pyfreya.revenue package

Submodules

pyfreya.revenue.revenue module

Short Tutorial in the Revenue Classes.

Let’s import the class and insert a some retention numbers along with the amount of new users in a cohort. Additional information about Retention and Cohort classes can be found at retention tutorial and cohort tutorial. Let’s also import a predefined revenue spending class (profile) ARPDAU.

                        1
DaysSinceInstall
0                     100
1                 50.0629
2                 32.1914
3                 24.8632
4                 20.6996
5                 17.9566
6                 15.9875
7                 14.4921
8                 13.3102
9                  12.348
10                11.5464
11                10.8662
12                10.2802
13                9.76917
14                9.31867
15                8.91795
16                8.55872
17                8.23447
18                7.94001
19                7.67118
20                7.42456
21                7.19733
22                6.98716
23                6.79206
24                6.61038
25                6.44069
26                6.28175
27                6.13252
28                5.99207
29                 5.8596
30                 5.7344
31                5.61586
dau revenue
Date
1 100 210
2 50.0629 105.132
3 32.1914 67.6019
4 24.8632 52.2127
5 20.6996 43.4692
6 17.9566 37.7089
7 15.9875 33.5737
8 14.4921 30.4334
9 13.3102 27.9515
10 12.348 25.9309
11 11.5464 24.2475
12 10.8662 22.819
13 10.2802 21.5885
14 9.76917 20.5153
15 9.31867 19.5692
16 8.91795 18.7277
17 8.55872 17.9733
18 8.23447 17.2924
19 7.94001 16.674
20 7.67118 16.1095
21 7.42456 15.5916
22 7.19733 15.1144
23 6.98716 14.673
24 6.79206 14.2633
25 6.61038 13.8818
26 6.44069 13.5254
27 6.28175 13.1917
28 6.13252 12.8783
29 5.99207 12.5833
30 5.8596 12.3052
31 5.7344 12.0422

As can be seen, there is also room for adding uncertainty. Let’s import the base revenue class and define ARPDAU but with uncertainty. It does make use of some properties of the Cohort class, it is recommended to be a bit familiar with that.

dau revenue
Date
1 100 210
2 50.0629 105.132
3 32.1914 67.6019
4 24.8632 52.2127
5 20.6996 43.4692
6 17.9566 37.7089
7 15.9875 33.5737
8 14.4921 30.4334
9 13.3102 27.9515
10 12.348 25.9309
11 11.5464 24.2475
12 10.8662 22.819
13 10.2802 21.5885
14 9.76917 20.5153
15 9.31867 19.5692
16 8.91795 18.7277
17 8.55872 17.9733
18 8.23447 17.2924
19 7.94001 16.674
20 7.67118 16.1095
21 7.42456 15.5916
22 7.19733 15.1144
23 6.98716 14.673
24 6.79206 14.2633
25 6.61038 13.8818
26 6.44069 13.5254
27 6.28175 13.1917
28 6.13252 12.8783
29 5.99207 12.5833
30 5.8596 12.3052
31 5.7344 12.0422
_images/Revenue_Example_5_0.png

If we are interested in uncertainties the Uncertainties package have been implemented. This can be used the following way:

_images/Revenue_Example_7_0.png

When working with uncertainties, the nominal values and the uncertainty values can be obtained with functions nominal_values and std_devs, respectively:

Date
1       210+/-23
2       107+/-14
3         68+/-8
4         52+/-6
5         43+/-5
6         37+/-4
7         33+/-4
8         30+/-4
9     27.0+/-3.3
10    25.0+/-3.1
11    23.3+/-2.9
12    21.9+/-2.8
13    20.6+/-2.6
14    19.6+/-2.5
15    18.6+/-2.4
16    17.8+/-2.3
17    17.1+/-2.2
18    16.4+/-2.2
19    15.8+/-2.1
20    15.2+/-2.0
21    14.7+/-2.0
22    14.2+/-1.9
23    13.8+/-1.9
24    13.4+/-1.8
25    13.0+/-1.8
26    12.7+/-1.8
27    12.4+/-1.7
28    12.1+/-1.7
29    11.8+/-1.7
30    11.5+/-1.6
31    11.2+/-1.6
Name: revenue, dtype: object

array([210.        , 107.11673044,  67.66930193,  51.72634515,
        42.74901227,  36.8733419 ,  32.67730124,  29.50438329,
        27.00601304,  24.97849563,  23.29415112,  21.86853369,
        20.64336873,  19.57703135,  18.63892795,  17.80602528,
        17.06062202,  16.38887082,  15.77976993,  15.22445827,
        14.71571191,  14.24757745,  13.81510062,  13.41412211,
        13.04112202,  12.69309974,  12.36748022,  12.06203999,
        11.77484823,  11.50421944,  11.24867512])

array([22.588714  , 13.73966474,  8.17827559,  6.15442801,  5.07962406,
        4.40195366,  3.9300445 ,  3.57937697,  3.3066122 ,  3.08712987,
        2.90585349,  2.75300466,  2.62194609,  2.5080031 ,  2.40778109,
        2.31875073,  2.23898552,  2.16699004,  2.1015841 ,  2.04182268,
        1.9869392 ,  1.93630468,  1.88939763,  1.84578168,  1.80508855,
        1.76700502,  1.73126286,  1.69763082,  1.66590837,  1.63592067,
        1.60751445])

It is possible to save a revenue class instance (using pickle) and loading it.

facebook.save('facebook_revenue.pkl')
facebook_loaded = pyfreya.load('facebook_revenue.pkl')

Revenue Classes

class pyfreya.revenue.revenue.ARPDAU(arpdau=0.0)[source]

Bases: pyfreya.revenue.revenue.BaseRevenue

A basic revenue spending class. It applies an average revenue to each active users on a given date. There is no uncertainty applied to this class.

class pyfreya.revenue.revenue.BaseRevenue[source]

Bases: object

Base class for the revenue profile. Revenue profiles are created by defining a new class which inherits functions from the BaseRevenue class:

class MyClass(BaseRevenue)
    def __init__(self):
        super().__init__()
    def _revenue():
        return 0
save(filename)[source]

Saves the revenue model. As revenue models can have quite different properties pickle is used to save instances of the class.

Returns:0

Module contents

Inits revenue