IUM_06

2024-05-06 21:14:18 +02:00
83 changed files with 98 additions and 2755534 deletions
--- a/.dvc/.gitignore
+++ b/.dvc/.gitignore
@ -1,3 +0,0 @@
-/config.local
-/tmp
-/cache
--- a/.dvc/config
+++ b/.dvc/config
@ -1,4 +0,0 @@
-[core]
-    remote = ium_ssh_remote
-['remote "ium_ssh_remote"']
-    url = ssh://ium-sftp@tzietkiewicz.vm.wmi.amu.edu.pl
--- a/.dvcignore
+++ b/.dvcignore
@ -1,3 +0,0 @@
-# Add patterns of files dvc should ignore, which could improve
-# the performance. Learn more at
-# https://dvc.org/doc/user-guide/dvcignore
--- a/.gitignore
+++ b/.gitignore
@ -1,4 +0,0 @@
-/beer_reviews_train.csv
-/beer_reviews_test.csv
-/beer_review_sentiment_model.h5
-/beer_review_sentiment_predictions.csv
--- a/4
+++ b/4
@ -8,9 +8,9 @@ ARG DEBIAN_FRONTEND=noninteractive
 ENV TZ=Etc/UTC

 RUN apt update && \
-    apt install -y python3 python3-pip unzip git
+    apt install -y python3 python3-pip unzip

-RUN pip install kaggle pandas seaborn scikit-learn tensorflow sacred pymongo --break-system-packages
+RUN pip install kaggle pandas seaborn scikit-learn tensorflow

 WORKDIR /app

--- a/IUM_02.py
+++ b/IUM_02.py
@ -22,7 +22,6 @@ api.dataset_download_files('thedevastator/1-5-million-beer-reviews-from-beer-adv
 #
 # get_ipython().system('kaggle datasets download -d thedevastator/1-5-million-beer-reviews-from-beer-advocate')
 #
-#Change
 #
 # # In[ ]:
 #
--- a/IUM_05-model.py
+++ b/IUM_05-model.py
@ -1,6 +1,5 @@
 import pandas as pd
 import tensorflow as tf
-import sys

 train_data = pd.read_csv('./beer_reviews_train.csv')
 X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
@ -23,6 +22,6 @@ model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

-model.fit(X_train_pad, y_train, epochs=int(sys.argv[1]), batch_size=int(sys.argv[2]), validation_split=0.1)
+model.fit(X_train_pad, y_train, epochs=40, batch_size=32, validation_split=0.1)

 model.save('beer_review_sentiment_model.h5')
--- a/IUM_05-split.py
+++ b/IUM_05-split.py
@ -1,7 +1,7 @@
 import pandas as pd
 from sklearn.model_selection import train_test_split

-data = pd.read_csv('beer_reviews.csv')
+data = pd.read_csv('./beer_reviews.csv')

 train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)

--- a/IUM_06-metrics.py
+++ b/IUM_06-metrics.py
@ -0,0 +1,24 @@
+import pandas as pd
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
+from math import sqrt
+import sys
+
+data = pd.read_csv('beer_review_sentiment_predictions.csv')
+y_pred = data['Predictions']
+y_test = data['Actual']
+y_test_binary = (y_test >= 3).astype(int)
+
+build_number = sys.argv[1]
+
+accuracy = accuracy_score(y_test_binary, y_pred.round())
+precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro')
+rmse = sqrt(mean_squared_error(y_test, y_pred))
+
+print(f'Accuracy: {accuracy}')
+print(f'Micro-avg Precision: {precision}')
+print(f'Micro-avg Recall: {recall}')
+print(f'F1 Score: {f1}')
+print(f'RMSE: {rmse}')
+
+with open(r"beer_metrics.txt", "a") as f:
+    f.write(f"{accuracy},{build_number}\n")
--- a/IUM_06-plot.py
+++ b/IUM_06-plot.py
@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+
+
+def main():
+    accuracy = []
+    build_numbers = []
+
+    with open("beer_metrics.txt") as f:
+        for line in f:
+            accuracy.append(float(line.split(",")[0]))
+            build_numbers.append(int(line.split(",")[1]))
+
+    plt.plot(build_numbers, accuracy)
+    plt.xlabel("Build Number")
+    plt.ylabel("Accuracy")
+    plt.title("Accuracy of the model over time")
+    plt.xticks(range(min(build_numbers), max(build_numbers) + 1))
+    plt.show()
+
+    plt.savefig("acc.png")
+
+
+if __name__ == "__main__":
+    main()
--- a/101
+++ b/101
@ -1,70 +1,61 @@
 pipeline {
-    agent any
-
-    parameters {
-        string(name: 'CUTOFF', defaultValue: '10000', description: 'Liczba wierszy do obcięcia ze zbioru danych')
-	    string(name: 'KAGGLE_USERNAME', defaultValue: '', description: 'Kaggle username')
-        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
+   agent {
+    dockerfile true
    }

+   triggers {
+     upstream(upstreamProjects: 's464979-training/training', threshold: hudson.model.Result.SUCCESS)
+   }
+
+   parameters {
+     buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
+     gitParameter branchFilter: 'origin/(.*)', defaultValue: 'training', name: 'BRANCH', type: 'PT_BRANCH'
+   }
+
    stages {
        stage('Clone Repository') {
            steps {
-                git url: "https://git.wmi.amu.edu.pl/s464979/ium_464979"
+                git branch: 'evaluation', url: "https://git.wmi.amu.edu.pl/s464979/ium_464979"
            }
        }
-        stage('Download dataset') {
+        stage('Copy Dataset Artifacts') {
            steps {
-                 withEnv(["KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", "KAGGLE_KEY=${env.KAGGLE_KEY}"]) {
-                    sh "kaggle datasets download -d thedevastator/1-5-million-beer-reviews-from-beer-advocate --unzip"
-                }
+                 copyArtifacts filter: 'beer_reviews.csv,beer_reviews_train.csv,beer_reviews_test.csv', projectName: 'z-s464979-create-dataset', selector: buildParameter('BUILD_SELECTOR')
            }
        }
-        stage('Process and Split Dataset') {
-            agent {
-                dockerfile {
-                    filename 'Dockerfile'
-                    reuseNode true
-                }
-            }
-            steps {
-                sh "chmod +x ./IUM_05-split.py"
-                sh "python3 ./IUM_05-split.py"
-                archiveArtifacts artifacts: 'beer_reviews.csv,beer_reviews_train.csv,beer_reviews_test.csv', onlyIfSuccessful: true
-            }
-        }
-        stage("Run") {
-            agent {
-                dockerfile {
-                    filename 'Dockerfile'
-                    reuseNode true
-                }
-            }
-            steps {
-                sh "chmod +x ./IUM_05-model.py"
-                sh "chmod +x ./IUM_05-predict.py"
-                sh "python3 ./IUM_05-model.py 10 32"
-                sh "python3 ./IUM_05-predict.py"
-                archiveArtifacts artifacts: 'beer_review_sentiment_model.h5,beer_review_sentiment_predictions.csv', onlyIfSuccessful: true
