distfit - Probability density fitting
Star it if you like it!
Background
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.
Functionalities
The distfit library is created with classes to ensure simplicity in usage.
# Import library
from distfit import distfit
dist = distfit() # Specify desired parameters
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)
Installation
Install distfit from PyPI (recommended). distfit is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows.
Install from PyPi
pip install distfit
Install directly from github source (beta version)
pip install git+https://github.com/erdogant/distfit#egg=master
Install by cloning (beta version)
git clone https://github.com/erdogant/distfit.git
cd distfit
pip install -U .
Check version number
import distfit
print(distfit.__version__)
Examples
Import distfit library
from distfit import distfit
Create Some random data and model using default parameters:
import numpy as np
X = np.random.normal(0, 2, [100,10])
y = [-8,-6,0,1,2,3,4,5,6]
Specify distfit parameters. In this example nothing is specied and that means that all parameters are set to default.
dist = distfit(todf=True)
dist.fit_transform(X)
dist.plot()
# 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]
Note that the best fit should be [normal], as this was also the input data. However, many other distributions can be very similar with specific loc/scale parameters. It is however not unusual to see gamma and beta distribution as these are the "barba-pappas" among the distributions. Lets print the summary of detected distributions with the Residual Sum of Squares.
# All scores of the tested distributions
print(dist.summary)
# Distribution parameters for best fit
dist.model
# Make plot
dist.plot_summary()
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.
dist.predict(y)
dist.plot()
#
# Prints to screen:
# [distfit] >predict..
# [distfit] >Multiple test correction..[fdr_bh]
The results of the prediction are stored in y_proba and y_pred
# Show the predictions for y
print(dist.results['y_pred'])
# ['down' 'down' 'none' 'none' 'none' 'none' 'up' 'up' 'up']
# Show the probabilities for y that belong with the predictions
print(dist.results['y_proba'])
# [2.75338375e-05 2.74664877e-03 4.74739680e-01 3.28636879e-01 1.99195071e-01 1.06316132e-01 5.05914722e-02 2.18922761e-02 8.89349927e-03]
# All predicted information is also stored in a structured dataframe
print(dist.results['df'])
# y y_proba y_pred P
# 0 -8 0.000028 down 0.000003
# 1 -6 0.002747 down 0.000610
# 2 0 0.474740 none 0.474740
# 3 1 0.328637 none 0.292122
# 4 2 0.199195 none 0.154929
# 5 3 0.106316 none 0.070877
# 6 4 0.050591 up 0.028106
# 7 5 0.021892 up 0.009730
# 8 6 0.008893 up 0.002964
Example if you want to test one specific distributions, such as the normal distribution:
The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html
dist = distfit(distr='norm')
dist.fit_transform(X)
# [distfit] >fit..
# [distfit] >transform..
# [distfit] >[norm] [RSS: 0.0151267] [loc=0.103 scale=2.028]
dist.plot()
Example if you want to test multiple distributions, such as the normal and t distribution:
The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html
dist = distfit(distr=['norm', 't', 'uniform'])
results = dist.fit_transform(X)
# [distfit] >fit..
# [distfit] >transform..
# [distfit] >[norm ] [0.00 sec] [RSS: 0.0012337] [loc=0.005 scale=1.982]
# [distfit] >[t ] [0.12 sec] [RSS: 0.0012336] [loc=0.005 scale=1.982]
# [distfit] >[uniform] [0.00 sec] [RSS: 0.2505846] [loc=-6.583 scale=15.076]
# [distfit] >Compute confidence interval [parametric]
Example to fit for discrete distribution:
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...]
# Initialize distfit for discrete distribution for which the binomial distribution is used.
dist = distfit(method='discrete')
# Run distfit to and determine whether we can find the parameters from the data.
dist.fit_transform(X)
# [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]
# Get the model and best fitted parameters.
print(dist.model)
# {'distr':
,
# 'params': (8, 0.4999585504197037),
# 'name': 'binom',
# 'SSE': 7.786589839641551,
# 'chi2r': 1.1123699770916502,
# 'n': 8,
# 'p': 0.4999585504197037,
# 'CII_min_alpha': 2.0,
# 'CII_max_alpha': 6.0}
# Best fitted n=8 and p=0.4999 which is great because the input was n=8 and p=0.5
dist.model['n']
dist.model['p']
# Make plot
dist.plot()
# With the fitted model we can start making predictions on new unseen data
y = [0, 1, 10, 11, 12]
results = dist.predict(y)
dist.plot()
# Make plot with the results
dist.plot()
df_results = pd.DataFrame(pd.DataFrame(results))
# y y_proba y_pred P
# 0 0.004886 down 0.003909
# 1 0.035174 down 0.035174
# 10 0.000000 up 0.000000
# 11 0.000000 up 0.000000
# 12 0.000000 up 0.000000
Example to generate samples based on the fitted distribution:
# import library
from distfit import distfit
# Generate random normal distributed data
X = np.random.normal(0, 2, 10000)
dist = distfit()
# Fit
dist.fit_transform(X)
# The fitted distribution can now be used to generate new samples.
# Generate samples
Xgenerate = dist.generate(n=1000)
Citation
Please cite distfit in your publications if this is useful for your research. See right top panel for the citation entry.
### Maintainer
Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
Contributions are welcome.
1 Dec 17, 2021
9 Sep 14, 2022
1.2k Jan 04, 2023
1 Apr 26, 2022
56 Dec 14, 2022
114 Nov 22, 2022
76 Dec 15, 2022
48 Dec 12, 2022
6.7k Jan 07, 2023
25 Nov 27, 2022
671 Dec 25, 2022
1 Jan 08, 2022
220 Dec 13, 2022
24 Oct 27, 2022
15 Mar 28, 2022
2 Nov 11, 2021
1 Jan 31, 2022
1 Feb 04, 2022
8.9k Jan 09, 2023
1 Oct 28, 2021