl.py {epochs}"

environment.yml

@@ -0,0 +1,415 @@
+name: null
+channels:
+  - defaults
+dependencies:
+  - _anaconda_depends=2021.11=py39_0
+  - alabaster=0.7.12=pyhd3eb1b0_0
+  - anaconda=custom=py39_1
+  - anaconda-client=1.9.0=py39haa95532_0
+  - anaconda-project=0.10.2=pyhd3eb1b0_0
+  - anyio=3.5.0=py39haa95532_0
+  - appdirs=1.4.4=pyhd3eb1b0_0
+  - argh=0.26.2=py39haa95532_0
+  - argon2-cffi=21.3.0=pyhd3eb1b0_0
+  - argon2-cffi-bindings=21.2.0=py39h2bbff1b_0
+  - arrow=1.2.2=pyhd3eb1b0_0
+  - asn1crypto=1.5.1=py39haa95532_0
+  - astroid=2.6.6=py39haa95532_0
+  - astropy=5.0.4=py39h080aedc_0
+  - asttokens=2.0.5=pyhd3eb1b0_0
+  - async_generator=1.10=pyhd3eb1b0_0
+  - atomicwrites=1.4.0=py_0
+  - attrs=21.4.0=pyhd3eb1b0_0
+  - autopep8=1.6.0=pyhd3eb1b0_0
+  - babel=2.9.1=pyhd3eb1b0_0
+  - backcall=0.2.0=pyhd3eb1b0_0
+  - backports=1.1=pyhd3eb1b0_0
+  - backports.shutil_get_terminal_size=1.0.0=pyhd3eb1b0_3
+  - bcrypt=3.2.0=py39h196d8e1_0
+  - beautifulsoup4=4.11.1=py39haa95532_0
+  - binaryornot=0.4.4=pyhd3eb1b0_1
+  - bitarray=2.5.0=py39h2bbff1b_0
+  - bkcharts=0.2=py39haa95532_0
+  - black=19.10b0=py_0
+  - blas=1.0=mkl
+  - bleach=4.1.0=pyhd3eb1b0_0
+  - blosc=1.21.0=h19a0ad4_0
+  - bokeh=2.4.2=py39haa95532_1
+  - boto=2.49.0=py39haa95532_0
+  - bottleneck=1.3.4=py39h080aedc_0
+  - brotli=1.0.9=ha925a31_2
+  - brotlipy=0.7.0=py39h2bbff1b_1003
+  - bzip2=1.0.8=he774522_0
+  - ca-certificates=2022.4.26=haa95532_0
+  - cached-property=1.5.2=py_0
+  - certifi=2022.5.18.1=py39haa95532_0
+  - cffi=1.15.0=py39h2bbff1b_1
+  - cfitsio=3.470=he774522_6
+  - chardet=4.0.0=py39haa95532_1003
+  - charls=2.2.0=h6c2663c_0
+  - charset-normalizer=2.0.4=pyhd3eb1b0_0
+  - click=8.0.4=py39haa95532_0
+  - cloudpickle=2.0.0=pyhd3eb1b0_0
+  - clyent=1.2.2=py39haa95532_1
+  - colorama=0.4.4=pyhd3eb1b0_0
+  - comtypes=1.1.10=py39haa95532_1002
+  - conda=4.12.0=py39haa95532_0
+  - conda-content-trust=0.1.1=pyhd3eb1b0_0
+  - conda-pack=0.6.0=pyhd3eb1b0_0
+  - conda-package-handling=1.8.1=py39h8cc25b3_0
+  - conda-token=0.3.0=pyhd3eb1b0_0
+  - console_shortcut=0.1.1=4
+  - contextlib2=0.6.0.post1=pyhd3eb1b0_0
+  - cookiecutter=1.7.3=pyhd3eb1b0_0
+  - cryptography=37.0.1=py39h21b164f_0
+  - curl=7.82.0=h2bbff1b_0
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - cython=0.29.28=py39hd77b12b_0
+  - cytoolz=0.11.0=py39h2bbff1b_0
+  - daal4py=2021.5.0=py39h8cb3d55_0
+  - dal=2021.5.0=haa95532_796
+  - dask=2022.2.1=pyhd3eb1b0_0
+  - dask-core=2022.2.1=pyhd3eb1b0_0
+  - dataclasses=0.8=pyh6d0b6a4_7
+  - debugpy=1.5.1=py39hd77b12b_0
+  - decorator=5.1.1=pyhd3eb1b0_0
+  - defusedxml=0.7.1=pyhd3eb1b0_0
