@@ -7,13 +7,13 @@ This class provides methods for the calculation of different metrics on text. It
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@@ -7,13 +7,13 @@ This class provides methods for the calculation of different metrics on text. It
-[INSTALL.md](INSTALL.md): A guide to make this project work on your local environment.
-[INSTALL.md](INSTALL.md): A guide to make this project work on your local environment.
## ./src/texty
## ./src/texty
-[analyzer.py](src/texty/analyzer.py): This class provides methods for the calculation of different metrics on text.
-[analyzer.py](src/texty/analyzer.py): This module provides a class with methods for the calculation of different metrics on text.
-[complexity.py](src/texty/complexity.py): This class provides methods for the calculation of different complexity metrics on text.
-[complexity.py](src/texty/complexity.py): This module provides a class methods for the calculation of different complexity metrics on text.
-[CREA_total.txt](CREA_total.txt): A dataset of 737799 spanish words ordered by its absolute frequency.
-[CREA_total.txt](CREA_total.txt): A dataset of 737799 spanish words ordered by its absolute frequency.
-[analyze_complexity.py](src/texty/analyze_complexity.py): A script that takes a .txt file and an output format as input and generates a file containing all metrics as calculated by the ComplexityAnalyzer class.
-[analyze_complexity.py](src/texty/analyze_complexity.py): Script that takes a .txt file and an output format as input and generates a file containing all metrics as calculated by the ComplexityAnalyzer class.
## ./examples
## ./examples
-[example_text.txt](examples/example_text.txt): A simple .txt file to test the library.
-[example_text.txt](examples/example_text.txt): Simple .txt file to test the library.
-[example.ipynb](examples/example.ipynb): Colab notebook that shows how to use the ComplexityAnalyzer class.
-[example.ipynb](examples/example.ipynb): Colab notebook that shows how to use the ComplexityAnalyzer class.
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@@ -34,3 +34,11 @@ In this section, we introduce the different metrics offered in this Python libra
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@@ -34,3 +34,11 @@ In this section, we introduce the different metrics offered in this Python libra
***Feature selection**: Remove features with low variance and SelectFromModel (Selection of functions based on L1)
***Feature selection**: Remove features with low variance and SelectFromModel (Selection of functions based on L1)
***kBest**: Selection of the k best features
***kBest**: Selection of the k best features
# Usage
You can run _Texty_ from terminal as follows:
`analyze-complexity {text_file.txt} [-o output_format (csv, tsv or json)]`