-            }
-        }
-
-        stage('Sacred') {
-            agent {
-                dockerfile {
-                    filename 'Dockerfile'
-                    reuseNode true
-                }
-            }
-            steps {
-               sh 'chmod +x sacred/sacred_training_model.py'
-               sh 'python3 sacred/sacred_training_model.py'
-            }
-        }
-
-        stage('Archive Artifacts from Experiments') {
+        stage('Copy Training Artifacts') {
          steps {
-            archiveArtifacts artifacts: 'sacred_runs/**/*.*', onlyIfSuccessful: true
+            copyArtifacts filter: 'beer_review_sentiment_model.h5', projectName: 's464979-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
+          }
+        }
+        stage('Copy Evaluation Artifacts') {
+          steps {
+            copyArtifacts filter: 'beer_metrics.txt', projectName: '_s464979-evaluation/evaluation', selector: buildParameter('BUILD_SELECTOR'), optional: true
+          }
+        }
+        stage("Run predictions") {
+            steps {
+                sh "chmod +x ./IUM_05-predict.py"
+                sh "python3 ./IUM_05-predict.py"
+                archiveArtifacts artifacts: 'beer_review_sentiment_predictions.csv', onlyIfSuccessful: true
+            }
+        }
+        stage('Run metrics') {
+          steps {
+            sh 'chmod +x ./IUM_06-metrics.py'
+            sh "python3 ./IUM_06-metrics.py ${currentBuild.number}"
+          }
+        }
+
+        stage('Run plot') {
+          steps {
+            sh 'chmod +x ./IUM_06-plot.py'
+            sh 'python3 ./IUM_06-plot.py'
+          }
+        }
+         stage('Archive Artifacts') {
+          steps {
+            archiveArtifacts artifacts: '*', onlyIfSuccessful: true
          }
        }
    }
--- a/Wieczarek.pptx
+++ b/Wieczarek.pptx
--- a/Test.txt
+++ b/Test.txt
@ -1 +0,0 @@
-asdasd
--- a/data/.gitignore
+++ b/data/.gitignore
@ -1 +0,0 @@
-/beer_reviews.csv
--- a/data/beer_reviews.csv.dvc
+++ b/data/beer_reviews.csv.dvc
@ -1,5 +0,0 @@
-outs:
- md5: 50f6eec0d0fe78bc0f10e35edd271998
-  size: 201644905
-  hash: md5
-  path: beer_reviews.csv
--- a/dvc.lock
+++ b/dvc.lock
@ -1,46 +0,0 @@
-schema: '2.0'
-stages:
-  split_data:
-    cmd: python IUM_05-split.py
-    deps:
-    - path: data/beer_reviews.csv
-      hash: md5
-      md5: 50f6eec0d0fe78bc0f10e35edd271998
-      size: 201644905
-    outs:
-    - path: beer_reviews_test.csv
-      hash: md5
-      md5: edbd0a7f05c59a0c0e936917f60e9b96
-      size: 40632354
-    - path: beer_reviews_train.csv
-      hash: md5
-      md5: 8c6877a26fef1542369bfae6b39d163c
-      size: 162599343
-  train_model:
-    cmd: python IUM_05-model.py 10 32
-    deps:
-    - path: beer_reviews_train.csv
-      hash: md5
-      md5: 8c6877a26fef1542369bfae6b39d163c
-      size: 162599343
-    outs:
-    - path: beer_review_sentiment_model.h5
-      hash: md5
-      md5: c126bd5d332a905262c66894585450e3
-      size: 1950856
-  predict:
-    cmd: python IUM_05-predict.py
-    deps:
-    - path: beer_review_sentiment_model.h5
-      hash: md5
-      md5: c126bd5d332a905262c66894585450e3
-      size: 1950856
-    - path: beer_reviews_test.csv
-      hash: md5
-      md5: edbd0a7f05c59a0c0e936917f60e9b96
-      size: 40632354
-    outs:
-    - path: beer_review_sentiment_predictions.csv
-      hash: md5
-      md5: 12a66fafb7f4d7d19eb0c4a90cc7d3ad
-      size: 4814242
--- a/dvc.yaml
+++ b/dvc.yaml
@ -1,23 +0,0 @@
-stages:
-  split_data:
-    cmd: python IUM_05-split.py
-    deps:
-      - data/beer_reviews.csv
-    outs:
-      - beer_reviews_train.csv
-      - beer_reviews_test.csv
-
-  train_model:
-    cmd: python IUM_05-model.py 10 32
-    deps:
-      - beer_reviews_train.csv
-    outs:
-      - beer_review_sentiment_model.h5
-
-  predict:
-    cmd: python IUM_05-predict.py
-    deps:
-      - beer_review_sentiment_model.h5
-      - beer_reviews_test.csv
-    outs:
-      - beer_review_sentiment_predictions.csv
--- a/environment.yml
+++ b/environment.yml
@ -1,338 +0,0 @@
-name: uczenie_glebokie
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - _tflow_select=2.3.0=mkl
-  - abseil-cpp=20211102.0=h36ffca9_3
-  - absl-py=2.1.0=pyhd8ed1ab_0
-  - aiohttp=3.9.3=py310h8d17308_1
-  - aiosignal=1.3.1=pyhd8ed1ab_0
-  - alembic=1.13.1=pyhd8ed1ab_1
-  - aniso8601=9.0.1=pyhd8ed1ab_0
-  - anyio=4.3.0=pyhd8ed1ab_0
-  - aom=3.6.0=hd77b12b_0
-  - argon2-cffi=23.1.0=pyhd8ed1ab_0
-  - argon2-cffi-bindings=21.2.0=py310h8d17308_4
-  - arrow=1.3.0=pyhd8ed1ab_0
-  - arrow-cpp=11.0.0=h2c9b28c_2
-  - asttokens=2.4.1=pyhd8ed1ab_0
-  - astunparse=1.6.3=pyhd8ed1ab_0
-  - async-lru=2.0.4=pyhd8ed1ab_0
-  - async-timeout=4.0.3=pyhd8ed1ab_0
-  - attrs=23.2.0=pyh71513ae_0
-  - aws-c-common=0.4.57=ha925a31_1
-  - aws-c-event-stream=0.1.6=h7915e17_3
-  - aws-checksums=0.1.9=hb01e4cc_0
-  - aws-sdk-cpp=1.8.185=hd77b12b_0
-  - babel=2.14.0=pyhd8ed1ab_0
-  - bcrypt=4.1.3=py310hc226416_0
-  - beautifulsoup4=4.12.3=pyha770c72_0
-  - blas=1.0=mkl
-  - bleach=6.1.0=pyhd8ed1ab_0
-  - blinker=1.7.0=pyhd8ed1ab_0
-  - blosc=1.21.5=hdccc3a2_0
-  - boost-cpp=1.84.0=h6f18f0d_2
-  - bottleneck=1.3.8=py310h3e78b6c_0
-  - brotli=1.0.9=h2bbff1b_7
-  - brotli-bin=1.0.9=h2bbff1b_7
-  - brotli-python=1.0.9=py310h00ffb61_8
-  - bzip2=1.0.8=hcfcfb64_5
-  - c-ares=1.28.1=hcfcfb64_0
-  - ca-certificates=2024.2.2=h56e8100_0
-  - cached-property=1.5.2=hd8ed1ab_1
-  - cached_property=1.5.2=pyha770c72_1
-  - cachetools=5.3.3=pyhd8ed1ab_0
-  - certifi=2024.2.2=pyhd8ed1ab_0
-  - cffi=1.16.0=py310h8d17308_0
-  - cfitsio=3.470=h2bbff1b_7
-  - charls=2.2.0=h6c2663c_0
-  - charset-normalizer=3.3.2=pyhd8ed1ab_0
-  - click=8.1.7=win_pyh7428d3b_0
-  - cloudpickle=3.0.0=pyhd8ed1ab_0
-  - colorama=0.4.6=pyhd8ed1ab_0
-  - comm=0.2.2=pyhd8ed1ab_0
-  - contourpy=1.2.1=py310h232114e_0
-  - cryptography=41.0.3=py310h3438e0d_0
-  - cycler=0.12.1=pyhd8ed1ab_0
-  - dav1d=1.2.1=hcfcfb64_0
-  - debugpy=1.8.1=py310h00ffb61_0
-  - decorator=5.1.1=pyhd8ed1ab_0
-  - defusedxml=0.7.1=pyhd8ed1ab_0
-  - docker-py=7.0.0=pyhd8ed1ab_0
-  - eigen=3.4.0=h91493d7_0
-  - entrypoints=0.4=pyhd8ed1ab_0
-  - exceptiongroup=1.2.0=pyhd8ed1ab_2