+  - diff-match-patch=20200713=pyhd3eb1b0_0
+  - distributed=2022.2.1=pyhd3eb1b0_0
+  - docutils=0.17.1=py39haa95532_1
+  - entrypoints=0.4=py39haa95532_0
+  - et_xmlfile=1.1.0=py39haa95532_0
+  - executing=0.8.3=pyhd3eb1b0_0
+  - fastcache=1.1.0=py39h196d8e1_0
+  - filelock=3.6.0=pyhd3eb1b0_0
+  - flake8=3.9.2=pyhd3eb1b0_0
+  - flask=2.0.3=pyhd3eb1b0_0
+  - fonttools=4.25.0=pyhd3eb1b0_0
+  - freetype=2.10.4=hd328e21_0
+  - fsspec=2022.3.0=py39haa95532_0
+  - get_terminal_size=1.0.0=h38e98db_0
+  - gevent=21.8.0=py39h2bbff1b_1
+  - giflib=5.2.1=h62dcd97_0
+  - glob2=0.7=pyhd3eb1b0_0
+  - greenlet=1.1.1=py39hd77b12b_0
+  - h5py=3.6.0=py39h3de5c98_0
+  - hdf5=1.10.6=h7ebc959_0
+  - heapdict=1.0.1=pyhd3eb1b0_0
+  - html5lib=1.1=pyhd3eb1b0_0
+  - icc_rt=2019.0.0=h0cc432a_1
+  - icu=58.2=ha925a31_3
+  - idna=3.3=pyhd3eb1b0_0
+  - imagecodecs=2021.8.26=py39ha1f97ea_0
+  - imageio=2.9.0=pyhd3eb1b0_0
+  - imagesize=1.3.0=pyhd3eb1b0_0
+  - importlib-metadata=4.11.3=py39haa95532_0
+  - importlib_metadata=4.11.3=hd3eb1b0_0
+  - inflection=0.5.1=py39haa95532_0
+  - iniconfig=1.1.1=pyhd3eb1b0_0
+  - intel-openmp=2021.4.0=haa95532_3556
+  - intervaltree=3.1.0=pyhd3eb1b0_0
+  - ipykernel=6.9.1=py39haa95532_0
+  - ipython=8.3.0=py39haa95532_0
+  - ipython_genutils=0.2.0=pyhd3eb1b0_1
+  - ipywidgets=7.6.5=pyhd3eb1b0_1
+  - isort=5.9.3=pyhd3eb1b0_0
+  - itsdangerous=2.0.1=pyhd3eb1b0_0
+  - jdcal=1.4.1=pyhd3eb1b0_0
+  - jedi=0.18.1=py39haa95532_1
+  - jinja2=3.0.3=pyhd3eb1b0_0
+  - jinja2-time=0.2.0=pyhd3eb1b0_3
+  - joblib=1.1.0=pyhd3eb1b0_0
+  - jpeg=9e=h2bbff1b_0
+  - json5=0.9.6=pyhd3eb1b0_0
+  - jsonschema=4.4.0=py39haa95532_0
+  - jupyter=1.0.0=py39haa95532_7
+  - jupyter_client=6.1.12=pyhd3eb1b0_0
+  - jupyter_console=6.4.0=pyhd3eb1b0_0
+  - jupyter_core=4.10.0=py39haa95532_0
+  - jupyter_server=1.13.5=pyhd3eb1b0_0
+  - jupyterlab=3.3.2=pyhd3eb1b0_0
+  - jupyterlab_pygments=0.1.2=py_0
+  - jupyterlab_server=2.12.0=py39haa95532_0
+  - jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
+  - keyring=23.4.0=py39haa95532_0
+  - kiwisolver=1.3.2=py39hd77b12b_0
+  - krb5=1.19.2=h5b6d351_0
+  - lazy-object-proxy=1.6.0=py39h2bbff1b_0
+  - lcms2=2.12=h83e58a3_0
+  - lerc=3.0=hd77b12b_0
+  - libaec=1.0.4=h33f27b4_1
+  - libarchive=3.4.2=h5e25573_0
+  - libcurl=7.82.0=h86230a5_0
+  - libdeflate=1.8=h2bbff1b_5
+  - libiconv=1.16=h2bbff1b_2
+  - liblief=0.11.5=hd77b12b_1
+  - libpng=1.6.37=h2a8f88b_0
+  - libspatialindex=1.9.3=h6c2663c_0
+  - libssh2=1.10.0=hcd4344a_0
+  - libtiff=4.2.0=hd0e1b90_0
+  - libwebp=1.2.2=h2bbff1b_0
+  - libxml2=2.9.12=h0ad7f3c_2
+  - libxslt=1.1.34=he774522_0
+  - libzopfli=1.0.3=ha925a31_0
+  - llvmlite=0.38.0=py39h23ce68f_0
