maybe lab6
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@ -27,8 +27,8 @@ ENV KAGGLE_KEY=${KAGGLE_KEY}
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WORKDIR /app
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WORKDIR /app
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COPY lab2/download.sh .
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COPY lab2/download.sh .
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COPY lab7/biblioteka_DL/dllib.py .
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COPY lab6/biblioteka_DL/dllib.py .
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COPY lab7/biblioteka_DL/evaluate.py .
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COPY lab6/biblioteka_DL/evaluate.py .
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COPY biblioteka_DL/imdb_top_1000.csv .
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COPY biblioteka_DL/imdb_top_1000.csv .
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COPY predict.py .
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COPY predict.py .
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COPY registry.py .
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COPY registry.py .
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4
dvc.yaml
4
dvc.yaml
@ -1,9 +1,9 @@
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stages:
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stages:
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train:
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train:
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cmd: python3 ./lab7/biblioteka_DL/dllib.py 100
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cmd: python3 ./lab6/biblioteka_DL/dllib.py 100
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deps:
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deps:
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- data/imdb_top_1000.csv
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- data/imdb_top_1000.csv
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evaluate:
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evaluate:
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cmd: python3 ./lab7/biblioteka_DL/evaluate.py
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cmd: python3 ./lab6/biblioteka_DL/evaluate.py
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deps:
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deps:
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- data/imdb_top_1000.csv
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- data/imdb_top_1000.csv
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@ -56,7 +56,7 @@ def normalize_gross(imbd_data):
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def prepare_dataset():
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def prepare_dataset():
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df = pd.read_csv('biblioteka_DL/imdb_top_1000.csv')
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df = pd.read_csv('data/imdb_top_1000.csv')
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df = drop_relevant_columns(df)
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df = drop_relevant_columns(df)
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df_lowercase = lowercase_columns_names(df)
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df_lowercase = lowercase_columns_names(df)
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df = data_to_numeric(df_lowercase)
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df = data_to_numeric(df_lowercase)
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@ -56,7 +56,7 @@ def normalize_gross(imbd_data):
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def prepare_dataset():
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def prepare_dataset():
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df = pd.read_csv('biblioteka_DL/imdb_top_1000.csv')
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df = pd.read_csv('data/imdb_top_1000.csv')
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df = drop_relevant_columns(df)
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df = drop_relevant_columns(df)
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df_lowercase = lowercase_columns_names(df)
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df_lowercase = lowercase_columns_names(df)
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df = data_to_numeric(df_lowercase)
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df = data_to_numeric(df_lowercase)
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