-  - executing=2.0.1=pyhd8ed1ab_0
-  - ffmpeg=4.2.3=ha925a31_0
-  - flask=3.0.3=pyhd8ed1ab_0
-  - flatbuffers=24.3.25=h63175ca_0
-  - fonttools=4.51.0=py310h8d17308_0
-  - fqdn=1.5.1=pyhd8ed1ab_0
-  - freetype=2.12.1=hdaf720e_2
-  - frozenlist=1.4.1=py310h8d17308_0
-  - gast=0.4.0=pyh9f0ad1d_0
-  - gflags=2.2.2=ha925a31_1004
-  - giflib=5.2.1=h64bf75a_3
-  - gitdb=4.0.11=pyhd8ed1ab_0
-  - gitpython=3.1.43=pyhd8ed1ab_0
-  - glib=2.80.0=h39d0aa6_3
-  - glib-tools=2.80.0=h0a98069_3
-  - glog=0.5.0=h4797de2_0
-  - google-auth=2.29.0=pyhca7485f_0
-  - google-auth-oauthlib=0.4.1=py_2
-  - google-pasta=0.2.0=pyh8c360ce_0
-  - graphene=3.3=pyhd8ed1ab_0
-  - graphql-core=3.2.3=pyhd8ed1ab_0
-  - graphql-relay=3.2.0=pyhd8ed1ab_0
-  - greenlet=3.0.3=py310h00ffb61_0
-  - grpc-cpp=1.48.2=hf108199_0
-  - grpcio=1.42.0=py310hc60d5dd_0
-  - gst-plugins-base=1.18.5=h9e645db_0
-  - gstreamer=1.18.5=hd78058f_0
-  - h11=0.14.0=pyhd8ed1ab_0
-  - h2=4.1.0=pyhd8ed1ab_0
-  - h5py=3.7.0=nompi_py310h00cbb18_100
-  - hdf5=1.12.1=nompi_h2a0e4a3_104
-  - hpack=4.0.0=pyh9f0ad1d_0
-  - httpcore=1.0.5=pyhd8ed1ab_0
-  - httpx=0.27.0=pyhd8ed1ab_0
-  - hyperframe=6.0.1=pyhd8ed1ab_0
-  - icu=58.2=ha925a31_3
-  - idna=3.6=pyhd8ed1ab_0
-  - imagecodecs=2023.1.23=py310h6c6a46e_0
-  - imageio=2.34.0=pyh4b66e23_0
-  - importlib-metadata=7.1.0=pyha770c72_0
-  - importlib_metadata=7.1.0=hd8ed1ab_0
-  - importlib_resources=6.4.0=pyhd8ed1ab_0
-  - intel-openmp=2023.1.0=h59b6b97_46320
-  - ipykernel=6.29.3=pyha63f2e9_0
-  - ipython=8.22.2=pyh7428d3b_0
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-  - isoduration=20.11.0=pyhd8ed1ab_0
-  - itsdangerous=2.2.0=pyhd8ed1ab_0
-  - jedi=0.19.1=pyhd8ed1ab_0
-  - jinja2=3.1.3=pyhd8ed1ab_0
-  - joblib=1.3.2=pyhd8ed1ab_0
-  - jpeg=9e=hcfcfb64_3
-  - json5=0.9.24=pyhd8ed1ab_0
-  - jsonpointer=2.4=py310h5588dad_3
-  - jsonschema=4.21.1=pyhd8ed1ab_0
-  - jsonschema-specifications=2023.12.1=pyhd8ed1ab_0
-  - jsonschema-with-format-nongpl=4.21.1=pyhd8ed1ab_0
-  - jupyter=1.0.0=py310haa95532_9
-  - jupyter-lsp=2.2.4=pyhd8ed1ab_0
-  - jupyter_client=8.6.1=pyhd8ed1ab_0
-  - jupyter_console=6.6.3=pyhd8ed1ab_0
-  - jupyter_core=5.7.2=py310h5588dad_0
-  - jupyter_events=0.10.0=pyhd8ed1ab_0
-  - jupyter_server=2.13.0=pyhd8ed1ab_0
-  - jupyter_server_terminals=0.5.3=pyhd8ed1ab_0
-  - jupyterlab=4.1.5=pyhd8ed1ab_0
-  - jupyterlab_pygments=0.3.0=pyhd8ed1ab_1
-  - jupyterlab_server=2.25.4=pyhd8ed1ab_0
-  - jupyterlab_widgets=3.0.10=pyhd8ed1ab_0
-  - keras=2.10.0=py310haa95532_0
-  - keras-preprocessing=1.1.2=pyhd8ed1ab_0
-  - kiwisolver=1.4.5=py310h232114e_1
-  - lazy_loader=0.4=pyhd8ed1ab_0
-  - lcms2=2.12=h83e58a3_0
-  - lerc=3.0=hd77b12b_0
-  - libabseil-static=20211102.0=cxx11_h58a5ce6_3
-  - libaec=1.1.3=h63175ca_0
-  - libavif=0.11.1=h2bbff1b_0
-  - libblas=3.9.0=20_win64_mkl
-  - libboost=1.84.0=hcc118f5_2
-  - libboost-devel=1.84.0=h91493d7_2
-  - libboost-headers=1.84.0=h57928b3_2
-  - libbrotlicommon=1.0.9=h2bbff1b_7
-  - libbrotlidec=1.0.9=h2bbff1b_7
-  - libbrotlienc=1.0.9=h2bbff1b_7
-  - libcblas=3.9.0=20_win64_mkl
-  - libclang=12.0.0=default_h627e005_2
-  - libcurl=8.5.0=h86230a5_0
-  - libdeflate=1.17=h2bbff1b_1
-  - libffi=3.4.2=h8ffe710_5
-  - libglib=2.80.0=h39d0aa6_3
-  - libiconv=1.17=hcfcfb64_2
-  - libintl=0.22.5=h5728263_2
-  - libintl-devel=0.22.5=h5728263_2
-  - liblapack=3.9.0=20_win64_mkl
-  - libogg=1.3.4=h8ffe710_1
-  - libopencv=4.6.0=haa95532_5
-  - libpng=1.6.43=h19919ed_0
-  - libprotobuf=3.20.3=h12be248_0
-  - libsodium=1.0.18=h8d14728_1
-  - libsqlite=3.45.2=hcfcfb64_0
-  - libssh2=1.10.0=hcd4344a_2
-  - libthrift=0.15.0=h636ae23_1
-  - libtiff=4.5.1=hd77b12b_0
-  - libvorbis=1.3.7=h0e60522_0
-  - libwebp=1.3.2=hcfcfb64_1
-  - libwebp-base=1.3.2=hcfcfb64_0
-  - libxml2=2.10.4=h0ad7f3c_1
-  - libxslt=1.1.37=h2bbff1b_1
-  - libzlib=1.2.13=hcfcfb64_5
-  - libzopfli=1.0.3=h0e60522_0
-  - lz4-c=1.9.4=hcfcfb64_0
-  - mako=1.3.5=pyhd8ed1ab_0
-  - markdown=3.6=pyhd8ed1ab_0
-  - markupsafe=2.1.5=py310h8d17308_0
-  - matplotlib-base=3.8.3=py310hc9baf74_0
-  - matplotlib-inline=0.1.6=pyhd8ed1ab_0
-  - mistune=3.0.2=pyhd8ed1ab_0
-  - mkl=2023.2.0=h6a75c08_50497
-  - mkl-service=2.4.1=py310h49a50da_0
-  - mkl_fft=1.3.8=py310h042f14a_1
-  - mkl_random=1.2.5=py310hd199dba_1
-  - mlflow=2.12.2=h5588dad_0
-  - mlflow-skinny=2.12.2=py310h5588dad_0
-  - mlflow-ui=2.12.2=py310h5588dad_0
-  - multidict=6.0.5=py310h8d17308_0
-  - munkres=1.1.4=pyh9f0ad1d_0
-  - nbclient=0.10.0=pyhd8ed1ab_0
-  - nbconvert=7.16.3=hd8ed1ab_0
-  - nbconvert-core=7.16.3=pyhd8ed1ab_0
-  - nbconvert-pandoc=7.16.3=hd8ed1ab_0
-  - nbformat=5.10.4=pyhd8ed1ab_0
-  - nest-asyncio=1.6.0=pyhd8ed1ab_0
-  - networkx=3.3=pyhd8ed1ab_1
-  - notebook=7.1.2=pyhd8ed1ab_0
-  - notebook-shim=0.2.4=pyhd8ed1ab_0
-  - numexpr=2.9.0=mkl_py310hc26a618_0
-  - numpy=1.24.3=py310h055cbcc_1
-  - numpy-base=1.24.3=py310h65a83cf_1
-  - oauthlib=3.2.2=pyhd8ed1ab_0
-  - opencv=4.6.0=py310ha36de5b_5
-  - openjpeg=2.4.0=h4fc8c34_0
-  - openssl=1.1.1w=hcfcfb64_0
-  - opt_einsum=3.3.0=pyhc1e730c_2
-  - orc=1.7.4=h623e30f_1
-  - overrides=7.7.0=pyhd8ed1ab_0
-  - packaging=24.0=pyhd8ed1ab_0
-  - pandas=2.2.1=py310h5da7b33_0
-  - pandoc=3.1.13=h57928b3_0
-  - pandocfilters=1.5.0=pyhd8ed1ab_0
-  - paramiko=3.4.0=pyhd8ed1ab_0
-  - parso=0.8.4=pyhd8ed1ab_0
-  - pcre2=10.43=h17e33f8_0
-  - pickleshare=0.7.5=py_1003
-  - pillow=10.2.0=py310h2bbff1b_0
-  - pip=24.0=pyhd8ed1ab_0
-  - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1
-  - platformdirs=4.2.0=pyhd8ed1ab_0
-  - ply=3.11=pyhd8ed1ab_2
-  - prometheus_client=0.20.0=pyhd8ed1ab_0
-  - prometheus_flask_exporter=0.23.0=pyhd8ed1ab_0
-  - prompt-toolkit=3.0.42=pyha770c72_0
-  - prompt_toolkit=3.0.42=hd8ed1ab_0
-  - protobuf=3.20.3=py310h5588dad_1
-  - psutil=5.9.8=py310h8d17308_0
-  - pure_eval=0.2.2=pyhd8ed1ab_0
-  - py-opencv=4.6.0=haa95532_5
-  - pyarrow=11.0.0=py310h790e06d_1
-  - pyasn1=0.5.1=pyhd8ed1ab_0
-  - pyasn1-modules=0.3.0=pyhd8ed1ab_0
-  - pycparser=2.22=pyhd8ed1ab_0
-  - pygments=2.17.2=pyhd8ed1ab_0
-  - pyjwt=2.8.0=pyhd8ed1ab_1
-  - pynacl=1.5.0=py310h635b8f1_3
-  - pyopenssl=23.2.0=pyhd8ed1ab_1
-  - pyparsing=3.1.2=pyhd8ed1ab_0
-  - pyqt=5.15.10=py310hd77b12b_0