+  - locket=1.0.0=py39haa95532_0
+  - lxml=4.8.0=py39h1985fb9_0
+  - lz4-c=1.9.3=h2bbff1b_1
+  - lzo=2.10=he774522_2
+  - m2w64-gcc-libgfortran=5.3.0=6
+  - m2w64-gcc-libs=5.3.0=7
+  - m2w64-gcc-libs-core=5.3.0=7
+  - m2w64-gmp=6.1.0=2
+  - m2w64-libwinpthread-git=5.0.0.4634.697f757=2
+  - markupsafe=2.0.1=py39h2bbff1b_0
+  - matplotlib=3.5.1=py39haa95532_1
+  - matplotlib-base=3.5.1=py39hd77b12b_1
+  - matplotlib-inline=0.1.2=pyhd3eb1b0_2
+  - mccabe=0.6.1=py39haa95532_1
+  - menuinst=1.4.18=py39h59b6b97_0
+  - mistune=0.8.4=py39h2bbff1b_1000
+  - mkl=2021.4.0=haa95532_640
+  - mkl-service=2.4.0=py39h2bbff1b_0
+  - mkl_fft=1.3.1=py39h277e83a_0
+  - mkl_random=1.2.2=py39hf11a4ad_0
+  - mock=4.0.3=pyhd3eb1b0_0
+  - more-itertools=8.12.0=pyhd3eb1b0_0
+  - mpmath=1.2.1=py39haa95532_0
+  - msgpack-python=1.0.3=py39h59b6b97_0
+  - msys2-conda-epoch=20160418=1
+  - multipledispatch=0.6.0=py39haa95532_0
+  - munkres=1.1.4=py_0
+  - mypy_extensions=0.4.3=py39haa95532_1
+  - nbclassic=0.3.5=pyhd3eb1b0_0
+  - nbclient=0.5.13=py39haa95532_0
+  - nbconvert=6.4.4=py39haa95532_0
+  - nbformat=5.3.0=py39haa95532_0
+  - nest-asyncio=1.5.5=py39haa95532_0
+  - networkx=2.7.1=pyhd3eb1b0_0
+  - nltk=3.7=pyhd3eb1b0_0
+  - nose=1.3.7=pyhd3eb1b0_1008
+  - notebook=6.4.11=py39haa95532_0
+  - numba=0.55.1=py39hf11a4ad_0
+  - numexpr=2.8.1=py39hb80d3ca_0
+  - numpy=1.21.5=py39h7a0a035_2
+  - numpy-base=1.21.5=py39hca35cd5_2
+  - numpydoc=1.2=pyhd3eb1b0_0
+  - olefile=0.46=pyhd3eb1b0_0
+  - openjpeg=2.4.0=h4fc8c34_0
+  - openpyxl=3.0.9=pyhd3eb1b0_0
+  - openssl=1.1.1o=h2bbff1b_0
+  - packaging=21.3=pyhd3eb1b0_0
+  - pandas=1.4.2=py39hd77b12b_0
+  - pandocfilters=1.5.0=pyhd3eb1b0_0
+  - paramiko=2.8.1=pyhd3eb1b0_0
+  - parso=0.8.3=pyhd3eb1b0_0
+  - partd=1.2.0=pyhd3eb1b0_1
+  - path=16.2.0=pyhd3eb1b0_0
+  - path.py=12.5.0=hd3eb1b0_0
+  - pathlib2=2.3.6=py39haa95532_2
+  - pathspec=0.7.0=py_0
+  - patsy=0.5.2=py39haa95532_1
+  - pep8=1.7.1=py39haa95532_0
+  - pexpect=4.8.0=pyhd3eb1b0_3
+  - pickleshare=0.7.5=pyhd3eb1b0_1003
+  - pillow=9.0.1=py39hdc2b20a_0
+  - pip=21.2.4=py39haa95532_0
+  - pkginfo=1.8.2=pyhd3eb1b0_0
+  - pluggy=1.0.0=py39haa95532_1
+  - ply=3.11=py39haa95532_0
+  - powershell_shortcut=0.0.1=3
+  - poyo=0.5.0=pyhd3eb1b0_0
+  - prometheus_client=0.13.1=pyhd3eb1b0_0
+  - prompt-toolkit=3.0.20=pyhd3eb1b0_0
+  - prompt_toolkit=3.0.20=hd3eb1b0_0
+  - psutil=5.8.0=py39h2bbff1b_1
+  - ptyprocess=0.7.0=pyhd3eb1b0_2
+  - pure_eval=0.2.2=pyhd3eb1b0_0
+  - py=1.11.0=pyhd3eb1b0_0
+  - py-lief=0.11.5=py39hd77b12b_1
+  - pycodestyle=2.7.0=pyhd3eb1b0_0
+  - pycosat=0.6.3=py39h2bbff1b_0
+  - pycparser=2.21=pyhd3eb1b0_0
