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amazonml-2024

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  1. .DS_Store +0 -0
  2. .gitattributes +15 -0
  3. README.md +141 -3
  4. dataset/final_train.csv +3 -0
  5. dataset/final_train.xlsx +3 -0
  6. dataset/ocr_data.csv +3 -0
  7. dataset/ocr_data.xlsx +3 -0
  8. dataset/ocr_test_data-99300.csv +3 -0
  9. dataset/ocr_test_data-99300.xlsx +3 -0
  10. dataset/ocr_test_data.csv +3 -0
  11. dataset/ocr_test_data.xlsx +3 -0
  12. dataset/ocr_test_data_final.csv +3 -0
  13. dataset/ocr_test_data_final.xlsx +3 -0
  14. dataset/output_file.csv +3 -0
  15. dataset/output_file.xlsx +3 -0
  16. dataset/sample_test.csv +89 -0
  17. dataset/sample_test_out.csv +89 -0
  18. dataset/sample_test_out_fail.csv +89 -0
  19. dataset/test.csv +0 -0
  20. dataset/test.xlsx +3 -0
  21. dataset/train.csv +3 -0
  22. dataset/train.xlsx +3 -0
  23. sample_code.py +21 -0
  24. src/.DS_Store +0 -0
  25. src/__pycache__/constants.cpython-310.pyc +0 -0
  26. src/__pycache__/utils.cpython-310.pyc +0 -0
  27. src/constants.py +36 -0
  28. src/sanity.py +51 -0
  29. src/test.ipynb +180 -0
  30. src/utils.py +83 -0
.DS_Store ADDED
Binary file (6.15 kB). View file
 
.gitattributes CHANGED
@@ -57,3 +57,18 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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+ dataset/final_train.csv filter=lfs diff=lfs merge=lfs -text
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+ dataset/final_train.xlsx filter=lfs diff=lfs merge=lfs -text
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+ dataset/ocr_data.csv filter=lfs diff=lfs merge=lfs -text
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+ dataset/ocr_data.xlsx filter=lfs diff=lfs merge=lfs -text
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+ dataset/ocr_test_data_final.csv filter=lfs diff=lfs merge=lfs -text
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+ dataset/ocr_test_data_final.xlsx filter=lfs diff=lfs merge=lfs -text
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+ dataset/ocr_test_data-99300.csv filter=lfs diff=lfs merge=lfs -text
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+ dataset/ocr_test_data-99300.xlsx filter=lfs diff=lfs merge=lfs -text
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+ dataset/ocr_test_data.csv filter=lfs diff=lfs merge=lfs -text
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+ dataset/ocr_test_data.xlsx filter=lfs diff=lfs merge=lfs -text
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+ dataset/output_file.csv filter=lfs diff=lfs merge=lfs -text
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+ dataset/output_file.xlsx filter=lfs diff=lfs merge=lfs -text
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+ dataset/test.xlsx filter=lfs diff=lfs merge=lfs -text
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+ dataset/train.csv filter=lfs diff=lfs merge=lfs -text
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+ dataset/train.xlsx filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,3 +1,141 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # ML Challenge Problem Statement
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+
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+ ## Feature Extraction from Images
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+
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+ In this hackathon, the goal is to create a machine learning model that extracts entity values from images. This capability is crucial in fields like healthcare, e-commerce, and content moderation, where precise product information is vital. As digital marketplaces expand, many products lack detailed textual descriptions, making it essential to obtain key details directly from images. These images provide important information such as weight, volume, voltage, wattage, dimensions, and many more, which are critical for digital stores.
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+
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+ ### Data Description:
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+
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+ The dataset consists of the following columns:
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+
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+ 1. **index:** An unique identifier (ID) for the data sample
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+ 2. **image_link**: Public URL where the product image is available for download. Example link - https://m.media-amazon.com/images/I/71XfHPR36-L.jpg
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+ To download images use `download_images` function from `src/utils.py`. See sample code in `src/test.ipynb`.
