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1# Copyright 2017-2020 Spotify AB
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
15import pandas as pd
16import numpy as np
17from itertools import product
20def example_data_binomial():
21 """
22 Returns an output dataframe with categorical
23 features (country and test variation), and orginal features (date),
24 as well as number of successes and total observations for each combination
25 """
26 countries = ["ca", "us"]
27 dates = pd.date_range("2018-01-01", "2018-02-01")
28 variation_names = ["test", "control", "test2"]
30 # test ca, test us, control ca, control us, test2 ca, test2 us
31 success_rates = [0.3, 0.32, 0.24, 0.22, 0.25, 0.42]
32 n_observations = [50, 80, 30, 50, 40, 50]
34 return_df = pd.DataFrame()
36 for i, (country, variation) in enumerate(product(countries, variation_names)):
37 df = pd.DataFrame({"date": dates})
38 df["country"] = country
39 df["variation_name"] = variation
40 df["total"] = np.random.poisson(n_observations[i], size=len(dates))
41 df["success"] = df["total"].apply(lambda x: np.random.binomial(x, success_rates[i]))
42 return_df = pd.concat([return_df, df], axis=0)
44 return return_df
47def example_data_gaussian():
48 df = pd.DataFrame(
49 {
50 "variation_name": [
51 "test",
52 "control",
53 "test2",
54 "test",
55 "control",
56 "test2",
57 "test",
58 "control",
59 "test2",
60 "test",
61 "control",
62 "test2",
63 "test",
64 "control",
65 "test2",
66 ],
67 "nr_of_items": [
68 500,
69 8,
70 100,
71 510,
72 8,
73 100,
74 520,
75 9,
76 104,
77 530,
78 7,
79 100,
80 530,
81 8,
82 103,
83 ],
84 "nr_of_items_sumsq": [
85 2500,
86 12,
87 150,
88 2510,
89 13,
90 140,
91 2520,
92 14,
93 154,
94 2530,
95 15,
96 160,
97 2530,
98 16,
99 103,
100 ],
101 "users": [
102 1010,
103 22,
104 150,
105 1000,
106 20,
107 153,
108 1030,
109 23,
110 154,
111 1000,
112 20,
113 150,
114 1040,
115 21,
116 155,
117 ],
118 "days_since_reg": [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5],
119 }
120 )
122 return df