-  - pyqt5-sip=12.13.0=py310h2bbff1b_0
-  - pysocks=1.7.1=pyh0701188_6
-  - python=3.10.13=h966fe2a_0
-  - python-dateutil=2.9.0=pyhd8ed1ab_0
-  - python-fastjsonschema=2.19.1=pyhd8ed1ab_0
-  - python-flatbuffers=24.3.25=pyh59ac667_0
-  - python-json-logger=2.0.7=pyhd8ed1ab_0
-  - python-tzdata=2024.1=pyhd8ed1ab_0
-  - python_abi=3.10=2_cp310
-  - pytz=2024.1=pyhd8ed1ab_0
-  - pyu2f=0.1.5=pyhd8ed1ab_0
-  - pywin32=306=py310h00ffb61_2
-  - pywin32-on-windows=0.1.0=pyh07e9846_2
-  - pywinpty=2.0.13=py310h00ffb61_0
-  - pyyaml=6.0.1=py310h8d17308_1
-  - pyzmq=25.1.2=py310h2849c00_0
-  - qt-main=5.15.2=he8e5bd7_7
-  - qt-webengine=5.15.9=h5bd16bc_7
-  - qtconsole=5.5.1=pyhd8ed1ab_0
-  - qtconsole-base=5.5.1=pyha770c72_0
-  - qtpy=2.4.1=pyhd8ed1ab_0
-  - qtwebkit=5.212=h2bbfb41_5
-  - querystring_parser=1.2.4=py_0
-  - re2=2022.04.01=h0e60522_0
-  - referencing=0.34.0=pyhd8ed1ab_0
-  - requests=2.31.0=pyhd8ed1ab_0
-  - requests-oauthlib=2.0.0=pyhd8ed1ab_0
-  - rfc3339-validator=0.1.4=pyhd8ed1ab_0
-  - rfc3986-validator=0.1.1=pyh9f0ad1d_0
-  - rpds-py=0.18.0=py310h87d50f1_0
-  - rsa=4.9=pyhd8ed1ab_0
-  - scikit-image=0.22.0=py310h25bd2df_0
-  - scikit-learn=1.3.0=py310h4ed8f06_1
-  - scipy=1.13.0=py310hf667824_0
-  - seaborn=0.12.2=py310haa95532_0
-  - send2trash=1.8.2=pyh08f2357_0
-  - setuptools=69.2.0=pyhd8ed1ab_0
-  - sip=6.7.12=py310h00ffb61_0
-  - six=1.16.0=pyh6c4a22f_0
-  - smmap=5.0.0=pyhd8ed1ab_0
-  - snappy=1.1.10=hfb803bf_0
-  - sniffio=1.3.1=pyhd8ed1ab_0
-  - soupsieve=2.5=pyhd8ed1ab_1
-  - sqlalchemy=2.0.30=py310ha8f682b_0
-  - sqlite=3.45.2=hcfcfb64_0
-  - sqlparse=0.5.0=pyhd8ed1ab_0
-  - stack_data=0.6.2=pyhd8ed1ab_0
-  - tbb=2021.8.0=h59b6b97_0
-  - tensorboard=2.10.0=py310haa95532_0
-  - tensorboard-data-server=0.6.1=py310haa95532_0
-  - tensorboard-plugin-wit=1.8.1=pyhd8ed1ab_0
-  - tensorflow=2.10.0=mkl_py310hd99672f_0
-  - tensorflow-base=2.10.0=mkl_py310h6a7f48e_0
-  - tensorflow-estimator=2.10.0=py310haa95532_0
-  - termcolor=2.4.0=pyhd8ed1ab_0
-  - terminado=0.18.1=pyh5737063_0
-  - threadpoolctl=3.4.0=pyhc1e730c_0
-  - tifffile=2023.2.28=pyhd8ed1ab_0
-  - tinycss2=1.2.1=pyhd8ed1ab_0
-  - tk=8.6.13=h5226925_1
-  - tomli=2.0.1=pyhd8ed1ab_0
-  - tornado=6.4=py310h8d17308_0
-  - traitlets=5.14.2=pyhd8ed1ab_0
-  - types-python-dateutil=2.9.0.20240316=pyhd8ed1ab_0
-  - typing-extensions=4.11.0=hd8ed1ab_0
-  - typing_extensions=4.11.0=pyha770c72_0
-  - typing_utils=0.1.0=pyhd8ed1ab_0
-  - tzdata=2024a=h0c530f3_0
-  - ucrt=10.0.22621.0=h57928b3_0
-  - unicodedata2=15.1.0=py310h8d17308_0
-  - uri-template=1.3.0=pyhd8ed1ab_0
-  - urllib3=2.2.1=pyhd8ed1ab_0
-  - utf8proc=2.6.1=h2bbff1b_1
-  - vc=14.3=hcf57466_18
-  - vc14_runtime=14.38.33130=h82b7239_18
-  - vs2015_runtime=14.38.33130=hcb4865c_18
-  - waitress=2.1.2=pyhd8ed1ab_0
-  - wcwidth=0.2.13=pyhd8ed1ab_0
-  - webcolors=1.13=pyhd8ed1ab_0
-  - webencodings=0.5.1=pyhd8ed1ab_2
-  - websocket-client=1.7.0=pyhd8ed1ab_0
-  - werkzeug=3.0.2=pyhd8ed1ab_0
-  - wheel=0.43.0=pyhd8ed1ab_1
-  - widgetsnbextension=4.0.10=pyhd8ed1ab_0
-  - win_inet_pton=1.1.0=pyhd8ed1ab_6
-  - winpty=0.4.3=4
-  - wrapt=1.16.0=py310h8d17308_0
-  - xz=5.4.6=h8cc25b3_0
-  - yaml=0.2.5=h8ffe710_2
-  - yarl=1.9.4=py310h8d17308_0
-  - zeromq=4.3.5=h63175ca_1
-  - zfp=1.0.1=h63175ca_0
-  - zipp=3.17.0=pyhd8ed1ab_0
-  - zlib=1.2.13=hcfcfb64_5
-  - zstd=1.5.5=h12be248_0
-prefix: C:\Users\adamw\.conda\envs\uczenie_glebokie
--- a/mlflow_project/MLproject
+++ b/mlflow_project/MLproject
@ -1,10 +0,0 @@
-name: MLflow_s464979
-
-conda_env: conda.yaml
-
-entry_points:
-  optimal_parameters:
-    parameters:
-      epochs: { type: int, default: 20 }
-      batch_size: { type: int, default: 32 }
-    command: 'python mlflow_training_model.py {epochs} {batch_size}'
--- a/mlflow_project/beer_review_sentiment_predictions.csv
+++ b/mlflow_project/beer_review_sentiment_predictions.csv
--- a/mlflow_project/conda.yaml
+++ b/mlflow_project/conda.yaml
@ -1,11 +0,0 @@
-name: MLflow_s464979
-channels:
-  - defaults
-dependencies:
-  - python=3.10
-  - pip
-  - pip:
-      - mlflow
-      - tensorflow
-      - pandas
-      - scikit-learn
--- a/mlflow_project/mlflow_training_model.py
+++ b/mlflow_project/mlflow_training_model.py
@ -1,53 +0,0 @@
-import pandas as pd
-import tensorflow as tf
-import sys
-import mlflow
-from sklearn.metrics import accuracy_score
-
-mlflow.set_tracking_uri("http://localhost:5000")
-
-def main():
-    train_data = pd.read_csv('./beer_reviews_train.csv')
-    X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
-    y_train = train_data['review_overall']
-
-    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
-    tokenizer.fit_on_texts(X_train)
-    X_train_seq = tokenizer.texts_to_sequences(X_train)
-
-    X_train_pad = tf.keras.preprocessing.sequence.pad_sequences(X_train_seq, maxlen=100)
-
-    with mlflow.start_run() as run:
-        print("MLflow run experiment_id: {0}".format(run.info.experiment_id))
-        print("MLflow run artifact_uri: {0}".format(run.info.artifact_uri))
-        model = tf.keras.Sequential([
-            tf.keras.layers.Embedding(input_dim=10000, output_dim=16, input_length=100),
-            tf.keras.layers.GlobalAveragePooling1D(),
-            tf.keras.layers.Dense(16, activation='relu'),
-            tf.keras.layers.Dense(1, activation='sigmoid')
-        ])
-
-        model.compile(optimizer='adam',
-                      loss='binary_crossentropy',
-                      metrics=['accuracy'])
-
-        print(sys.argv[1])
-        print(sys.argv[2])
-        model.fit(X_train_pad, y_train, epochs=int(sys.argv[1]), batch_size=int(sys.argv[2]), validation_split=0.1)
-
-        mlflow.log_param("epochs", int(sys.argv[1]))
-        mlflow.log_param("batch_size", int(sys.argv[2]))
-
-        test_data = pd.read_csv('./beer_reviews_test.csv')
-        X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
-        y_test = test_data['review_overall']
-
-        predictions = model.predict(X_test).flatten()
-
-        y_test_binary = (y_test >= 3).astype(int)
-
-        accuracy = accuracy_score(y_test_binary, predictions.round())
-        mlflow.log_metric("accuracy", accuracy)
-
