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from uuid import uuid4
from datetime import datetime, timedelta
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
import pandas as pd
from scipy.stats import t
from scipy.stats import norm
import altair as alt
import plotly.express as px
import streamlit as st
def conversion_rate(conversions, visitors):
return (conversions / visitors) * 100
def lift(cra, crb):
return ((crb - cra) / cra) * 100
def std_err(cr, visitors):
return np.sqrt((cr / 100 * (1 - cr / 100)) / visitors)
def std_err_diff(sea, seb):
return np.sqrt(sea ** 2 + seb ** 2)
def z_score(cra, crb, error):
return ((crb - cra) / error) / 100
def p_value(z, hypothesis):
if hypothesis == "One-sided" and z < 0:
return 1 - norm().sf(z)
elif hypothesis == "One-sided" and z >= 0:
return norm().sf(z) / 2
else:
return norm().sf(z)
def significance(alpha, p):
return "YES" if p < alpha else "NO"
def plot_chart(df):
chart = (
alt.Chart(df)
.mark_bar(color="#61b33b")
.encode(
x=alt.X("Group:O", axis=alt.Axis(labelAngle=0)),
y=alt.Y("Conversion:Q", title="Conversion rate (%)"),
opacity="Group:O",
)
.properties(width=500, height=500)
)
chart_text = chart.mark_text(
align="center", baseline="middle", dy=-10, color="black"
).encode(text=alt.Text("Conversion:Q", format=",.3g"))
return st.altair_chart((chart + chart_text).interactive())
def style_negative(v, props=""):
return props if v < 0 else None
def style_p_value(v, props=""):
return np.where(v < st.session_state.alpha, "color:green;", props)
def calculate_significance(
conversions_a, conversions_b, visitors_a, visitors_b
):
st.session_state.cra = conversion_rate(int(conversions_a), int(visitors_a))
st.session_state.crb = conversion_rate(int(conversions_b), int(visitors_b))
st.session_state.uplift = lift(st.session_state.cra, st.session_state.crb)
st.session_state.sea = std_err(st.session_state.cra, float(visitors_a))
st.session_state.seb = std_err(st.session_state.crb, float(visitors_b))
st.session_state.sed = std_err_diff(st.session_state.sea, st.session_state.seb)
st.session_state.z = z_score(
st.session_state.cra, st.session_state.crb, st.session_state.sed
)
st.session_state.p = p_value(st.session_state.z, st.session_state.hypothesis)
st.session_state.significant = significance(
st.session_state.alpha, st.session_state.p
)
def get_dataset(size, days) -> pd.DataFrame:
end = datetime.today()
start = end - timedelta(days=days)
data = pd.DataFrame(data={
'user_id': [str(uuid4()) for _ in range(size)],
'group': np.random.choice(['old_version', 'new_version'], size=size),
'timestamp': pd.date_range(start=start, end=end, periods=size)
})
old_version_index = data[data['group'] == 'old_version'].index
new_version_index = data[data['group'] == 'new_version'].index
data.loc[old_version_index, 'converted'] = np.random.choice(
[0, 1],
size=(len(old_version_index), 1),
p=[0.8, 0.2]
)
data.loc[new_version_index, 'converted'] = np.random.choice(
[0, 1],
size=(len(new_version_index), 1),
p=[0.75, 0.25]
)
data['converted'] = data['converted'].astype('int')
data.loc[old_version_index, 'avg_check'] = np.random.normal(
size=len(old_version_index),
loc=15,
scale=7
)
data.loc[new_version_index, 'avg_check'] = np.random.normal(
size=len(new_version_index),
loc=17,
scale=6.4
)
return data
def get_plotly_converted_hist(data: pd.DataFrame):
fig = go.Figure()
fig.add_trace(
go.Histogram(
dict(
x=data[data['group'] == 'old_version']['converted'].map({1: 'Да', 0: 'Нет'}),
name='old_version'
)
)
)
fig.add_trace(
go.Histogram(
dict(
x=data[data['group'] == 'new_version']['converted'].map({1: 'Да', 0: 'Нет'}),
name='new_version'
)
)
)
fig.update_traces(hovertemplate="Сконвертирован: %{x}<br>"
"Количество: %{y}")
fig.update_layout(
title='Распределение конверсий в новой и старой версии сайта'
)
fig.update_xaxes(
title='Сконвертирован'
)
fig.update_yaxes(
title='Количество'
)
return fig
def get_fig(df: pd.DataFrame):
p = []
x = []
with st.spinner('Строю график статзначимости...'):
for i in range(50, df.shape[0]):
visitors_a = df.loc[:i][df['group'] == 'old_version'].shape[0]
visitors_b = df.loc[:i][df['group'] == 'new_version'].shape[0]
conversions_a = df.loc[:i].groupby(['group', 'converted']).agg('count')['user_id'][3]
conversions_b = df.loc[:i].groupby(['group', 'converted']).agg('count')['user_id'][1]
calculate_significance(
conversions_a,
conversions_b,
visitors_a,
visitors_b
)
p.append(np.round(p_value(st.session_state.z, st.session_state.hypothesis) * 100, 2))
x.append(df['timestamp'].iloc[i])
fig = px.line(
x=x,
y=p,
title='Зависимость статзначимости от времени проведения эксперимента')
fig.update_xaxes(
title='Количество пользователей'
)
fig.update_yaxes(
title='p-value'
)
fig.update_layout(
showlegend=False
)
fig.add_hline(
y=st.session_state.alpha * 100,
line_color='green',
line_dash='dash'
)
fig.update_traces(hovertemplate="Время А/B теста: %{x}<br>"
"Достигнутая статзначимость: %{y}%")
return fig
def get_interval(data):
return t.interval(
alpha=st.session_state.alpha,
df=2,
loc=data['avg_check'].mean(),
scale=data['avg_check'].sem()
) |