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+ 3. **group_id**: Category code of the product
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+ 4. **entity_name:** Product entity name. For eg: “item_weight”
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+ 5. **entity_value:** Product entity value. For eg: “34 gram”
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+ Note: For test.csv, you will not see the column `entity_value` as it is the target variable.
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+
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+ ### Output Format:
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+
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+ The output file should be a csv with 2 columns:
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+
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+ 1. **index:** The unique identifier (ID) of the data sample. Note the index should match the test record index.
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+ 2. **prediction:** A string which should have the following format: “x unit” where x is a float number in standard formatting and unit is one of the allowed units (allowed units are mentioned in the Appendix). The two values should be concatenated and have a space between them. For eg: “2 gram”, “12.5 centimetre”, “2.56 ounce” are valid. Few invalid cases: “2 gms”, “60 ounce/1.7 kilogram”, “2.2e2 kilogram” etc.
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+ Note: Make sure to output a prediction for all indices. If no value is found in the image for any test sample, return empty string, i.e, `“”`. If you have less/more number of output samples in the output file as compared to test.csv, your output won’t be evaluated.
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+
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+ ### File Descriptions:
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+
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+ *source files*
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+
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+ 1. **src/sanity.py**: Sanity checker to ensure that the final output file passes all formatting checks. Note: the script will not check if less/more number of predictions are present compared to the test file. See sample code in `src/test.ipynb`
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+ 2. **src/utils.py**: Contains helper functions for downloading images from the image_link.
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+ 3. **src/constants.py:** Contains the allowed units for each entity type.
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+ 4. **sample_code.py:** We also provided a sample dummy code that can generate an output file in the given format. Usage of this file is optional.
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+
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+ *Dataset files*
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+
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+ 1. **dataset/train.csv**: Training file with labels (`entity_value`).
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+ 2. **dataset/test.csv**: Test file without output labels (`entity_value`). Generate predictions using your model/solution on this file's data and format the output file to match sample_test_out.csv (Refer the above section "Output Format")
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+ 3. **dataset/sample_test.csv**: Sample test input file.
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+ 4. **dataset/sample_test_out.csv**: Sample outputs for sample_test.csv. The output for test.csv must be formatted in the exact same way. Note: The predictions in the file might not be correct
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+
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+ ### Constraints
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+
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+ 1. You will be provided with a sample output file and a sanity checker file. Format your output to match the sample output file exactly and pass it through the sanity checker to ensure its validity. Note: If the file does not pass through the sanity checker, it will not be evaluated. You should recieve a message like `Parsing successfull for file: ...csv` if the output file is correctly formatted.
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+
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+ 2. You are given the list of allowed units in constants.py and also in Appendix. Your outputs must be in these units. Predictions using any other units will be considered invalid during validation.
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+
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+ ### Evaluation Criteria
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+
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+ Submissions will be evaluated based on F1 score, which are standard measures of prediction accuracy for classification and extraction problems.