-if __name__ == '__main__':
-    main()
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/meta.yaml
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/meta.yaml
@ -1,15 +0,0 @@
-artifact_uri: file:///C:/Users/adamw/REPOS/ium_464979/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/artifacts
-end_time: 1715624675138
-entry_point_name: ''
-experiment_id: '0'
-lifecycle_stage: active
-run_id: 2824b8df5d6d414faf28b318d8b870b9
-run_name: auspicious-pig-388
-run_uuid: 2824b8df5d6d414faf28b318d8b870b9
-source_name: ''
-source_type: 4
-source_version: ''
-start_time: 1715624648948
-status: 3
-tags: []
-user_id: adamw
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/metrics/accuracy
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/metrics/accuracy
@ -1 +0,0 @@
-1715624674602 0.9242538359967604 0
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/params/batch_size
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/params/batch_size
@ -1 +0,0 @@
-64
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/params/epochs
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/params/epochs
@ -1 +0,0 @@
-20
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.gitRepoURL
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.gitRepoURL
@ -1 +0,0 @@
-https://git.wmi.amu.edu.pl/s464979/ium_464979.git
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.project.backend
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.project.backend
@ -1 +0,0 @@
-local
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.project.entryPoint
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.project.entryPoint
@ -1 +0,0 @@
-optimal_parameters
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.project.env
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.project.env
@ -1 +0,0 @@
-conda
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.runName
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.runName
@ -1 +0,0 @@
-auspicious-pig-388
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.source.git.commit
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.source.git.commit
@ -1 +0,0 @@
-0dbf6f1959cb042149cd568c8b11684f23c68024
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.source.git.repoURL
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.source.git.repoURL
@ -1 +0,0 @@
-https://git.wmi.amu.edu.pl/s464979/ium_464979.git
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.source.name
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.source.name
@ -1 +0,0 @@
-file://C:\Users\adamw\REPOS\ium_464979#\mlflow_project
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.source.type
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.source.type
@ -1 +0,0 @@
-PROJECT
--- a/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.user
+++ b/mlruns/0/2824b8df5d6d414faf28b318d8b870b9/tags/mlflow.user
@ -1 +0,0 @@
-adamw
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/meta.yaml
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/meta.yaml
@ -1,15 +0,0 @@
-artifact_uri: file:///C:/Users/adamw/REPOS/ium_464979/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/artifacts
-end_time: 1715624631264
-entry_point_name: ''
-experiment_id: '0'
-lifecycle_stage: active
-run_id: 5cbc975a93e94b8eb27d7dca17d65191
-run_name: unruly-lamb-469
-run_uuid: 5cbc975a93e94b8eb27d7dca17d65191
-source_name: ''
-source_type: 4
-source_version: ''
-start_time: 1715624604038
-status: 3
-tags: []
-user_id: adamw
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/metrics/accuracy
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/metrics/accuracy
@ -1 +0,0 @@
-1715624630726 0.9242538359967604 0
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/params/batch_size
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/params/batch_size
@ -1 +0,0 @@
-32
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/params/epochs
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/params/epochs
@ -1 +0,0 @@
-40
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.gitRepoURL
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.gitRepoURL
@ -1 +0,0 @@
-https://git.wmi.amu.edu.pl/s464979/ium_464979.git
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.project.backend
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.project.backend
@ -1 +0,0 @@
-local
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.project.entryPoint
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.project.entryPoint
@ -1 +0,0 @@
-optimal_parameters
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.project.env
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.project.env
@ -1 +0,0 @@
-conda
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.runName
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.runName
@ -1 +0,0 @@
-unruly-lamb-469
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.source.git.commit
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.source.git.commit
@ -1 +0,0 @@
-0dbf6f1959cb042149cd568c8b11684f23c68024
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.source.git.repoURL
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.source.git.repoURL
@ -1 +0,0 @@
-https://git.wmi.amu.edu.pl/s464979/ium_464979.git
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.source.name
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.source.name
@ -1 +0,0 @@
-file://C:\Users\adamw\REPOS\ium_464979#\mlflow_project
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.source.type
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.source.type
@ -1 +0,0 @@
-PROJECT
--- a/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.user
+++ b/mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/tags/mlflow.user
@ -1 +0,0 @@
-adamw
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/meta.yaml
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/meta.yaml
@ -1,15 +0,0 @@
-artifact_uri: file:///C:/Users/adamw/REPOS/ium_464979/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/artifacts
-end_time: 1715624529496
-entry_point_name: ''
-experiment_id: '0'
-lifecycle_stage: active
-run_id: 5df6baa9d7c143d7a16314c7721ef48e
-run_name: silent-hound-874
-run_uuid: 5df6baa9d7c143d7a16314c7721ef48e
