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Merge pull request #156 from rasbt/improve-bonus-code-ch06
Improve bonus code in chapter 06
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@ -14,7 +14,7 @@ pip install -r requirements-extra.txt
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The codes are using the 50k movie reviews from IMDb ([dataset source](https://ai.stanford.edu/~amaas/data/sentiment/)) to predict whether a movie review is positive or negative.
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Run the following code to create the `train.csv`, `val.csv`, and `test.csv` datasets:
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Run the following code to create the `train.csv`, `validation.csv`, and `test.csv` datasets:
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```bash
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python download-prepare-dataset.py
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@ -19,7 +19,8 @@ def reporthook(count, block_size, total_size):
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duration = time.time() - start_time
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progress_size = int(count * block_size)
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percent = count * block_size * 100 / total_size
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speed = progress_size / (1024**2 * duration)
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speed = int(progress_size / (1024 * duration)) if duration else 0
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sys.stdout.write(
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f"\r{int(percent)}% | {progress_size / (1024**2):.2f} MB "
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f"| {speed:.2f} MB/s | {duration:.2f} sec elapsed"
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@ -32,6 +33,7 @@ def download_and_extract_dataset(dataset_url, target_file, directory):
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if os.path.exists(target_file):
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os.remove(target_file)
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urllib.request.urlretrieve(dataset_url, target_file, reporthook)
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print("\nExtracting dataset ...")
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with tarfile.open(target_file, "r:gz") as tar:
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tar.extractall()
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else:
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@ -74,6 +76,9 @@ def partition_and_save(df, sizes=(35000, 5000, 10000)):
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if __name__ == "__main__":
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dataset_url = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
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print("Downloading dataset ...")
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download_and_extract_dataset(dataset_url, "aclImdb_v1.tar.gz", "aclImdb")
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print("Creating data frames ...")
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df = load_dataset_to_dataframe()
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print("Partitioning and saving data frames ...")
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partition_and_save(df)
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