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+
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+ Let GT = Ground truth value for a sample and OUT be output prediction from the model for a sample. Then we classify the predictions into one of the 4 classes with the following logic:
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+
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+ 1. *True Positives* - If OUT != `""` and GT != `""` and OUT == GT
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+ 2. *False Positives* - If OUT != `""` and GT != `""` and OUT != GT
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+ 3. *False Positives* - If OUT != `""` and GT == `""`
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+ 4. *False Negatives* - If OUT == `""` and GT != `""`
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+ 5. *True Negatives* - If OUT == `""` and GT == `""`
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+
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+ Then, F1 score = 2*Precision*Recall/(Precision + Recall) where:
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+
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+ 1. Precision = True Positives/(True Positives + False Positives)
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+ 2. Recall = True Positives/(True Positives + False Negatives)
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+
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+ ### Submission File
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+
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+ Upload a test_out.csv file in the Portal with the exact same formatting as sample_test_out.csv
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+
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+ ### Appendix
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+
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+ ```
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+ entity_unit_map = {
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+ "width": {
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+ "centimetre",
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+ "foot",
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+ "millimetre",
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+ "metre",
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+ "inch",
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+ "yard"
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+ },
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+ "depth": {
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+ "centimetre",
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+ "foot",
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+ "millimetre",
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+ "metre",
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+ "inch",
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+ "yard"
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+ },
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+ "height": {
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+ "centimetre",
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+ "foot",
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+ "millimetre",
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+ "metre",
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+ "inch",
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+ "yard"
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+ },
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+ "item_weight": {
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+ "milligram",
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+ "kilogram",
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+ "microgram",
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+ "gram",
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+ "ounce",
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+ "ton",
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+ "pound"
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+ },
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+ "maximum_weight_recommendation": {
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+ "milligram",
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+ "kilogram",
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+ "microgram",