-source_name: ''
-source_type: 4
-source_version: ''
-start_time: 1715624503432
-status: 3
-tags: []
-user_id: adamw
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/metrics/accuracy
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/metrics/accuracy
@ -1 +0,0 @@
-1715624528800 0.9242538359967604 0
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/params/batch_size
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/params/batch_size
@ -1 +0,0 @@
-32
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/params/epochs
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/params/epochs
@ -1 +0,0 @@
-20
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.gitRepoURL
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.gitRepoURL
@ -1 +0,0 @@
-https://git.wmi.amu.edu.pl/s464979/ium_464979.git
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.project.backend
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.project.backend
@ -1 +0,0 @@
-local
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.project.entryPoint
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.project.entryPoint
@ -1 +0,0 @@
-optimal_parameters
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.project.env
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.project.env
@ -1 +0,0 @@
-conda
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.runName
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.runName
@ -1 +0,0 @@
-silent-hound-874
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.source.git.commit
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.source.git.commit
@ -1 +0,0 @@
-0dbf6f1959cb042149cd568c8b11684f23c68024
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.source.git.repoURL
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.source.git.repoURL
@ -1 +0,0 @@
-https://git.wmi.amu.edu.pl/s464979/ium_464979.git
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.source.name
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.source.name
@ -1 +0,0 @@
-file://C:\Users\adamw\REPOS\ium_464979#\mlflow_project
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.source.type
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.source.type
@ -1 +0,0 @@
-PROJECT
--- a/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.user
+++ b/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/tags/mlflow.user
@ -1 +0,0 @@
-adamw
--- a/mlruns/0/meta.yaml
+++ b/mlruns/0/meta.yaml
@ -1,6 +0,0 @@
-artifact_location: file:///C:/Users/adamw/REPOS/ium_464979/mlruns/0
-creation_time: 1715624503432
-experiment_id: '0'
-last_update_time: 1715624503432
-lifecycle_stage: active
-name: Default
--- a/sacred/beer_reviews.csv
+++ b/sacred/beer_reviews.csv
--- a/sacred/beer_reviews_test.csv
+++ b/sacred/beer_reviews_test.csv
--- a/sacred/beer_reviews_train.csv
+++ b/sacred/beer_reviews_train.csv
--- a/sacred/sacred_runs/1/beer_review_model.h5
+++ b/sacred/sacred_runs/1/beer_review_model.h5
--- a/sacred/sacred_runs/1/config.json
+++ b/sacred/sacred_runs/1/config.json
@ -1,5 +0,0 @@
-{
-  "batch_size": 32,
-  "epochs": 10,
-  "seed": 373303958
-}
--- a/sacred/sacred_runs/1/cout.txt
+++ b/sacred/sacred_runs/1/cout.txt
--- a/sacred/sacred_runs/1/metrics.json
+++ b/sacred/sacred_runs/1/metrics.json
@ -1 +0,0 @@
-{}
--- a/sacred/sacred_runs/1/run.json
+++ b/sacred/sacred_runs/1/run.json
@ -1,100 +0,0 @@
-{
-  "artifacts": [
-    "beer_review_model.h5"
-  ],
-  "command": "run_experiment",
-  "experiment": {
-    "base_dir": "C:\\Users\\adamw\\REPOS\\ium_464979\\sacred",
-    "dependencies": [
-      "keras==2.12.0",
-      "numpy==1.23.5",
-      "sacred==0.8.5",
-      "scikit-learn==1.2.2"
-    ],
-    "mainfile": "sacred_training_model.py",
-    "name": "464979",
-    "repositories": [
-      {
-        "commit": "e9f53be95453a8da8811653ba3c4a6e75895cd33",
-        "dirty": true,
-        "url": "https://git.wmi.amu.edu.pl/s464979/ium_464979.git"
-      }
-    ],
-    "sources": [
-      [
-        "sacred_training_model.py",
-        "_sources\\sacred_training_model_2a1e89d7c820c7a00319e1e22827c7f9.py"
-      ]
-    ]
-  },
-  "heartbeat": "2024-06-11T17:21:14.840246",
-  "host": {
-    "ENV": {},
-    "cpu": "Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz",
-    "gpus": {
-      "driver_version": "555.85",
-      "gpus": [
-        {
-          "model": "NVIDIA GeForce GTX 1660 Ti",
-          "persistence_mode": false,
-          "total_memory": 6144
-        }
-      ]
-    },
-    "hostname": "DESKTOP-9SEHQM2",
-    "os": [
-      "Windows",
-      "Windows-10-10.0.19045-SP0"
-    ],
-    "python_version": "3.11.7"
-  },
-  "meta": {
-    "command": "run_experiment",
-    "config_updates": {},
-    "named_configs": [],
-    "options": {
-      "--beat-interval": null,
-      "--capture": null,
-      "--comment": null,
-      "--debug": false,
-      "--enforce_clean": false,
-      "--file_storage": null,
-      "--force": false,
-      "--help": false,
-      "--id": null,
-      "--loglevel": null,
-      "--mongo_db": null,
-      "--name": null,
-      "--pdb": false,
-      "--print-config": false,
-      "--priority": null,
-      "--queue": false,
-      "--s3": null,
-      "--sql": null,
-      "--tiny_db": null,
-      "--unobserved": false,
-      "COMMAND": null,
-      "UPDATE": [],
-      "help": false,
-      "with": false
-    }
-  },
-  "resources": [
-    [
-      "C:\\Users\\adamw\\REPOS\\ium_464979\\sacred\\beer_reviews_train.csv",
-      "sacred_runs\\_resources\\beer_reviews_train_e8dab75a0ec202f56510a0e1f9926ad7.csv"
-    ],
-    [
-      "C:\\Users\\adamw\\REPOS\\ium_464979\\sacred\\beer_reviews_test.csv",
-      "sacred_runs\\_resources\\beer_reviews_test_56070f83bef3ee1d17d1a632aa55b798.csv"
-    ]
-  ],
-  "result": {
-    "dtype": "float64",
-    "py/object": "numpy.float64",
-    "value": 0.9237146778770103
-  },
-  "start_time": "2024-06-11T17:21:03.851734",
-  "status": "COMPLETED",
-  "stop_time": "2024-06-11T17:21:14.839247"
-}
--- a/sacred/sacred_runs/2/beer_review_sentiment_model.keras
+++ b/sacred/sacred_runs/2/beer_review_sentiment_model.keras
--- a/sacred/sacred_runs/2/config.json
+++ b/sacred/sacred_runs/2/config.json