+  - pycurl=7.44.1=py39hcd4344a_1
+  - pydocstyle=6.1.1=pyhd3eb1b0_0
+  - pyerfa=2.0.0=py39h2bbff1b_0
+  - pyflakes=2.3.1=pyhd3eb1b0_0
+  - pygments=2.11.2=pyhd3eb1b0_0
+  - pylint=2.9.6=py39haa95532_1
+  - pyls-spyder=0.4.0=pyhd3eb1b0_0
+  - pynacl=1.4.0=py39hbd8134f_1
+  - pyodbc=4.0.32=py39hd77b12b_1
+  - pyopenssl=22.0.0=pyhd3eb1b0_0
+  - pyparsing=3.0.4=pyhd3eb1b0_0
+  - pyqt=5.9.2=py39hd77b12b_6
+  - pyreadline=2.1=py39haa95532_1
+  - pyrsistent=0.18.0=py39h196d8e1_0
+  - pysocks=1.7.1=py39haa95532_0
+  - pytables=3.6.1=py39h56d22b6_1
+  - pytest=7.1.1=py39haa95532_0
+  - python=3.9.2=h6244533_0
+  - python-dateutil=2.8.2=pyhd3eb1b0_0
+  - python-fastjsonschema=2.15.1=pyhd3eb1b0_0
+  - python-libarchive-c=2.9=pyhd3eb1b0_1
+  - python-lsp-black=1.0.0=pyhd3eb1b0_0
+  - python-lsp-jsonrpc=1.0.0=pyhd3eb1b0_0
+  - python-lsp-server=1.2.4=pyhd3eb1b0_0
+  - python-slugify=5.0.2=pyhd3eb1b0_0
+  - pytz=2021.3=pyhd3eb1b0_0
+  - pywavelets=1.3.0=py39h2bbff1b_0
+  - pywin32-ctypes=0.2.0=py39haa95532_1000
+  - pywinpty=2.0.2=py39h5da7b33_0
+  - pyyaml=6.0=py39h2bbff1b_1
+  - pyzmq=22.3.0=py39hd77b12b_2
+  - qdarkstyle=3.0.2=pyhd3eb1b0_0
+  - qstylizer=0.1.10=pyhd3eb1b0_0
+  - qt=5.9.7=vc14h73c81de_0
+  - qtawesome=1.0.3=pyhd3eb1b0_0
+  - qtconsole=5.3.0=pyhd3eb1b0_0
+  - qtpy=2.0.1=pyhd3eb1b0_0
+  - regex=2022.3.15=py39h2bbff1b_0
+  - requests=2.27.1=pyhd3eb1b0_0
+  - rope=0.22.0=pyhd3eb1b0_0
+  - rtree=0.9.7=py39h2eaa2aa_1
+  - ruamel_yaml=0.15.100=py39h2bbff1b_0
+  - scikit-image=0.19.2=py39hf11a4ad_0
+  - scikit-learn=1.0.2=py39hf11a4ad_1
+  - scikit-learn-intelex=2021.5.0=py39haa95532_0
+  - scipy=1.7.3=py39h0a974cb_0
+  - seaborn=0.11.2=pyhd3eb1b0_0
+  - send2trash=1.8.0=pyhd3eb1b0_1
+  - setuptools=61.2.0=py39haa95532_0
+  - simplegeneric=0.8.1=py39haa95532_2
+  - singledispatch=3.7.0=pyhd3eb1b0_1001
+  - sip=4.19.13=py39hd77b12b_0
+  - six=1.16.0=pyhd3eb1b0_1
+  - snappy=1.1.9=h6c2663c_0
+  - sniffio=1.2.0=py39haa95532_1
+  - snowballstemmer=2.2.0=pyhd3eb1b0_0
+  - sortedcollections=2.1.0=pyhd3eb1b0_0
+  - sortedcontainers=2.4.0=pyhd3eb1b0_0
+  - soupsieve=2.3.1=pyhd3eb1b0_0
+  - sphinx=4.4.0=pyhd3eb1b0_0
+  - sphinxcontrib=1.0=py39haa95532_1
+  - sphinxcontrib-applehelp=1.0.2=pyhd3eb1b0_0
+  - sphinxcontrib-devhelp=1.0.2=pyhd3eb1b0_0
+  - sphinxcontrib-htmlhelp=2.0.0=pyhd3eb1b0_0
+  - sphinxcontrib-jsmath=1.0.1=pyhd3eb1b0_0
+  - sphinxcontrib-qthelp=1.0.3=pyhd3eb1b0_0
+  - sphinxcontrib-serializinghtml=1.1.5=pyhd3eb1b0_0
+  - sphinxcontrib-websupport=1.2.4=py_0
+  - spyder=5.1.5=py39haa95532_1
+  - spyder-kernels=2.1.3=py39haa95532_0
+  - sqlalchemy=1.4.32=py39h2bbff1b_0
+  - sqlite=3.38.3=h2bbff1b_0