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+ "gram",
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+ "ounce",
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+ "ton",
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+ "pound"
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+ },
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+ "voltage": {
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+ "millivolt",
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+ "kilovolt",
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+ "volt"
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+ },
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+ "wattage": {
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+ "kilowatt",
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+ "watt"
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+ },
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+ "item_volume": {
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+ "cubic foot",
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+ "microlitre",
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+ "cup",
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+ "fluid ounce",
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+ "centilitre",
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+ "imperial gallon",
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+ "pint",
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+ "decilitre",
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+ "litre",
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+ "millilitre",
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+ "quart",
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+ "cubic inch",
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+ "gallon"
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+ }
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+ }
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+ ```
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+ 77,https://m.media-amazon.com/images/I/71eCfiIG-AL.jpg,275506,item_weight
80
+ 78,https://m.media-amazon.com/images/I/71fWddA0+yL.jpg,318770,wattage
81
+ 79,https://m.media-amazon.com/images/I/71ta6wY3HtL.jpg,983323,voltage
82
+ 80,https://m.media-amazon.com/images/I/71ta6wY3HtL.jpg,983323,wattage
83
+ 81,https://m.media-amazon.com/images/I/71v+pim0lfL.jpg,529489,item_weight
84
+ 82,https://m.media-amazon.com/images/I/71v+pim0lfL.jpg,529489,maximum_weight_recommendation
85
+ 83,https://m.media-amazon.com/images/I/81IYdOV0mVL.jpg,721522,maximum_weight_recommendation
86
+ 84,https://m.media-amazon.com/images/I/81PG3ea0MOL.jpg,240413,voltage
87
+ 85,https://m.media-amazon.com/images/I/81aZ2ozp1GL.jpg,805279,maximum_weight_recommendation
88
+ 86,https://m.media-amazon.com/images/I/81qUmRUUTTL.jpg,603688,maximum_weight_recommendation
89
+ 87,https://m.media-amazon.com/images/I/81qUmRUUTTL.jpg,603688,item_weight
dataset/sample_test_out.csv ADDED
@@ -0,0 +1,89 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ index,prediction
2
+ 0,21.9 foot
3
+ 1,10 foot
4
+ 2,
5
+ 3,289.52 kilovolt
6
+ 4,1078.99 kilowatt
7
+ 5,58.21 ton
8
+ 6,10 volt
9
+ 7,34.57 watt
10
+ 8,
11
+ 9,20 volt
12
+ 10,
13
+ 11,4.95 millimetre
14
+ 12,
15
+ 13,50 centimetre
16
+ 14,6.75 pound
17
+ 15,
18
+ 16,40 inch
19
+ 17,
20
+ 18,6.2 inch
21
+ 19,9 metre
22
+ 20,10 foot
23
+ 21,10 millimetre
24
+ 22,10 inch
25
+ 23,
26
+ 24,11.94 kilovolt
27
+ 25,10 kilowatt
28
+ 26,70 centimetre
29
+ 27,10 inch
30
+ 28,10 yard
31
+ 29,10 millimetre
32
+ 30,6.37 inch
33
+ 31,
34
+ 32,10 millimetre
35
+ 33,73 centimetre
36
+ 34,107.78 kilogram
37
+ 35,10 kilogram
38
+ 36,
39
+ 37,10.54 inch
40
+ 38,
41
+ 39,
42
+ 40,
43
+ 41,4.5 centimetre
44
+ 42,163.43 kilogram
45
+ 43,10 volt
46
+ 44,
47
+ 45,108.57 pound
48
+ 46,10 metre
49
+ 47,
50
+ 48,10 pound
51
+ 49,45 watt
52
+ 50,10 kilovolt
53
+ 51,
54
+ 52,90 centimetre
55
+ 53,
56
+ 54,
57
+ 55,13.51 inch
58
+ 56,109.13 millivolt
59
+ 57,361.82 watt
60
+ 58,
61
+ 59,10 inch
62
+ 60,7 inch
63
+ 61,10 yard
64
+ 62,50 centimetre
65
+ 63,156 gram
66
+ 64,10 ton
67
+ 65,
68
+ 66,10 pound
69
+ 67,613.86 kilowatt
70
+ 68,10 pound
71
+ 69,
72
+ 70,246.23 ton
73
+ 71,10 volt
74
+ 72,
75
+ 73,10 volt
76
+ 74,110 watt
77
+ 75,10 pound
78
+ 76,1.34 microgram
79
+ 77,
80
+ 78,60 watt
81
+ 79,12 volt
82
+ 80,65.33 kilowatt
83
