From ca6b7b948cbd862c2fa1ab31ec0d1fb771012faf Mon Sep 17 00:00:00 2001 From: meghdadFar Date: Mon, 8 Apr 2024 11:49:20 +0200 Subject: [PATCH] Update docs --- docs/.doctrees/api.doctree | Bin 189287 -> 198397 bytes docs/.doctrees/chat.doctree | Bin 5417 -> 8666 bytes docs/.doctrees/mwes.doctree | Bin 16268 -> 16580 bytes docs/_images/chat_mwe.png | Bin 0 -> 300040 bytes docs/_images/chat_stats.png | Bin 0 -> 235451 bytes docs/_sources/chat.rst.txt | 43 +++++++++++++++++++++++++++++++++--- docs/_sources/mwes.rst.txt | 12 ++++++---- docs/api.html | 29 +++++++++++++++++++----- docs/chat.html | 36 ++++++++++++++++++++++++++++-- docs/genindex.html | 6 ++++- docs/mwes.html | 14 +++++++----- docs/objects.inv | Bin 813 -> 818 bytes docs/searchindex.js | 2 +- 13 files changed, 121 insertions(+), 21 deletions(-) create mode 100644 docs/_images/chat_mwe.png create mode 100644 docs/_images/chat_stats.png diff --git a/docs/.doctrees/api.doctree b/docs/.doctrees/api.doctree index 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b/docs/_sources/chat.rst.txt index 435e2dc..805bcf6 100644 --- a/docs/_sources/chat.rst.txt +++ b/docs/_sources/chat.rst.txt @@ -19,7 +19,7 @@ call the `chat` method to interact with the data and get insights from it via Na from wordview.text_analysis import TextStatsPlots imdb_df = pd.read_csv("data/IMDB_Dataset_sample_5k.csv") - with open("wordview/chat/secrets/openai_api_key.json", "r") as f: + with open("your_secrets_dir/openai_api_key.json", "r") as f: credentials = json.load(f) tsp = TextStatsPlots(df=imdb_df, text_column="review") @@ -27,6 +27,43 @@ call the `chat` method to interact with the data and get insights from it via Na The chat UI is available under http://127.0.0.1:5000/ -|chat| +|chat_stats| -.. |chat| image:: ../figs/chat.png \ No newline at end of file +Chat with MWEs +~~~~~~~~~~~~~~ + +After allowing Wordview to extract MWEs, you can call the `chat` method to get insights from this extraction through Natural Language. + +.. code:: python + + import json + + import pandas as pd + + from wordview.mwe_extraction import MWEs + from wordview.preprocessing import NgramExtractor + + imdb_df = pd.read_csv("data/IMDB_Dataset_sample_5k.csv") + with open("your_secrets_dir/openai_api_key.json", "r") as f: + credentials = json.load(f) + + extractor = NgramExtractor(imdb_df, "review") + extractor.extract_ngrams() + extractor.get_ngram_counts(ngram_count_file_path="ngram_counts.json") + + mwe_obj = MWE(imdb_df, 'review', + ngram_count_file_path='ngram_counts.json', + language='EN', + custom_patterns="NP: {

?*}", + only_custom_patterns=False, + ) + mwe_obj.extract_mwes(sort=True, top_n=10) + mwe_obj.chat(api_key=credentials.get("openai_api_key")) + +The chat UI for MWEs is available under http://127.0.0.1:5001/ + +|chat_mwe| + +.. |chat_stats| image:: ../figs/chat_stats.png + +.. |chat_mwe| image:: ../figs/chat_mwe.png \ No newline at end of file diff --git a/docs/_sources/mwes.rst.txt b/docs/_sources/mwes.rst.txt index 003fec7..d7a0c19 100644 --- a/docs/_sources/mwes.rst.txt +++ b/docs/_sources/mwes.rst.txt @@ -32,16 +32,20 @@ the documentation. custom_patterns="NP: {
?*}", only_custom_patterns=False, ) - mwes = mwe_obj.extract_mwes(sort=True, top_n=10) - json.dump(mwes, open('data/mwes.json', 'w'), indent=4) - + mwe_obj.extract_mwes(sort=True, top_n=10) + json.dump(mwe_obj.mwes, open('data/mwes.json', 'w'), indent=4) -The above returns the results in a dictionary, that in this example we stored in `mwes.json` file. +The above returns the results in a dictionary, that in this example we stored in a json file called `data/mwes.json`. You can also return the result in a table: .. code-block:: python mwe_obj.print_mwe_table() + +Which will return a table like this: + +.. code-block:: text + ╔═════════════════════════╦═══════════════╗ ║ LVC ║ Association ║ ╠═════════════════════════╬═══════════════╣ diff --git a/docs/api.html b/docs/api.html index c00f3f8..b12c894 100644 --- a/docs/api.html +++ b/docs/api.html @@ -96,6 +96,7 @@
  • Extraction & Analysis of Multiword Expressions
    • MWE @@ -404,11 +405,12 @@

      Label Analysis

      -class wordview.mwes.MWE(df: DataFrame, text_column: str, ngram_count_source=None, ngram_count_file_path=None, language: str = 'EN', custom_patterns: str | None = None, only_custom_patterns: bool = False)
      -

      Extract MWEs of type LVC, VPC, Noun Compounds, Adjective Compounds, and custom patterns from a text corpus.

