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توضیحات

Statistical association measures for Python pandas

ویژگی	مقدار
سیستم عامل	-
نام فایل	association-measures-0.2.6
نام	association-measures
نسخه کتابخانه	0.2.6
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Philipp Heinrich & Markus Opolka
ایمیل نویسنده	philipp.heinrich@fau.de
آدرس صفحه اصلی	https://github.com/fau-klue/pandas-association-measures
آدرس اینترنتی	https://pypi.org/project/association-measures/
مجوز	MIT

[![Build Status](https://github.com/fau-klue/pandas-association-measures/actions/workflows/python-build.yml/badge.svg)](https://github.com/fau-klue/pandas-association-measures/actions/workflows/python-build.yml) [![PyPI version](https://badge.fury.io/py/association-measures.svg)](https://badge.fury.io/py/association-measures) [![License](https://img.shields.io/pypi/l/association-measures.svg)](https://github.com/fau-klue/association-measures/blob/master/LICENSE) [![Imports: pandas](https://img.shields.io/badge/%20imports-pandas-%231674b1?style=flat&labelColor=gray)](https://pandas.pydata.org/)  # Statistical Association Measures for Python pandas > Association measures are mathematical formulae that interpret cooccurrence frequency data. For each pair of words extracted from a corpus, they compute an association score, a single real value that indicates the amount of (statistical) association between the two words. http://www.collocations.de/AM/index.html # Installation ##### Dependencies - [pandas](https://pandas.pydata.org/) - [scipy](https://scipy.org/) ##### Installation using pip python3 -m pip install association-measures ##### Installation from source (requires Cython) # Compile Cython code python3 setup.py build_ext --inplace # Cython already compiled python3 setup.py install # Usage ## Input The module expects a pandas dataframe with reasonably named columns; i.e. the columns must follow one of the following notations: ### contingency table ```python3 >>> df item O11 O12 O21 O22 1 appreciated 1 15333 1 176663 2 certain 7 15327 113 176551 3 measuring 1 15333 7 176657 4 particularly 2 15332 45 176619 5 arrived 2 15332 3 176661 ``` ### frequency signature (see [Evert 2008: Figure 8](https://www.stephanie-evert.de/PUB/Evert2007HSK_extended_manuscript.pdf)) ```python3 >>> df item f f1 f2 N 1 appreciated 1 15334 2 191998 2 certain 7 15334 120 191998 3 measuring 1 15334 8 191998 4 particularly 2 15334 47 191998 5 arrived 2 15334 5 191998 ``` where `f=O11`, `f1=O11+O12`, `f2=O11+O21`, `N=O11+O12+O21+O22`. ### corpus frequencies (“keyword-friendly”) ```python3 >>> df item f1 N1 f2 N2 1 appreciated 1 15334 1 176664 2 certain 7 15334 113 176664 3 measuring 1 15334 7 176664 4 particularly 2 15334 45 176664 5 arrived 2 15334 3 176664 ``` where `f1=O11`, `f2=O21`, `N1=O11+O12`, `N2=O21+O22`. ## Observed and Expected Frequencies Given a dataframe following one of the notations specified above, you can calculate expected frequencies via ```python3 >>> import association_measures.frequencies as fq >>> fq.expected_frequencies(df) E11 E12 E21 E22 1 0.159731 15333.840269 1.840269 176662.159731 2 9.583850 15324.416150 110.416150 176553.583850 3 0.638923 15333.361077 7.361077 176656.638923 4 3.753675 15330.246325 43.246325 176620.753675 5 0.399327 15333.600673 4.600673 176659.399327 ``` The `observed_frequency` method will convert to contingency notation: ```python3 >>> import association_measures.frequencies as fq >>> fq.observed_frequencies(df) O11 O12 O21 O22 1 1 15333 1 176663 2 7 15327 113 176551 3 1 15333 7 176657 4 2 15332 45 176619 5 2 15332 3 176661 ``` Note that all methods return dataframes that are indexed the same way the input dataframe is indexed: ```python3 >>> df f f1 f2 N item appreciated 1 15334 2 191998 certain 7 15334 120 191998 measuring 1 15334 8 191998 particularly 2 15334 47 191998 arrived 2 15334 5 191998 >>> fq.observed_frequencies(df) O11 O12 O21 O22 item appreciated 1 15333 1 176663 certain 7 15327 113 176551 measuring 1 15333 7 176657 particularly 2 15332 45 176619 arrived 2 15332 3 176661 ``` You can thus `join` the results directly to the input. ## Association Measures The following association measures are currently implemented (v0.2.2): - asymptotic hypothesis tests: - **z-score** (`z_score`) - **t-score** (`t_score`) - parameter: `disc` - **Dunning's log-likelihood ratio** (`log_likelihood`) - parameter: `signed` - **simple-ll** (`simple_ll`) - parameter: `signed` - point estimates of association strength: - **Liddell** (`liddell`) - **minimum sensitivity** (`min_sensitivity`) - [**log-ratio**](http://cass.lancs.ac.uk/log-ratio-an-informal-introduction/) (`log_ratio`) - parameter: `disc` - **Dice coefficient** (`dice`) - information theory: - **mutual information** (`mutual_information`) - parameter: `disc` - **local mutual information** (`local_mutual_information`) - conservative estimates - [**conservative log-ratio**](https://osf.io/cy6mw/) (`conservative_log_ratio`) - parameters: `disc`, `alpha`, `correct`, `one_sided`, `boundary`, `vocab` You can either calculate specific measures: ```python3 >>> import association_measures.measures as am >>> am.log_likelihood(df) item appreciated 2.448757 certain -0.829802 measuring 0.191806 particularly -1.059386 arrived 3.879126 ``` This assumes that `df` contains the necessary columns (observed frequencies in contingency notation and expected frequencies). In most cases, it is most convenient to just use `score()`: ```python3 >>> import association_measures.measures as am >>> am.score(df, measures=['log_likelihood']) O11 O12 O21 O22 R1 R2 C1 C2 N E11 E12 E21 E22 log_likelihood ipm ipm_reference ipm_expected item appreciated 1 15333 1 176663 15334 176664 2 191996 191998 0.159731 15333.840269 1.840269 176662.159731 2.448757 65.214556 5.660463 10.416775 certain 7 15327 113 176551 15334 176664 120 191878 191998 9.583850 15324.416150 110.416150 176553.583850 -0.829802 456.501891 639.632296 625.006510 measuring 1 15333 7 176657 15334 176664 8 191990 191998 0.638923 15333.361077 7.361077 176656.638923 0.191806 65.214556 39.623240 41.667101 particularly 2 15332 45 176619 15334 176664 47 191951 191998 3.753675 15330.246325 43.246325 176620.753675 -1.059386 130.429112 254.720826 244.794217 arrived 2 15332 3 176661 15334 176664 5 191993 191998 0.399327 15333.600673 4.600673 176659.399327 3.879126 130.429112 16.981388 26.041938 ``` Note that by default, `score()` yields observed frequencies in contingency notation (and marginal frequencies) as well as expected frequencies. You can turn off this behaviour setting `freq=False`. To calculate all available measures, don't specify any measures: ```python3 >>> am.score(df, freq=False) z_score t_score log_likelihood simple_ll min_sensitivity liddell dice log_ratio binomial_likelihood conservative_log_ratio mutual_information local_mutual_information item appreciated 2.102442 0.840269 2.448757 1.987992 0.000065 0.420139 0.000130 3.526202 0.000000 0.0 0.796611 0.796611 certain -0.834636 -0.976603 -0.829802 -0.769331 0.000457 -0.021546 0.000906 -0.486622 0.117117 0.0 -0.136442 -0.955094 measuring 0.451726 0.361077 0.191806 0.173788 0.000065 0.045136 0.000130 0.718847 0.000000 0.0 0.194551 0.194551 particularly -0.905150 -1.240035 -1.059386 -0.988997 0.000130 -0.037321 0.000260 -0.965651 0.224042 0.0 -0.273427 -0.546853 arrived 2.533018 1.131847 3.879126 3.243141 0.000130 0.320143 0.000261 2.941240 0.000000 0.0 0.699701 1.399402 ``` You can also pass constant integer counts as parameters to `score()`. This is reasonable for the following notations: - frequency signature: integers `f1` and `N` (DataFrame contains columns `f` and `f2`) ```python3 >>> df f f2 item appreciated 1 2 certain 7 120 measuring 1 8 particularly 2 47 arrived 2 5 >>> am.score(df, f1=15334, N=191998) ``` - corpus frequencies: integers `N1` and `N2` (DataFrame contains columns `f1` and `f2`) ```python3 >>> df f1 f2 item appreciated 1 1 certain 7 113 measuring 1 7 particularly 2 45 arrived 2 3 >>> am.score(df, N1=15334, N2=176664) ``` Some association measures have parameters (see above). You can pass these parameters as keywords to `score()`, e.g.: ```python3 >>> am.score(df, measures=['log_likelihood'], signed=False, freq=False) log_likelihood item appreciated 2.448757 certain 0.829802 measuring 0.191806 particularly 1.059386 arrived 3.879126 ``` # Development The package is tested using pylint and pytest. ```bash # Installing dev requirements make install # Compile Cython code make compile # Lint make lint # Unittest make test # Coverage make coverage # Performance make performance ```

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl association-measures-0.2.6:

pip install association-measures-0.2.6.whl

نصب پکیج tar.gz association-measures-0.2.6:

pip install association-measures-0.2.6.tar.gz