+ 81,4.29 pound
84
+ 82,300 kilogram
85
+ 83,
86
+ 84,
87
+ 85,500 pound
88
+ 86,354.58 pound
89
+ 87,
dataset/sample_test_out_fail.csv ADDED
@@ -0,0 +1,89 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ index,prediction
2
+ 0,21.9 foot
3
+ 1,10 foot
4
+ 2,
5
+ 3,289.52 kilovolt
6
+ 4,1078.99 kilowatt
7
+ 5,58.21 ton
8
+ 6,10 volt
9
+ 7,34.57 watt
10
+ 8,
11
+ 9,20 volt
12
+ 10,
13
+ 11,4.95 millimetre
14
+ 12,
15
+ 13,50 centimetre
16
+ 14,6.75 lbs
17
+ 15,
18
+ 16,40 inch
19
+ 17,
20
+ 18,6.2 inch
21
+ 19,9 metre
22
+ 20,10 foot
23
+ 21,10 millimetre
24
+ 22,10 inch
25
+ 23,
26
+ 24,11.94 kilovolt
27
+ 25,10 kilowatt
28
+ 26,70 centimetre
29
+ 27,10 inch
30
+ 28,10 yard
31
+ 29,10 millimetre
32
+ 30,6.37 inch
33
+ 31,
34
+ 32,10 millimetre
35
+ 33,73 centimetre
36
+ 34,107.78 kilogram
37
+ 35,10 kilogram
38
+ 36,
39
+ 37,10.54 inch
40
+ 38,
41
+ 39,
42
+ 40,
43
+ 41,4.5 centimetre
44
+ 42,163.43 kilogram
45
+ 43,10 volt
46
+ 44,
47
+ 45,108.57 pound
48
+ 46,10 metre
49
+ 47,
50
+ 48,10 pound
51
+ 49,45 watt
52
+ 50,10 kilovolt
53
+ 51,
54
+ 52,90 centimetre
55
+ 53,
56
+ 54,
57
+ 55,13.51 inch
58
+ 56,109.13 millivolt
59
+ 57,361.82 watt
60
+ 58,
61
+ 59,10 inch
62
+ 60,7 inch
63
+ 61,10 yard
64
+ 62,50 centimetre
65
+ 63,156 gram
66
+ 64,10 ton
67
+ 65,
68
+ 66,10 pound
69
+ 67,613.86 kilowatt
70
+ 68,10 pound
71
+ 69,
72
+ 70,246.23 ton
73
+ 71,10 volt
74
+ 72,
75
+ 73,10 volt
76
+ 74,110 watt
77
+ 75,10 pound
78
+ 76,1.34 microgram
79
+ 77,
80
+ 78,60 watt
81
+ 79,12 volt
82
+ 80,65.33 kilowatt
83
+ 81,4.29 pound
84
+ 82,300 kilogram
85
+ 83,
86
+ 84,
87
+ 85,500 pound
88
+ 86,354.58 pound
89
+ 87,
dataset/test.csv ADDED
The diff for this file is too large to render. See raw diff
 
dataset/test.xlsx ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0e5f31f59da028ca7257b2164d478b80c8d1e732e5ae63b1cbc3d6081dceff62
3
+ size 5635963
dataset/train.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e93cc3b4e06308d486fa707ed69a8b476cc3f8214bc859a7dc3d3c99af762f43
3
+ size 21290769
dataset/train.xlsx ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4f6441b6be606cca90823b0675056d185aacca86fc1d48ac73ad5499767c91ed
3
+ size 13024768
sample_code.py ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import random
3
+ import pandas as pd
4
+
5
+ def predictor(image_link, category_id, entity_name):
6
+ '''
7
+ Call your model/approach here
8
+ '''
9
+ #TODO
10
+ return "" if random.random() > 0.5 else "10 inch"
11
+
12
+ if __name__ == "__main__":
13
+ DATASET_FOLDER = '../dataset/'
14
+
15
+ test = pd.read_csv(os.path.join(DATASET_FOLDER, 'test.csv'))
16
+
17
+ test['prediction'] = test.apply(
18
+ lambda row: predictor(row['image_link'], row['group_id'], row['entity_name']), axis=1)
19
+
20
+ output_filename = os.path.join(DATASET_FOLDER, 'test_out.csv')
21
+ test[['index', 'prediction']].to_csv(output_filename, index=False)
src/.DS_Store ADDED
Binary file (6.15 kB). View file
 
src/__pycache__/constants.cpython-310.pyc ADDED
Binary file (809 Bytes). View file
 
src/__pycache__/utils.cpython-310.pyc ADDED
Binary file (2.52 kB). View file
 
src/constants.py ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ entity_unit_map = {
2
+ 'width': {'centimetre', 'foot', 'inch', 'metre', 'millimetre', 'yard'},
3
+ 'depth': {'centimetre', 'foot', 'inch', 'metre', 'millimetre', 'yard'},
4
+ 'height': {'centimetre', 'foot', 'inch', 'metre', 'millimetre', 'yard'},
5
+ 'item_weight': {'gram',
6
+ 'kilogram',
7
+ 'microgram',
8
+ 'milligram',
9
+ 'ounce',
10
+ 'pound',
11
+ 'ton'},
12
+ 'maximum_weight_recommendation': {'gram',
13
+ 'kilogram',
14
+ 'microgram',
15
+ 'milligram',
16
+ 'ounce',
17
+ 'pound',
18
+ 'ton'},
19
+ 'voltage': {'kilovolt', 'millivolt', 'volt'},
20
+ 'wattage': {'kilowatt', 'watt'},
21
+ 'item_volume': {'centilitre',
22
+ 'cubic foot',
23
+ 'cubic inch',
24
+ 'cup',
25
+ 'decilitre',
26
+ 'fluid ounce',
27
+ 'gallon',
28
+ 'imperial gallon',
29
+ 'litre',
30
+ 'microlitre',
31
+ 'millilitre',
32
+ 'pint',
33
+ 'quart'}
34
+ }
35
+
36
+ allowed_units = {unit for entity in entity_unit_map for unit in entity_unit_map[entity]}
src/sanity.py ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pandas as pd
2
+ import argparse
3
+ import re
4
+ import os
5
+ import constants
6
+ from utils import parse_string
7
+
8
+ def check_file(filename):
9
+ if not filename.lower().endswith('.csv'):
10
+ raise ValueError("Only CSV files are allowed.")