      +class wordview.mwes.MWE(df: DataFrame, text_column: str, ngram_count_source=None, ngram_count_file_path=None, language: str = 'EN', custom_patterns: str | None = None, only_custom_patterns: bool = False, mwe_frequency_threshold: int = 10, association_threshold: float = 1.0)
  • +

    Extract MWEs of typeS: +LVC, VPC, Noun Compounds, Adjective Compounds, and custom patterns from a text corpus.

    -__init__(df: DataFrame, text_column: str, ngram_count_source=None, ngram_count_file_path=None, language: str = 'EN', custom_patterns: str | None = None, only_custom_patterns: bool = False) None
    +__init__(df: DataFrame, text_column: str, ngram_count_source=None, ngram_count_file_path=None, language: str = 'EN', custom_patterns: str | None = None, only_custom_patterns: bool = False, mwe_frequency_threshold: int = 10, association_threshold: float = 1.0) None

    Initializes a new instance of MWE class.

    Parameters:
    @@ -428,16 +430,33 @@

    Extraction & Analysis of Multiword Expressions
  • only_custom_pattern – If True, only the custom pattern will be used to extract MWEs, otherwise, the default patterns will be used as well.

  • +
  • mwe_frequency_threshold – The minimum frequency of an MWE to be considered for extraction. Defaults to 10.

  • +
  • association_threshold – A threshold value for the association measure. Only MWEs with an association measure above this threshold will be returned.

  • Returns – None

  • +
    +
    +chat(api_key: str = '')
    +

    Chat with OpenAI’s latest model about MWEs . +Access the chat UI in your localhost under http://127.0.0.1:5001/

    +
    +
    Parameters:
    +

    api_key – OpenAI API key.

    +
    +
    Returns:
    +

    None

    +
    +
    +
    +
    extract_mwes(sort: bool = True, top_n: int | None = None) dict[str, dict[str, float]]
    -

    Extract MWEs from the text corpus.

    +

    Extract MWEs from the text corpus and add them to self.mwes.

    Parameters:
      @@ -446,7 +465,7 @@

      Extraction & Analysis of Multiword Expressions

    Returns:
    -

    A dictionary containing the MWEs and their association measures.

    +

    None.

    diff --git a/docs/chat.html b/docs/chat.html index be77f02..10b7b2c 100644 --- a/docs/chat.html +++ b/docs/chat.html @@ -60,6 +60,7 @@
  • Cluster Analysis
  • Chat with Wordview
  • @@ -115,7 +116,7 @@

    Chat with TextStatsPlotsfrom wordview.text_analysis import TextStatsPlots imdb_df = pd.read_csv("data/IMDB_Dataset_sample_5k.csv") -with open("wordview/chat/secrets/openai_api_key.json", "r") as f: +with open("your_secrets_dir/openai_api_key.json", "r") as f: credentials = json.load(f) tsp = TextStatsPlots(df=imdb_df, text_column="review") @@ -123,7 +124,38 @@

    Chat with TextStatsPlotshttp://127.0.0.1:5000/

    -

    chat

    +

    chat_stats

    + +
    +

    Chat with MWEs

    +

    After allowing Wordview to extract MWEs, you can call the chat method to get insights from this extraction through Natural Language.

    +
    import json
    +
    +import pandas as pd
    +
    +from wordview.mwe_extraction import MWEs
    +from wordview.preprocessing import NgramExtractor
    +
    +imdb_df = pd.read_csv("data/IMDB_Dataset_sample_5k.csv")
    +with open("your_secrets_dir/openai_api_key.json", "r") as f:
    +   credentials = json.load(f)
    +
    +extractor = NgramExtractor(imdb_df, "review")
    +extractor.extract_ngrams()
    +extractor.get_ngram_counts(ngram_count_file_path="ngram_counts.json")
    +
    +mwe_obj = MWE(imdb_df, 'review',
    +             ngram_count_file_path='ngram_counts.json',
    +             language='EN',
    +             custom_patterns="NP: {<DT>?<JJ>*<NN>}",
    +             only_custom_patterns=False,
    +             )
    + mwe_obj.extract_mwes(sort=True, top_n=10)
    + mwe_obj.chat(api_key=credentials.get("openai_api_key"))
    +
    +
    +

    The chat UI for MWEs is available under http://127.0.0.1:5001/

    +

    chat_mwe

    diff --git a/docs/genindex.html b/docs/genindex.html index 676c116..7da0839 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -153,8 +153,12 @@

    B

    C

    Utilities