distfit is a python package for probability density fitting across 89 univariate distributions to non-censored data by residual sum of squares (RSS), and hypothesis testing.
Probability density fitting is the fitting of a probability distribution to a series of data concerning the repeated measurement of a variable phenomenon. distfit scores each of the 89 different distributions for the fit wih the empirical distribution and return the best scoring distribution.
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On the documentation pages you can find detailed information about the distfit library with many examples.
pip install bnlearn install git+https://github.com/erdogant/distfit#egg=masterimport distfit
print(distfit.__version__)# Import library
from distfit import distfit
dist = distfit() # Initialize
dist.fit_transform(X) # Fit distributions on empirical data X
dist.predict(y) # Predict the probability of the resonse variables
dist.plot() # Plot the best fitted distribution (y is included if prediction is made)# Prints the screen:
# [distfit] >fit..
# [distfit] >transform..
# [distfit] >[norm ] [RSS: 0.0133619] [loc=-0.059 scale=2.031]
# [distfit] >[expon ] [RSS: 0.3911576] [loc=-6.213 scale=6.154]
# [distfit] >[pareto ] [RSS: 0.6755185] [loc=-7.965 scale=1.752]
# [distfit] >[dweibull ] [RSS: 0.0183543] [loc=-0.053 scale=1.726]
# [distfit] >[t ] [RSS: 0.0133619] [loc=-0.059 scale=2.031]
# [distfit] >[genextreme] [RSS: 0.0115116] [loc=-0.830 scale=1.964]
# [distfit] >[gamma ] [RSS: 0.0111372] [loc=-19.843 scale=0.209]
# [distfit] >[lognorm ] [RSS: 0.0111236] [loc=-29.689 scale=29.561]
# [distfit] >[beta ] [RSS: 0.0113012] [loc=-12.340 scale=41.781]
# [distfit] >[uniform ] [RSS: 0.2481737] [loc=-6.213 scale=12.281]
After we have a fitted model, we can make some predictions using the theoretical distributions. After making some predictions, we can plot again but now the predictions are automatically included.
The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html
The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html
from scipy.stats import binom
# Generate random numbers
# Set parameters for the test-case
n = 8
p = 0.5
# Generate 10000 samples of the distribution of (n, p)
X = binom(n, p).rvs(10000)
print(X)
# [5 1 4 5 5 6 2 4 6 5 4 4 4 7 3 4 4 2 3 3 4 4 5 1 3 2 7 4 5 2 3 4 3 3 2 3 5
# 4 6 7 6 2 4 3 3 5 3 5 3 4 4 4 7 5 4 5 3 4 3 3 4 3 3 6 3 3 5 4 4 2 3 2 5 7
# 5 4 8 3 4 3 5 4 3 5 5 2 5 6 7 4 5 5 5 4 4 3 4 5 6 2...]
# [distfit] >fit..
# [distfit] >transform..
# [distfit] >Fit using binomial distribution..
# [distfit] >[binomial] [SSE: 7.79] [n: 8] [p: 0.499959] [chi^2: 1.11]
# [distfit] >Compute confidence interval [discrete]
- All kinds of contributions are welcome!
Please cite distfit in your publications if this is useful for your research. See column right for citation information.