11
+ if not os.path.exists(filename):
12
+ raise FileNotFoundError("Filepath: {} invalid or not found.".format(filename))
13
+
14
+ def sanity_check(test_filename, output_filename):
15
+ check_file(test_filename)
16
+ check_file(output_filename)
17
+
18
+ try:
19
+ test_df = pd.read_csv(test_filename)
20
+ output_df = pd.read_csv(output_filename)
21
+ except Exception as e:
22
+ raise ValueError(f"Error reading the CSV files: {e}")
23
+
24
+ if 'index' not in test_df.columns:
25
+ raise ValueError("Test CSV file must contain the 'index' column.")
26
+
27
+ if 'index' not in output_df.columns or 'prediction' not in output_df.columns:
28
+ raise ValueError("Output CSV file must contain 'index' and 'prediction' columns.")
29
+
30
+ missing_index = set(test_df['index']).difference(set(output_df['index']))
31
+ if len(missing_index) != 0:
32
+ print("Missing index in test file: {}".format(missing_index))
33
+
34
+ extra_index = set(output_df['index']).difference(set(test_df['index']))
35
+ if len(extra_index) != 0:
36
+ print("Extra index in test file: {}".format(extra_index))
37
+
38
+ output_df.apply(lambda x: parse_string(x['prediction']), axis=1)
39
+ print("Parsing successfull for file: {}".format(output_filename))
40
+
41
+ if __name__ == "__main__":
42
+ #Usage example: python sanity.py --test_filename sample_test.csv --output_filename sample_test_out.csv
43
+
44
+ parser = argparse.ArgumentParser(description="Run sanity check on a CSV file.")
45
+ parser.add_argument("--test_filename", type=str, required=True, help="The test CSV file name.")
46
+ parser.add_argument("--output_filename", type=str, required=True, help="The output CSV file name to check.")
47
+ args = parser.parse_args()
48
+ try:
49
+ sanity_check(args.test_filename, args.output_filename)
50
+ except Exception as e:
51
+ print('Error:', e)
src/test.ipynb ADDED
@@ -0,0 +1,180 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "id": "1b70b34e",
6
+ "metadata": {},
7
+ "source": [
8
+ "### Basic library imports"
9
+ ]
10
+ },
11
+ {
12
+ "cell_type": "code",
13
+ "execution_count": 9,
14
+ "id": "719d15af",
15
+ "metadata": {},
16
+ "outputs": [],
17
+ "source": [
18
+ "import os\n",
19
+ "import pandas as pd"
20
+ ]
21
+ },
22
+ {
23
+ "cell_type": "markdown",
24
+ "id": "b8911e33",
25
+ "metadata": {},
26
+ "source": [
27
+ "### Read Dataset"
28
+ ]
29
+ },
30
+ {
31
+ "cell_type": "code",
32
+ "execution_count": 10,
33
+ "id": "d3136aac",
34
+ "metadata": {},
35
+ "outputs": [],
36
+ "source": [
37
+ "DATASET_FOLDER = '../dataset/'\n",
38
+ "train = pd.read_csv(os.path.join(DATASET_FOLDER, 'train.csv'))\n",
39
+ "test = pd.read_csv(os.path.join(DATASET_FOLDER, 'test.csv'))\n",
40
+ "sample_test = pd.read_csv(os.path.join(DATASET_FOLDER, 'sample_test.csv'))\n",
41
+ "sample_test_out = pd.read_csv(os.path.join(DATASET_FOLDER, 'sample_test_out.csv'))"
42
+ ]
43
+ },
44
+ {
45
+ "cell_type": "markdown",
46
+ "id": "60ebd689",
47
+ "metadata": {},
48
+ "source": [
49
+ "### Run Sanity check using src/sanity.py"
50
+ ]
51
+ },
52
