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Runtime error
Runtime error
try fix running in hf.co
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app.py
CHANGED
@@ -61,9 +61,10 @@ def download_dataset():
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@st.cache
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def load_dataset(feature_set: str):
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-
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# read the feature metadata and get a feature set (or all the features)
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with open("
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feature_metadata = json.load(f)
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# features = list(feature_metadata["feature_stats"].keys()) # get all the features
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# features = feature_metadata["feature_sets"]["small"] # get the small
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@@ -75,9 +76,9 @@ def load_dataset(feature_set: str):
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# note: sometimes when trying to read the downloaded data you get an error about invalid magic parquet bytes...
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# if so, delete the file and rerun the napi.download_dataset to fix the
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# corrupted file
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training_data = pd.read_parquet('
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columns=read_columns)
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validation_data = pd.read_parquet('
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columns=read_columns)
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live_data = pd.read_parquet(f'v4/live_{current_round}.parquet',
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columns=read_columns)
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@@ -215,7 +216,7 @@ def get_model_preds(model_name, *params):
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validation_data["prediction"].to_csv(validation_prediction_fname)
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live_data["prediction"].to_csv(f"live_predictions_{current_round}.csv")
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validation_preds = pd.read_parquet('
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validation_data[EXAMPLE_PREDS_COL] = validation_preds["prediction"]
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# get some stats about each of our models to compare...
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@st.cache
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def load_dataset(feature_set: str):
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dataset_path = get_dataset_path()
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print(f'load_dataset with feature_set {feature_set} and path {dataset_path}')
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# read the feature metadata and get a feature set (or all the features)
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with open(f"{dataset_path}/features.json", "r") as f:
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feature_metadata = json.load(f)
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# features = list(feature_metadata["feature_stats"].keys()) # get all the features
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# features = feature_metadata["feature_sets"]["small"] # get the small
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# note: sometimes when trying to read the downloaded data you get an error about invalid magic parquet bytes...
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# if so, delete the file and rerun the napi.download_dataset to fix the
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# corrupted file
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training_data = pd.read_parquet(f'{dataset_path}/train.parquet',
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columns=read_columns)
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validation_data = pd.read_parquet(f'{dataset_path}/validation.parquet',
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columns=read_columns)
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live_data = pd.read_parquet(f'v4/live_{current_round}.parquet',
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columns=read_columns)
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validation_data["prediction"].to_csv(validation_prediction_fname)
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live_data["prediction"].to_csv(f"live_predictions_{current_round}.csv")
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validation_preds = pd.read_parquet(f'{get_dataset_path()}/validation_example_preds.parquet')
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validation_data[EXAMPLE_PREDS_COL] = validation_preds["prediction"]
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# get some stats about each of our models to compare...
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