Compare commits
16 Commits
evaluation
...
master
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3
.dvc/.gitignore
vendored
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3
.dvc/.gitignore
vendored
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@ -0,0 +1,3 @@
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/config.local
|
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/tmp
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/cache
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4
.dvc/config
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4
.dvc/config
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@ -0,0 +1,4 @@
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[core]
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remote = ium_ssh_remote
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['remote "ium_ssh_remote"']
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url = ssh://ium-sftp@tzietkiewicz.vm.wmi.amu.edu.pl
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3
.dvcignore
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3
.dvcignore
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@ -0,0 +1,3 @@
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# Add patterns of files dvc should ignore, which could improve
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# the performance. Learn more at
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# https://dvc.org/doc/user-guide/dvcignore
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4
.gitignore
vendored
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4
.gitignore
vendored
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@ -0,0 +1,4 @@
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/beer_reviews_train.csv
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/beer_reviews_test.csv
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/beer_review_sentiment_model.h5
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/beer_review_sentiment_predictions.csv
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@ -8,9 +8,9 @@ ARG DEBIAN_FRONTEND=noninteractive
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ENV TZ=Etc/UTC
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RUN apt update && \
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apt install -y python3 python3-pip unzip
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apt install -y python3 python3-pip unzip git
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RUN pip install kaggle pandas seaborn scikit-learn tensorflow
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RUN pip install kaggle pandas seaborn scikit-learn tensorflow sacred pymongo --break-system-packages
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WORKDIR /app
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@ -22,6 +22,7 @@ api.dataset_download_files('thedevastator/1-5-million-beer-reviews-from-beer-adv
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#
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# get_ipython().system('kaggle datasets download -d thedevastator/1-5-million-beer-reviews-from-beer-advocate')
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#
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#Change
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#
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# # In[ ]:
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#
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@ -1,5 +1,6 @@
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import pandas as pd
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import tensorflow as tf
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import sys
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train_data = pd.read_csv('./beer_reviews_train.csv')
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X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
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@ -22,6 +23,6 @@ model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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model.fit(X_train_pad, y_train, epochs=40, batch_size=32, validation_split=0.1)
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model.fit(X_train_pad, y_train, epochs=int(sys.argv[1]), batch_size=int(sys.argv[2]), validation_split=0.1)
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model.save('beer_review_sentiment_model.h5')
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@ -1,18 +1,18 @@
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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test_data = pd.read_csv('./beer_reviews_test.csv')
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X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
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y_test = test_data['review_overall']
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model = tf.keras.models.load_model('beer_review_sentiment_model.h5')
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tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
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predictions = model.predict(X_test)
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print(f'Predictions shape: {predictions.shape}')
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X_test_seq = tokenizer.texts_to_sequences(X_test)
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X_test_pad = tf.keras.preprocessing.sequence.pad_sequences(X_test_seq, maxlen=100)
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if len(predictions.shape) > 1:
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predictions = predictions[:, 0]
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predictions = model.predict(X_test_pad)
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np.savetxt('beer_review_sentiment_predictions.csv', predictions, delimiter=',', fmt='%.10f')
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results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
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results.to_csv('beer_review_sentiment_predictions.csv', index=False)
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@ -1,7 +1,7 @@
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import pandas as pd
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from sklearn.model_selection import train_test_split
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data = pd.read_csv('./beer_reviews.csv')
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data = pd.read_csv('beer_reviews.csv')
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train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
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21
Jenkinsfile
vendored
21
Jenkinsfile
vendored
@ -43,10 +43,29 @@ pipeline {
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steps {
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sh "chmod +x ./IUM_05-model.py"
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sh "chmod +x ./IUM_05-predict.py"
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sh "python3 ./IUM_05-model.py"
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sh "python3 ./IUM_05-model.py 10 32"
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sh "python3 ./IUM_05-predict.py"
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archiveArtifacts artifacts: 'beer_review_sentiment_model.h5,beer_review_sentiment_predictions.csv', onlyIfSuccessful: true
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}
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}
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stage('Sacred') {
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agent {
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dockerfile {
|
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filename 'Dockerfile'
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reuseNode true
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}
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||||
}
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steps {
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sh 'chmod +x sacred/sacred_training_model.py'
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sh 'python3 sacred/sacred_training_model.py'
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}
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}
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stage('Archive Artifacts from Experiments') {
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steps {
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archiveArtifacts artifacts: 'sacred_runs/**/*.*', onlyIfSuccessful: true
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}
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||||
}
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||||
}
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}
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BIN
Przegląd rynku pracy - IUM - Adam Wieczarek.pptx
Normal file
BIN
Przegląd rynku pracy - IUM - Adam Wieczarek.pptx
Normal file
Binary file not shown.