@ -1,5 +0,0 @@
-{
-  "batch_size": 32,
-  "epochs": 10,
-  "seed": 541882551
-}
--- a/sacred/sacred_runs/2/cout.txt
+++ b/sacred/sacred_runs/2/cout.txt
--- a/sacred/sacred_runs/2/metrics.json
+++ b/sacred/sacred_runs/2/metrics.json
@ -1 +0,0 @@
-{}
--- a/sacred/sacred_runs/2/run.json
+++ b/sacred/sacred_runs/2/run.json
@ -1,100 +0,0 @@
-{
-  "artifacts": [
-    "beer_review_sentiment_model.keras"
-  ],
-  "command": "run_experiment",
-  "experiment": {
-    "base_dir": "C:\\Users\\adamw\\REPOS\\ium_464979\\sacred",
-    "dependencies": [
-      "keras==2.12.0",
-      "numpy==1.23.5",
-      "sacred==0.8.5",
-      "scikit-learn==1.2.2"
-    ],
-    "mainfile": "sacred_training_model.py",
-    "name": "464979",
-    "repositories": [
-      {
-        "commit": "490b8cf77306ea482543e03ba29e37b07f689ae1",
-        "dirty": true,
-        "url": "https://git.wmi.amu.edu.pl/s464979/ium_464979.git"
-      }
-    ],
-    "sources": [
-      [
-        "sacred_training_model.py",
-        "_sources\\sacred_training_model_ccb7ce6317e0e291ec9a10a9f4fffffe.py"
-      ]
-    ]
-  },
-  "heartbeat": "2024-06-11T21:08:46.548013",
-  "host": {
-    "ENV": {},
-    "cpu": "Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz",
-    "gpus": {
-      "driver_version": "555.85",
-      "gpus": [
-        {
-          "model": "NVIDIA GeForce GTX 1660 Ti",
-          "persistence_mode": false,
-          "total_memory": 6144
-        }
-      ]
-    },
-    "hostname": "DESKTOP-9SEHQM2",
-    "os": [
-      "Windows",
-      "Windows-10-10.0.19045-SP0"
-    ],
-    "python_version": "3.11.7"
-  },
-  "meta": {
-    "command": "run_experiment",
-    "config_updates": {},
-    "named_configs": [],
-    "options": {
-      "--beat-interval": null,
-      "--capture": null,
-      "--comment": null,
-      "--debug": false,
-      "--enforce_clean": false,
-      "--file_storage": null,
-      "--force": false,
-      "--help": false,
-      "--id": null,
-      "--loglevel": null,
-      "--mongo_db": null,
-      "--name": null,
-      "--pdb": false,
-      "--print-config": false,
-      "--priority": null,
-      "--queue": false,
-      "--s3": null,
-      "--sql": null,
-      "--tiny_db": null,
-      "--unobserved": false,
-      "COMMAND": null,
-      "UPDATE": [],
-      "help": false,
-      "with": false
-    }
-  },
-  "resources": [
-    [
-      "C:\\Users\\adamw\\REPOS\\ium_464979\\sacred\\beer_reviews_train.csv",
-      "sacred_runs\\_resources\\beer_reviews_train_e8dab75a0ec202f56510a0e1f9926ad7.csv"
-    ],
-    [
-      "C:\\Users\\adamw\\REPOS\\ium_464979\\sacred\\beer_reviews_test.csv",
-      "sacred_runs\\_resources\\beer_reviews_test_56070f83bef3ee1d17d1a632aa55b798.csv"
-    ]
-  ],
-  "result": {
-    "dtype": "float64",
-    "py/object": "numpy.float64",
-    "value": 0.9237146778770103
-  },
-  "start_time": "2024-06-11T21:08:35.823687",
-  "status": "COMPLETED",
-  "stop_time": "2024-06-11T21:08:46.547012"
-}
--- a/sacred/sacred_runs/_resources/beer_reviews_test_56070f83bef3ee1d17d1a632aa55b798.csv
+++ b/sacred/sacred_runs/_resources/beer_reviews_test_56070f83bef3ee1d17d1a632aa55b798.csv
--- a/sacred/sacred_runs/_resources/beer_reviews_train_e8dab75a0ec202f56510a0e1f9926ad7.csv
+++ b/sacred/sacred_runs/_resources/beer_reviews_train_e8dab75a0ec202f56510a0e1f9926ad7.csv
--- a/sacred/sacred_runs/_sources/sacred_training_model_2a1e89d7c820c7a00319e1e22827c7f9.py
+++ b/sacred/sacred_runs/_sources/sacred_training_model_2a1e89d7c820c7a00319e1e22827c7f9.py
@ -1,85 +0,0 @@
-import pandas as pd
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Embedding, GlobalAveragePooling1D, Dense
-from tensorflow.keras.models import load_model
-from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
-from sacred import Experiment
-from sacred.observers import MongoObserver, FileStorageObserver
-from math import sqrt
-
-ex = Experiment('464979')
-# ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017'))
-ex.observers.append(FileStorageObserver('sacred_runs'))
-
-@ex.config
-def my_config():
-    epochs = 10
-    batch_size = 32
-
-@ex.automain
-def run_experiment(epochs, batch_size, _run):
-    train_data = pd.read_csv('beer_reviews_train.csv')
-    X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
-    y_train = train_data['review_overall']
-
-    tokenizer = Tokenizer(num_words=10000)
-    tokenizer.fit_on_texts(X_train)
-    X_train_seq = tokenizer.texts_to_sequences(X_train)
-
-    X_train_pad = pad_sequences(X_train_seq, maxlen=100)
-
-    model = Sequential([
-        Embedding(input_dim=10000, output_dim=16, input_length=100),
-        GlobalAveragePooling1D(),
-        Dense(16, activation='relu'),
-        Dense(1, activation='sigmoid')
-    ])
-
-    model.compile(optimizer='adam',
-                  loss='binary_crossentropy',
-                  metrics=['accuracy'])
-
-    model.fit(X_train_pad, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1)
-
-    model.save('beer_review_sentiment_model.keras')
-    _run.add_artifact('beer_review_model.h5')
-
-    test_data = pd.read_csv('beer_reviews_test.csv')
-    X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
-    y_test = test_data['review_overall']
-
-    tokenizer = Tokenizer(num_words=10000)
-    tokenizer.fit_on_texts(X_test)
-
-    X_test_text = X_test.astype(str).agg(' '.join, axis=1)
-    X_test_seq = tokenizer.texts_to_sequences(X_test_text)
-    X_test_pad = pad_sequences(X_test_seq, maxlen=100)
-
-    predictions = model.predict(X_test_pad)
-
-    if len(predictions.shape) > 1:
-        predictions = predictions[:, 0]
-
-    results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
-    results.to_csv('beer_review_sentiment_predictions.csv', index=False)
-
-    y_pred = results['Predictions']
-    y_test = results['Actual']
-    y_test_binary = (y_test >= 3).astype(int)
-
-    accuracy = accuracy_score(y_test_binary, y_pred.round())
-    precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro')
-    rmse = sqrt(mean_squared_error(y_test, y_pred))
-
-    print(f'Accuracy: {accuracy}')
-    print(f'Micro-avg Precision: {precision}')
-    print(f'Micro-avg Recall: {recall}')