+  - stack_data=0.2.0=pyhd3eb1b0_0
+  - statsmodels=0.13.2=py39h2bbff1b_0
+  - sympy=1.10.1=py39haa95532_0
+  - tbb=2021.5.0=h59b6b97_0
+  - tbb4py=2021.5.0=py39h59b6b97_0
+  - tblib=1.7.0=pyhd3eb1b0_0
+  - terminado=0.13.1=py39haa95532_0
+  - testpath=0.5.0=pyhd3eb1b0_0
+  - text-unidecode=1.3=pyhd3eb1b0_0
+  - textdistance=4.2.1=pyhd3eb1b0_0
+  - threadpoolctl=2.2.0=pyh0d69192_0
+  - three-merge=0.1.1=pyhd3eb1b0_0
+  - tifffile=2021.7.2=pyhd3eb1b0_2
+  - tinycss=0.4=pyhd3eb1b0_1002
+  - tk=8.6.11=h2bbff1b_1
+  - toml=0.10.2=pyhd3eb1b0_0
+  - tomli=1.2.2=pyhd3eb1b0_0
+  - toolz=0.11.2=pyhd3eb1b0_0
+  - tornado=6.1=py39h2bbff1b_0
+  - tqdm=4.64.0=py39haa95532_0
+  - traitlets=5.1.1=pyhd3eb1b0_0
+  - typed-ast=1.4.3=py39h2bbff1b_1
+  - typing-extensions=4.1.1=hd3eb1b0_0
+  - typing_extensions=4.1.1=pyh06a4308_0
+  - tzdata=2022a=hda174b7_0
+  - ujson=5.1.0=py39hd77b12b_0
+  - unicodecsv=0.14.1=py39haa95532_0
+  - unidecode=1.2.0=pyhd3eb1b0_0
+  - urllib3=1.26.9=py39haa95532_0
+  - vc=14.2=h21ff451_1
+  - vs2015_runtime=14.27.29016=h5e58377_2
+  - watchdog=2.1.6=py39haa95532_0
+  - wcwidth=0.2.5=pyhd3eb1b0_0
+  - webencodings=0.5.1=py39haa95532_1
+  - websocket-client=0.58.0=py39haa95532_4
+  - werkzeug=2.0.3=pyhd3eb1b0_0
+  - wheel=0.37.1=pyhd3eb1b0_0
+  - whichcraft=0.6.1=pyhd3eb1b0_0
+  - widgetsnbextension=3.5.2=py39haa95532_0
+  - win_inet_pton=1.1.0=py39haa95532_0
+  - win_unicode_console=0.5=py39haa95532_0
+  - wincertstore=0.2=py39haa95532_2
+  - winpty=0.4.3=4
+  - wrapt=1.12.1=py39h196d8e1_1
+  - xlrd=2.0.1=pyhd3eb1b0_0
+  - xlsxwriter=3.0.3=pyhd3eb1b0_0
+  - xlwings=0.24.9=py39haa95532_0
+  - xlwt=1.3.0=py39haa95532_0
+  - xz=5.2.5=h8cc25b3_1
+  - yaml=0.2.5=he774522_0
+  - yapf=0.31.0=pyhd3eb1b0_0
+  - zfp=0.5.5=hd77b12b_6
+  - zict=2.0.0=pyhd3eb1b0_0
+  - zipp=3.8.0=py39haa95532_0
+  - zlib=1.2.12=h8cc25b3_2
+  - zope=1.0=py39haa95532_1
+  - zope.event=4.5.0=py39haa95532_0
+  - zope.interface=5.4.0=py39h2bbff1b_0
+  - zstd=1.4.9=h19a0ad4_0
+  - pip:
+    - absl-py==1.0.0
+    - alembic==1.7.7
+    - astunparse==1.6.3
+    - cachetools==5.1.0
+    - databricks-cli==0.16.6
+    - docker==5.0.3
+    - docopt==0.6.2
+    - flatbuffers==1.12
+    - gast==0.4.0
+    - gitdb==4.0.9
+    - gitpython==3.1.27
+    - google-auth==2.6.6
+    - google-auth-oauthlib==0.4.6
+    - google-pasta==0.2.0
+    - grpcio==1.46.3
+    - jsonpickle==1.5.2
+    - kaggle==1.5.12
+    - keras==2.9.0
+    - keras-preprocessing==1.1.2
+    - libclang==14.0.1
+    - mako==1.2.0
+    - markdown==3.3.7
+    - mlflow==1.26.0
+    - munch==2.5.0
+    - oauthlib==3.2.0
+    - opt-einsum==3.3.0
+    - prometheus-flask-exporter==0.20.1