1
data/.gitignore
vendored
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1
data/.gitignore
vendored
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@ -0,0 +1 @@
|
||||
/beer_reviews.csv
|
||||
5
data/beer_reviews.csv.dvc
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5
data/beer_reviews.csv.dvc
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@ -0,0 +1,5 @@
|
||||
outs:
|
||||
- md5: 50f6eec0d0fe78bc0f10e35edd271998
|
||||
size: 201644905
|
||||
hash: md5
|
||||
path: beer_reviews.csv
|
||||
46
dvc.lock
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46
dvc.lock
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@ -0,0 +1,46 @@
|
||||
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
|
||||
23
dvc.yaml
Normal file
23
dvc.yaml
Normal file
@ -0,0 +1,23 @@
|
||||
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
|
||||
338
environment.yml
Normal file
338
environment.yml
Normal file
@ -0,0 +1,338 @@
|
||||
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
|
||||
- ipywidgets=8.1.2=pyhd8ed1ab_0
|
||||
- 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
|
||||
10
mlflow_project/MLproject
Normal file
10
mlflow_project/MLproject
Normal file
@ -0,0 +1,10 @@
|
||||
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}'
|
||||
317324
mlflow_project/beer_review_sentiment_predictions.csv
Normal file
317324
mlflow_project/beer_review_sentiment_predictions.csv
Normal file
File diff suppressed because it is too large
Load Diff
11
mlflow_project/conda.yaml
Normal file
11
mlflow_project/conda.yaml
Normal file
@ -0,0 +1,11 @@
|
||||
name: MLflow_s464979
|
||||
channels:
|
||||
- defaults
|
||||
dependencies:
|
||||
- python=3.10
|
||||
- pip
|
||||
- pip:
|
||||
- mlflow
|
||||
- tensorflow
|
||||
- pandas
|
||||
- scikit-learn
|
||||
53
mlflow_project/mlflow_training_model.py
Normal file
53
mlflow_project/mlflow_training_model.py
Normal file
@ -0,0 +1,53 @@
|
||||
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()
|
||||
15
mlruns/0/2824b8df5d6d414faf28b318d8b870b9/meta.yaml
Normal file
15
mlruns/0/2824b8df5d6d414faf28b318d8b870b9/meta.yaml
Normal file
@ -0,0 +1,15 @@
|
||||
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
|
||||
@ -0,0 +1 @@
|
||||
1715624674602 0.9242538359967604 0
|
||||
@ -0,0 +1 @@
|
||||
64
|
||||
1
mlruns/0/2824b8df5d6d414faf28b318d8b870b9/params/epochs
Normal file
1
mlruns/0/2824b8df5d6d414faf28b318d8b870b9/params/epochs
Normal file
@ -0,0 +1 @@
|
||||
20
|
||||
@ -0,0 +1 @@
|
||||
https://git.wmi.amu.edu.pl/s464979/ium_464979.git
|
||||
@ -0,0 +1 @@
|
||||
local
|
||||
@ -0,0 +1 @@
|
||||
optimal_parameters
|
||||
@ -0,0 +1 @@
|
||||
conda
|
||||
@ -0,0 +1 @@
|
||||
auspicious-pig-388
|
||||
@ -0,0 +1 @@
|
||||
0dbf6f1959cb042149cd568c8b11684f23c68024
|
||||
@ -0,0 +1 @@
|
||||
https://git.wmi.amu.edu.pl/s464979/ium_464979.git
|
||||
@ -0,0 +1 @@
|
||||
file://C:\Users\adamw\REPOS\ium_464979#\mlflow_project
|
||||
@ -0,0 +1 @@
|
||||
PROJECT
|
||||
@ -0,0 +1 @@
|
||||
adamw
|
||||
15
mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/meta.yaml
Normal file
15
mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/meta.yaml
Normal file
@ -0,0 +1,15 @@
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32
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1
mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/params/epochs
Normal file
1
mlruns/0/5cbc975a93e94b8eb27d7dca17d65191/params/epochs
Normal file
@ -0,0 +1 @@
|
||||
40
|
||||
@ -0,0 +1 @@
|
||||
https://git.wmi.amu.edu.pl/s464979/ium_464979.git
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local
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0dbf6f1959cb042149cd568c8b11684f23c68024
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file://C:\Users\adamw\REPOS\ium_464979#\mlflow_project
|
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@ -0,0 +1 @@
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PROJECT
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adamw
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||||
15
mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/meta.yaml
Normal file
15
mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/meta.yaml
Normal file
@ -0,0 +1,15 @@
|
||||
artifact_uri: file:///C:/Users/adamw/REPOS/ium_464979/mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/artifacts
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|
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|
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@ -0,0 +1 @@
|
||||
32
|
||||
1
mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/params/epochs
Normal file
1
mlruns/0/5df6baa9d7c143d7a16314c7721ef48e/params/epochs
Normal file
@ -0,0 +1 @@
|
||||
20
|
||||
@ -0,0 +1 @@
|
||||
https://git.wmi.amu.edu.pl/s464979/ium_464979.git
|
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@ -0,0 +1 @@
|
||||
local
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@ -0,0 +1 @@
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optimal_parameters
|
||||
@ -0,0 +1 @@
|
||||
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|
||||
@ -0,0 +1 @@
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https://git.wmi.amu.edu.pl/s464979/ium_464979.git
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file://C:\Users\adamw\REPOS\ium_464979#\mlflow_project
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||||
@ -0,0 +1 @@
|
||||
PROJECT
|
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@ -0,0 +1 @@
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||||
adamw
|
||||
6
mlruns/0/meta.yaml
Normal file
6
mlruns/0/meta.yaml
Normal file
@ -0,0 +1,6 @@
|
||||
artifact_location: file:///C:/Users/adamw/REPOS/ium_464979/mlruns/0
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||||
creation_time: 1715624503432
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last_update_time: 1715624503432
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||||
lifecycle_stage: active
|
||||
name: Default
|
||||
812346
sacred/beer_reviews.csv
Normal file
812346
sacred/beer_reviews.csv
Normal file
File diff suppressed because it is too large
Load Diff
162470
sacred/beer_reviews_test.csv
Normal file
162470
sacred/beer_reviews_test.csv
Normal file
File diff suppressed because it is too large
Load Diff
649877
sacred/beer_reviews_train.csv
Normal file
649877
sacred/beer_reviews_train.csv
Normal file
File diff suppressed because it is too large
Load Diff
BIN
sacred/sacred_runs/1/beer_review_model.h5
Normal file
BIN
sacred/sacred_runs/1/beer_review_model.h5
Normal file
Binary file not shown.