+ {
53
+ "cell_type": "code",
54
+ "execution_count": 11,
55
+ "id": "81bb3988",
56
+ "metadata": {},
57
+ "outputs": [
58
+ {
59
+ "name": "stdout",
60
+ "output_type": "stream",
61
+ "text": [
62
+ "Parsing successfull for file: ../dataset/sample_test_out.csv\r\n"
63
+ ]
64
+ }
65
+ ],
66
+ "source": [
67
+ "!python sanity.py --test_filename ../dataset/sample_test.csv --output_filename ../dataset/sample_test_out.csv"
68
+ ]
69
+ },
70
+ {
71
+ "cell_type": "code",
72
+ "execution_count": 12,
73
+ "id": "5aa79459",
74
+ "metadata": {},
75
+ "outputs": [
76
+ {
77
+ "name": "stdout",
78
+ "output_type": "stream",
79
+ "text": [
80
+ "Error: Invalid unit [lbs] found in 6.75 lbs. Allowed units: {'watt', 'litre', 'kilogram', 'gallon', 'millilitre', 'microgram', 'millimetre', 'microlitre', 'kilowatt', 'imperial gallon', 'foot', 'kilovolt', 'millivolt', 'quart', 'inch', 'centimetre', 'yard', 'decilitre', 'ton', 'metre', 'pound', 'fluid ounce', 'cup', 'pint', 'volt', 'centilitre', 'ounce', 'gram', 'cubic inch', 'milligram', 'cubic foot'}\r\n"
81
+ ]
82
+ }
83
+ ],
84
+ "source": [
85
+ "!python sanity.py --test_filename ../dataset/sample_test.csv --output_filename ../dataset/sample_test_out_fail.csv"
86
+ ]
87
+ },
88
+ {
89
+ "cell_type": "markdown",
90
+ "id": "dbe930a8",
91
+ "metadata": {},
92
+ "source": [
93
+ "### Download images"
94
+ ]
95
+ },
96
+ {
97
+ "cell_type": "code",
98
+ "execution_count": 13,
99
+ "id": "a3d1aad8",
100
+ "metadata": {},
101
+ "outputs": [
102
+ {
103
+ "name": "stdout",
104
+ "output_type": "stream",
105
+ "text": [
106
+ "#images to download: 54\n",
107
+ "Download started... using 64 threads\n"
108
+ ]
109
+ },
110
+ {
111
+ "name": "stderr",
112
+ "output_type": "stream",
113
+ "text": [
114
+ "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(54 of 54)\u001b[39m |########################| Elapsed Time: 0:00:00 ETA: 00:00:00\n"
115
+ ]
116
+ },
117
+ {
118
+ "name": "stdout",
119
+ "output_type": "stream",
120
+ "text": [
121
+ "... Download completed, Elapsed Time: 0.9588 seconds\n"
122
+ ]
123
+ }
124
+ ],
125
+ "source": [
126
+ "from utils import download_images\n",
127
+ "download_images(sample_test['image_link'], '../images')"
128
+ ]
129
+ },
130
+ {
131
+ "cell_type": "code",
132
+ "execution_count": 14,
133
+ "id": "89aaba53",
134
+ "metadata": {},
135
+ "outputs": [],
136
+ "source": [
137
+ "assert len(os.listdir('../images')) > 0"
138
+ ]
139
+ },
140
+ {
141
+ "cell_type": "code",
142
+ "execution_count": 15,
143
+ "id": "1ba3d802",
144
+ "metadata": {},
145
+ "outputs": [],
146
+ "source": [
147
+ "rm -rf ../images"
148
+ ]
149
+ },
150
+ {
151
+ "cell_type": "code",
152
+ "execution_count": null,
153
+ "id": "6c38a641",
154
+ "metadata": {},
155
+ "outputs": [],
156
+ "source": []
157
+ }
158
+ ],
159
+ "metadata": {
160
+ "kernelspec": {
161
+ "display_name": "conda_python3",
162
+ "language": "python",
163
+ "name": "conda_python3"
164
+ },
165
+ "language_info": {
166
+ "codemirror_mode": {
167
+ "name": "ipython",
168
+ "version": 3
169
