distfit 1.2.7

Python package for probability density function fitting and hypothesis testing.

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 distribution, such as the normal distribution:

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 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': <scipy.stats._distn_infrastructure.rv_frozen at 0x1ff23e3beb0>,
#  '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

Citation

Please cite distfit in your publications if this is useful for your research. Here is an example BibTeX entry:

@misc{erdogant2019distfit,
  title={distfit},
  author={Erdogan Taskesen},
  year={2019},
  howpublished={\url{https://github.com/erdogant/distfit}},
}

Maintainer

Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
Contributions are welcome.