-    print(f'F1 Score: {f1}')
-    print(f'RMSE: {rmse}')
-
-    _run.add_resource('./beer_reviews_train.csv')
-    _run.add_resource('./beer_reviews_test.csv')
-
-    return accuracy
--- a/sacred/sacred_runs/_sources/sacred_training_model_ccb7ce6317e0e291ec9a10a9f4fffffe.py
+++ b/sacred/sacred_runs/_sources/sacred_training_model_ccb7ce6317e0e291ec9a10a9f4fffffe.py
@ -1,84 +0,0 @@
-import pandas as pd
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Embedding, GlobalAveragePooling1D, Dense
-from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
-from sacred import Experiment
-from sacred.observers import MongoObserver, FileStorageObserver
-from math import sqrt
-
-ex = Experiment('464979')
-# ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017'))
-ex.observers.append(FileStorageObserver('sacred_runs'))
-
-@ex.config
-def my_config():
-    epochs = 10
-    batch_size = 32
-
-@ex.automain
-def run_experiment(epochs, batch_size, _run):
-    train_data = pd.read_csv('beer_reviews_train.csv')
-    X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
-    y_train = train_data['review_overall']
-
-    tokenizer = Tokenizer(num_words=10000)
-    tokenizer.fit_on_texts(X_train)
-    X_train_seq = tokenizer.texts_to_sequences(X_train)
-
-    X_train_pad = pad_sequences(X_train_seq, maxlen=100)
-
-    model = Sequential([
-        Embedding(input_dim=10000, output_dim=16, input_length=100),
-        GlobalAveragePooling1D(),
-        Dense(16, activation='relu'),
-        Dense(1, activation='sigmoid')
-    ])
-
-    model.compile(optimizer='adam',
-                  loss='binary_crossentropy',
-                  metrics=['accuracy'])
-
-    model.fit(X_train_pad, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1)
-
-    model.save('beer_review_sentiment_model.keras')
-    _run.add_artifact('beer_review_sentiment_model.keras')
-
-    test_data = pd.read_csv('beer_reviews_test.csv')
-    X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
-    y_test = test_data['review_overall']
-
-    tokenizer = Tokenizer(num_words=10000)
-    tokenizer.fit_on_texts(X_test)
-
-    X_test_text = X_test.astype(str).agg(' '.join, axis=1)
-    X_test_seq = tokenizer.texts_to_sequences(X_test_text)
-    X_test_pad = pad_sequences(X_test_seq, maxlen=100)
-
-    predictions = model.predict(X_test_pad)
-
-    if len(predictions.shape) > 1:
-        predictions = predictions[:, 0]
-
-    results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
-    results.to_csv('beer_review_sentiment_predictions.csv', index=False)
-
-    y_pred = results['Predictions']
-    y_test = results['Actual']
-    y_test_binary = (y_test >= 3).astype(int)
-
-    accuracy = accuracy_score(y_test_binary, y_pred.round())
-    precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro')
-    rmse = sqrt(mean_squared_error(y_test, y_pred))
-
-    print(f'Accuracy: {accuracy}')
-    print(f'Micro-avg Precision: {precision}')
-    print(f'Micro-avg Recall: {recall}')
-    print(f'F1 Score: {f1}')
-    print(f'RMSE: {rmse}')
-
-    _run.add_resource('./beer_reviews_train.csv')
-    _run.add_resource('./beer_reviews_test.csv')
-
-    return accuracy
--- a/sacred/sacred_training_model.py
+++ b/sacred/sacred_training_model.py
@ -1,84 +0,0 @@
-import pandas as pd
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Embedding, GlobalAveragePooling1D, Dense
-from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
-from sacred import Experiment
-from sacred.observers import MongoObserver, FileStorageObserver
-from math import sqrt
-
-ex = Experiment('464979')
-ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017'))
-ex.observers.append(FileStorageObserver('sacred_runs'))
-
-@ex.config
-def my_config():
-    epochs = 10
-    batch_size = 32
-
-@ex.automain
-def run_experiment(epochs, batch_size, _run):
-    train_data = pd.read_csv('beer_reviews_train.csv')
-    X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
-    y_train = train_data['review_overall']
-
-    tokenizer = Tokenizer(num_words=10000)
-    tokenizer.fit_on_texts(X_train)
-    X_train_seq = tokenizer.texts_to_sequences(X_train)
-
-    X_train_pad = pad_sequences(X_train_seq, maxlen=100)
-
-    model = Sequential([
-        Embedding(input_dim=10000, output_dim=16, input_length=100),
-        GlobalAveragePooling1D(),
-        Dense(16, activation='relu'),
-        Dense(1, activation='sigmoid')
-    ])
-
-    model.compile(optimizer='adam',
-                  loss='binary_crossentropy',
-                  metrics=['accuracy'])
-
-    model.fit(X_train_pad, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1)
-
-    model.save('beer_review_sentiment_model.keras')
-    _run.add_artifact('beer_review_sentiment_model.keras')
-
-    test_data = pd.read_csv('beer_reviews_test.csv')
-    X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
-    y_test = test_data['review_overall']
-
-    tokenizer = Tokenizer(num_words=10000)
-    tokenizer.fit_on_texts(X_test)
-
-    X_test_text = X_test.astype(str).agg(' '.join, axis=1)
-    X_test_seq = tokenizer.texts_to_sequences(X_test_text)
-    X_test_pad = pad_sequences(X_test_seq, maxlen=100)
-
-    predictions = model.predict(X_test_pad)
-
-    if len(predictions.shape) > 1:
-        predictions = predictions[:, 0]
-
-    results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
-    results.to_csv('beer_review_sentiment_predictions.csv', index=False)
-
-    y_pred = results['Predictions']
-    y_test = results['Actual']
-    y_test_binary = (y_test >= 3).astype(int)
-
-    accuracy = accuracy_score(y_test_binary, y_pred.round())
-    precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro')
-    rmse = sqrt(mean_squared_error(y_test, y_pred))
-
-    print(f'Accuracy: {accuracy}')
-    print(f'Micro-avg Precision: {precision}')
-    print(f'Micro-avg Recall: {recall}')
-    print(f'F1 Score: {f1}')
-    print(f'RMSE: {rmse}')
-
-    _run.add_resource('./beer_reviews_train.csv')
-    _run.add_resource('./beer_reviews_test.csv')
-
-    return accuracy
				`@ -1 +0,0 @@`
				`https://git.wmi.amu.edu.pl/s464979/ium_464979.git`
				`@ -1 +0,0 @@`
				`file://C:\Users\adamw\REPOS\ium_464979#\mlflow_project`