<jupyter_start><jupyter_text>Helpseeker_nbdev> A demo of nbdev in the context of a deep dive project.  Install `pip install Helpseeker_nbdev`  How to use Running the TargetValuesPreprocessing script First use the `logging.logging_setup` method to set create a logger.<jupyter_code>logger = logging.logging_setup(log_level='INFO')<jupyter_output><empty_output><jupyter_text>The `get_target_df` function from the `TargetValuesPreprocessing` module returns a dictionary that maps the targets to their respective poly-mapped values.<jupyter_code>mapped_targets = TargetValuesPreprocessing.get_target_df(
       path_config.target_features,
       path_config.target_path,
       path_config.polygon_path,
       path_config.target_df_regions,
       logger)<jupyter_output>{
 "event": "Preprocessing target features",
 "timestamp": "datetime.datetime(2021, 2, 24, 19, 19, 29, 165385)"
}
{
 "event": "mapping homeless polygon",
 "timestamp": "datetime.datetime(2021, 2, 24, 19, 19, 29, 167382)"
}
Failed to auto identify EPSG: 7
{
 "event": "homeless polygon mapping completed",
 "timestamp": "datetime.datetime(2021, 2, 24, 19, 19, 29, 267113)"
}
{
 "event": "mapping suicide polygon",
 "timestamp": "datetime.datetime(2021, 2, 24, 19, 19, 29, 267113)"
}
Failed to auto identify EPSG: 7
{
 "event": "suicide polygon mapping completed",
 "timestamp": "datetime.datetime(2021, 2, 24, 19, 19, 29, 359865)"
}
{
 "event": "mapping violence polygon",
 "timestamp": "datetime.datetime(2021, 2, 24, 19, 19, 29, 360862)"
}
Failed to auto identify EPSG: 7
{
 "event": "violence polygon mapping completed",
 "timestamp": "datetime.datetime(2021, 2, 24, 19, 19, 29, 433868)"
}<jupyter_text>This returns a dictionary of `geo dataframes` with the target values and geometry corresponding to the locations the target values are referencing.<jupyter_code>mapped_targets['homeless'].head()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11801896733403583, 'fraction_numerical': 0.07165437302423604, 'mean_word_length': 6.007380073800738, 'pattern_counts': {'":': 14, '<': 12, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 7, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '598797', 'license': 'Apache-2.0', 'n_tokens_mistral': 720, 'n_tokens_neox': 590, 'n_words': 186, 'path': 'index.ipynb', 'repo_name': 'BYZ007/Helpseeker_nbdev', 'size': '1898'}