+ },
170
+ "file_extension": ".py",
171
+ "mimetype": "text/x-python",
172
+ "name": "python",
173
+ "nbconvert_exporter": "python",
174
+ "pygments_lexer": "ipython3",
175
+ "version": "3.10.14"
176
+ }
177
+ },
178
+ "nbformat": 4,
179
+ "nbformat_minor": 5
180
+ }
src/utils.py ADDED
@@ -0,0 +1,83 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import re
2
+ import constants
3
+ import os
4
+ import requests
5
+ import pandas as pd
6
+ import multiprocessing
7
+ import time
8
+ from time import time as timer
9
+ from tqdm import tqdm
10
+ import numpy as np
11
+ from pathlib import Path
12
+ from functools import partial
13
+ import requests
14
+ import urllib
15
+ from PIL import Image
16
+
17
+ def common_mistake(unit):
18
+ if unit in constants.allowed_units:
19
+ return unit
20
+ if unit.replace('ter', 'tre') in constants.allowed_units:
21
+ return unit.replace('ter', 'tre')
22
+ if unit.replace('feet', 'foot') in constants.allowed_units:
23
+ return unit.replace('feet', 'foot')
24
+ return unit
25
+
26
+ def parse_string(s):
27
+ s_stripped = "" if s==None or str(s)=='nan' else s.strip()
28
+ if s_stripped == "":
29
+ return None, None
30
+ pattern = re.compile(r'^-?\d+(\.\d+)?\s+[a-zA-Z\s]+$')
31
+ if not pattern.match(s_stripped):
32
+ raise ValueError("Invalid format in {}".format(s))
33
+ parts = s_stripped.split(maxsplit=1)
34
+ number = float(parts[0])
35
+ unit = common_mistake(parts[1])
36
+ if unit not in constants.allowed_units:
37
+ raise ValueError("Invalid unit [{}] found in {}. Allowed units: {}".format(
38
+ unit, s, constants.allowed_units))
39
+ return number, unit
40
+
41
+
42
+ def create_placeholder_image(image_save_path):
43
+ try:
44
+ placeholder_image = Image.new('RGB', (100, 100), color='black')
45
+ placeholder_image.save(image_save_path)
46
+ except Exception as e:
47
+ return
48
+
49
+ def download_image(image_link, save_folder, retries=3, delay=3):
50
+ if not isinstance(image_link, str):
51
+ return
52
+
53
+ filename = Path(image_link).name
54
+ image_save_path = os.path.join(save_folder, filename)
55
+
56
+ if os.path.exists(image_save_path):
57
+ return
58
+
59
+ for _ in range(retries):
60
+ try:
61
+ urllib.request.urlretrieve(image_link, image_save_path)
62
+ return
63
+ except:
64
+ time.sleep(delay)
65
+
66
+ create_placeholder_image(image_save_path) #Create a black placeholder image for invalid links/images
67
+
68
+ def download_images(image_links, download_folder, allow_multiprocessing=True):
69
+ if not os.path.exists(download_folder):
70
+ os.makedirs(download_folder)
71
+
72
+ if allow_multiprocessing:
73
+ download_image_partial = partial(
74
+ download_image, save_folder=download_folder, retries=3, delay=3)
75
+
76
+ with multiprocessing.Pool(64) as pool:
77
+ list(tqdm(pool.imap(download_image_partial, image_links), total=len(image_links)))
78
+ pool.close()
79
+ pool.join()
80
+ else:
81
+ for image_link in tqdm(image_links, total=len(image_links)):
82
+ download_image(image_link, save_folder=download_folder, retries=3, delay=3)
83
+