5
sacred/sacred_runs/1/config.json
Normal file
5
sacred/sacred_runs/1/config.json
Normal file
@ -0,0 +1,5 @@
|
||||
{
|
||||
"batch_size": 32,
|
||||
"epochs": 10,
|
||||
"seed": 373303958
|
||||
}
|
||||
26
sacred/sacred_runs/1/cout.txt
Normal file
26
sacred/sacred_runs/1/cout.txt
Normal file
File diff suppressed because one or more lines are too long
1
sacred/sacred_runs/1/metrics.json
Normal file
1
sacred/sacred_runs/1/metrics.json
Normal file
@ -0,0 +1 @@
|
||||
{}
|
||||
100
sacred/sacred_runs/1/run.json
Normal file
100
sacred/sacred_runs/1/run.json
Normal file
@ -0,0 +1,100 @@
|
||||
{
|
||||
"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",
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||||
"repositories": [
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{
|
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"commit": "e9f53be95453a8da8811653ba3c4a6e75895cd33",
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"dirty": true,
|
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"url": "https://git.wmi.amu.edu.pl/s464979/ium_464979.git"
|
||||
}
|
||||
],
|
||||
"sources": [
|
||||
[
|
||||
"sacred_training_model.py",
|
||||
"_sources\\sacred_training_model_2a1e89d7c820c7a00319e1e22827c7f9.py"
|
||||
]
|
||||
]
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||||
},
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||||
"heartbeat": "2024-06-11T17:21:14.840246",
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||||
"host": {
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||||
"cpu": "Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz",
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||||
"gpus": {
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||||
"driver_version": "555.85",
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"gpus": [
|
||||
{
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||||
"model": "NVIDIA GeForce GTX 1660 Ti",
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"persistence_mode": false,
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"total_memory": 6144
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||||
}
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||||
]
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},
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||||
"hostname": "DESKTOP-9SEHQM2",
|
||||
"os": [
|
||||
"Windows",
|
||||
"Windows-10-10.0.19045-SP0"
|
||||
],
|
||||
"python_version": "3.11.7"
|
||||
},
|
||||
"meta": {
|
||||
"command": "run_experiment",
|
||||
"config_updates": {},
|
||||
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|
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"options": {
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||||
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|
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|
||||
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|
||||
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|
||||
"--mongo_db": null,
|
||||
"--name": null,
|
||||
"--pdb": false,
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||||
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||||
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|
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||||
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"--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"
|
||||
}
|
||||
BIN
sacred/sacred_runs/2/beer_review_sentiment_model.keras
Normal file
BIN
sacred/sacred_runs/2/beer_review_sentiment_model.keras
Normal file
Binary file not shown.
5
sacred/sacred_runs/2/config.json
Normal file
5
sacred/sacred_runs/2/config.json
Normal file
@ -0,0 +1,5 @@
|
||||
{
|
||||
"batch_size": 32,
|
||||
"epochs": 10,
|
||||
"seed": 541882551
|
||||
}
|
||||
26
sacred/sacred_runs/2/cout.txt
Normal file
26
sacred/sacred_runs/2/cout.txt
Normal file
File diff suppressed because one or more lines are too long
1
sacred/sacred_runs/2/metrics.json
Normal file
1
sacred/sacred_runs/2/metrics.json
Normal file
@ -0,0 +1 @@
|
||||
{}
|
||||
100
sacred/sacred_runs/2/run.json
Normal file
100
sacred/sacred_runs/2/run.json
Normal file
@ -0,0 +1,100 @@
|
||||
{
|
||||
"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": {
|
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"--beat-interval": null,
|
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|
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"--comment": null,
|
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"--debug": false,
|
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"--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"
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,85 @@
|
||||
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
|
||||
@ -0,0 +1,84 @@
|
||||
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
|
||||
84
sacred/sacred_training_model.py
Normal file
84
sacred/sacred_training_model.py
Normal file
@ -0,0 +1,84 @@
|
||||
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
|
||||
Loading…
Reference in New Issue
Block a user