+    - protobuf==3.20.1
+    - py-cpuinfo==8.0.0
+    - pyasn1==0.4.8
+    - pyasn1-modules==0.2.8
+    - pyjwt==2.4.0
+    - pymongo==4.1.1
+    - pywin32==227
+    - querystring-parser==1.2.4
+    - requests-oauthlib==1.3.1
+    - rsa==4.8
+    - sacred==0.8.2
+    - sklearn==0.0
+    - smmap==5.0.0
+    - sqlparse==0.4.2
+    - tabulate==0.8.9
+    - tensorboard==2.9.0
+    - tensorboard-data-server==0.6.1
+    - tensorboard-plugin-wit==1.8.1
+    - tensorflow==2.9.0
+    - tensorflow-estimator==2.9.0
+    - tensorflow-io-gcs-filesystem==0.26.0
+    - termcolor==1.1.0
+    - waitress==2.1.1
+prefix: D:\anaconda\envs\s444386

mlflow.py

@@ -0,0 +1,139 @@
+import tensorflow as tf
+import os
+import pandas as pd
+import numpy as np
+import csv
+from sklearn.model_selection import train_test_split
+import sys
+import mlflow
+
+# os.system("kaggle datasets download -d tamber/steam-video-games")
+# os.system("unzip -o steam-video-games.zip")
+
+with mlflow.start_run():
+    epoch = int(sys.argv[1])
+
+    steam=pd.read_csv('data.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
+    steam.isnull().values.any()
+    steam['userId'] = steam.userId.astype(str)
+    purchaseCount = steam[steam["behavior"] != "play"]["game"].value_counts()
+    playCount = steam[steam["behavior"] != "purchase"]["game"].value_counts()
+
+    playerPurchaseCount = steam[steam["behavior"] != "play"]["userId"].value_counts()
+    playerPlayCount = steam[steam["behavior"] != "purchase"]["userId"].value_counts()
+
+    steam = steam[steam['behavior'] != 'purchase']
+    steam = steam.groupby("game").filter(lambda x: len(x)>10)
+    size=int(len(steam)/10)
+
+    meanGame = steam[steam["behavior"] != "purchase"].groupby("game").mean()
+    meanGame = meanGame.to_dict()
+    meanGame = meanGame['hoursPlayed']
+
+    purchaseCount = purchaseCount.to_dict()
+    playCount = playCount.to_dict()
+    playerPurchaseCount = playerPurchaseCount.to_dict()
+    playerPlayCount = playerPlayCount.to_dict()
+
+    steam['meanTime'] = 0;
+    steam['purchaseCount'] = 0;
+    steam['playCount'] = 0;
+    steam['playerPurchaseCount'] =0;
+    steam['playerPlayCount'] =0;
+    steam['playPercent'] =0;
+
+    for i in steam.index:
+        steam.at[i,'meanTime'] = meanGame[steam.at[i,'game']]
+        steam.at[i,'purchaseCount'] = purchaseCount[steam.at[i,'game']]
+        steam.at[i,'playCount'] = playCount[steam.at[i,'game']]
+        steam.at[i,'playerPurchaseCount'] = playerPurchaseCount[steam.at[i,'userId']]
+        steam.at[i,'playerPlayCount'] = playerPlayCount[steam.at[i,'userId']]
+        steam.at[i,'playPercent'] = playerPlayCount[steam.at[i,'userId']]/playerPurchaseCount[steam.at[i,'userId']]
+
+
+    steam_train, steam_test = train_test_split(steam, test_size=size, random_state=1, stratify=steam["game"])
+    steam_train, steam_dev = train_test_split(steam_train, test_size=size, random_state=1, stratify=steam_train["game"])
+
+    print(steam)
+
+    games = {}
+    for i in steam['game']:
+        games[i] = 0
+
+    j=0
+    for key,game in games.items():
+        games[key]=j
+        j=j+1
+
+    for i in steam['game']:
+        i = games[i]
+        
+    invGames = {v: k for k, v in games.items()}
+
+    x_train = steam_train[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
+    y_train = steam_train['game']
+
+    x_test = steam_test[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
+    y_test = steam_test['game']
+
+
+    x_train = np.array(x_train)
+    y_train = np.array(y_train)
+    x_test = np.array(x_test)
+    y_test = np.array(y_test)
+
+    with open('xtest.csv','w',encoding='UTF-8',newline='') as xtest:
+        writer = csv.writer(xtest)
+        for i in x_test:
+            writer.writerow(i)
+        
+    for i,j in enumerate(y_train):
+        y_train[i] = games[j]
+        
+    for i,j in enumerate(y_test):
+        y_test[i] = games[j]
+
+    np.savetxt("ytest.csv",y_test,delimiter=",",fmt='%d')
+
+
+    model = tf.keras.models.Sequential([
+      tf.keras.layers.Flatten(input_shape=(5,1)),
+      tf.keras.layers.Dense(256, activation='relu'),
+      tf.keras.layers.Dropout(0.01),
+      tf.keras.layers.Dense(1000, activation='softmax')
+    ])
+    mlflow.log_param("layers dense relu",256)
+    mlflow.log_param("layers droout",0.01)
+    mlflow.log_param("layers dense softmax",1000)
+    mlflow.log_param("iterations",epoch)
+
+
+    model.compile(optimizer='adam',
+                  loss='sparse_categorical_crossentropy',
+                  metrics=['accuracy'])
+
+    y_train = np.array(y_train).astype(np.float32)
+    y_test = np.array(y_test).astype(np.float32)
+
+
+
+    model.fit(x_train, y_train, epochs=epoch)
+    eval = model.evaluate(x_test, y_test)
+    
+    mlflow.log_metric("accuraccy", eval[1])
+    
+    prediction = model.predict(x_test)
+    classes_x=np.argmax(prediction,axis=1)
+
+    rows = []
+
+    for j,i in enumerate(classes_x):
+        row = [invGames[i],invGames[y_test[j]]]
+        rows.append(row)
+    with open('results.csv','w',encoding='UTF-8',newline='') as f:
+            writer = csv.writer(f)
+            writer.writerow(["predicted", "expected"])
+            for row in rows:
+                writer.writerow(row)
+    
+    model.save('./model')