import pandas as pd
from sklearn.model_selection import train_test_split
from sklift.datasets import fetch_hillstrom
from sklift.metrics import weighted_average_uplift
import streamlit as st
import plotly.express as px
def test():
return 'Test'
@st.experimental_memo
def get_data():
# получаем датасет
dataset = fetch_hillstrom(target_col='visit')
dataset, target, treatment = dataset['data'], dataset['target'], dataset['treatment']
# выбираем два сегмента
dataset = dataset[treatment != 'Mens E-Mail']
target = target[treatment != 'Mens E-Mail']
treatment = treatment[treatment != 'Mens E-Mail'].map({
'Womens E-Mail': 1,
'No E-Mail': 0
})
return dataset, target, treatment
@st.experimental_memo
def data_split(data: pd.DataFrame, treatment: pd.DataFrame, target: pd.DataFrame):
# склеиваем threatment и target для дальнейшей стратификации по ним
stratify_cols = pd.concat([treatment, target], axis=1)
# сплитим датасет
X_train, X_val, trmnt_train, trmnt_val, y_train, y_val = train_test_split(
data,
treatment,
target,
stratify=stratify_cols,
test_size=0.5,
random_state=42
)
return X_train, X_val, trmnt_train, trmnt_val, y_train, y_val
def filter_by_newbie(data: pd.DataFrame, newbie_filter: str) -> pd.DataFrame:
if newbie_filter == 'Все':
return data
elif newbie_filter == 'Только новые':
return data[data['newbie'] == 1]
elif newbie_filter == 'Только старые':
return data[data['newbie'] == 0]
def filter_by_channel(data: pd.DataFrame, channel_filter: str) -> pd.DataFrame:
if channel_filter == 'Все':
return data
if channel_filter == 'Phone':
return data[data['channel'] == channel_filter]
if channel_filter == 'Web':
return data[data['channel'] == channel_filter]
if channel_filter == 'Multichannel':
return data[data['channel'] == channel_filter]
def filter_by_mens(data: pd.DataFrame, mens_filter: str) -> pd.DataFrame:
if mens_filter == 'Любые':
return data
if mens_filter == 'Мужские':
return data[data['mens'] == 1]
if mens_filter == 'Женские':
return data[data['womens'] == 1]
def filter_by_history_segments(data: pd.DataFrame, history_segments_filter: dict) -> pd.DataFrame:
filtered_indexes = set()
if history_segments_filter.get('1) $0 - $100'):
filtered_indexes = filtered_indexes.union(data[data['history_segment'] == '1) $0 - $100'].index)
if history_segments_filter.get('2) $100 - $200'):
filtered_indexes = filtered_indexes.union(data[data['history_segment'] == '2) $100 - $200'].index)
if history_segments_filter.get('3) $200 - $350'):
filtered_indexes = filtered_indexes.union(data[data['history_segment'] == '3) $200 - $350'].index)
if history_segments_filter.get('4) $350 - $500'):
filtered_indexes = filtered_indexes.union(data[data['history_segment'] == '4) $350 - $500'].index)
if history_segments_filter.get('5) $500 - $750'):
filtered_indexes = filtered_indexes.union(data[data['history_segment'] == '5) $500 - $750'].index)
if history_segments_filter.get('6) $750 - $1,000'):
filtered_indexes = filtered_indexes.union(data[data['history_segment'] == '6) $750 - $1,000'].index)
if history_segments_filter.get('7) $1,000 +'):
filtered_indexes = filtered_indexes.union(data[data['history_segment'] == '7) $1,000 +'].index)
return data.loc[list(filtered_indexes)]
def filter_by_zip_code(data: pd.DataFrame, zip_code_filter: dict) -> pd.DataFrame:
filterd_indexes = set()
if zip_code_filter.get('surburban'):
filterd_indexes = filterd_indexes.union(data[data['zip_code'] == 'Surburban'].index)
if zip_code_filter.get('urban'):
filterd_indexes = filterd_indexes.union(data[data['zip_code'] == 'Urban'].index)
if zip_code_filter.get('rural'):
filterd_indexes = filterd_indexes.union(data[data['zip_code'] == 'Rural'].index)
return data.loc[list(filterd_indexes)]
def filter_by_recency(data: pd.DataFrame, recency_filter: list) -> pd.DataFrame:
return data[(data['recency'] >= recency_filter[0]) & (data['recency'] <= recency_filter[1])]
def filter_data(data: pd.DataFrame, filters: dict) -> pd.DataFrame or None:
"""
Filter data by user filters
:param data: filtered data
:param filters: dict of filters
:return: filtered data
"""
data = filter_by_newbie(data, filters['newbie_filter'])
if data.shape[0] == 0:
return None
data = filter_by_channel(data, filters['channel_filter'])
if data.shape[0] == 0:
return None
data = filter_by_mens(data, filters['mens_filter'])
if data.shape[0] == 0:
return None
data = filter_by_history_segments(data, filters['history_segments'])
if data.shape[0] == 0:
return None
data = filter_by_zip_code(data, filters['zip_code'])
if data.shape[0] == 0:
return None
data = filter_by_recency(data, filters['recency'])
if data.shape[0] == 0:
return None
return data
def uplift_by_percentile():
pass
def get_weighted_average_uplift(target_test: pd.DataFrame, uplift, treatment_test: pd.DataFrame):
return weighted_average_uplift(target_test, uplift, treatment_test)
def get_newbie_plot(data):
fig = px.histogram(
data['newbie'],
color=data['newbie'],
title='Распределение клиентов по флагу newbie'
)
fig.update_xaxes(
title='',
ticktext=['"Старые" клиенты', 'Новые клиенты'],
tickvals=[0, 1]
)
fig.update_yaxes(
title='Количество клиентов'
)
fig.update_layout(
showlegend=False,
bargap=0.3,
margin=dict(l=20, r=10, t=80, b=10)
)
fig.update_traces(hovertemplate="Количество клиентов: %{y}")
return fig
def get_zipcode_plot(data):
fig = px.histogram(
data['zip_code'],
color=data['newbie'],
title='Распределение клиентов по почтовым индексам'
)
fig.update_xaxes(
title='',
categoryorder='total descending'
)
fig.update_yaxes(
title='Количество клиентов'
)
fig.update_layout(
showlegend=True,
legend_orientation="h",
legend=dict(x=.66, y=.99, title='Новый клиент'),
margin=dict(l=20, r=10, t=80, b=10),
hovermode="x",
bargap=0.3
)
fig.update_traces(hovertemplate="Количество клиентов: %{y}")
return fig
def get_channel_plot(data):
fig = px.histogram(
data['channel'],
color=data['newbie'],
title='Распределение клиентов по каналам покупки товаров'
)
fig.update_xaxes(
title='',
categoryorder='total descending'
)
fig.update_yaxes(
title='Количество клиентов'
)
fig.update_layout(
showlegend=True,
legend_orientation="h",
legend=dict(x=.66, y=.99, title='Новый клиент'),
margin=dict(l=20, r=10, t=80, b=10),
hovermode="x",
bargap=0.3
)
fig.update_traces(hovertemplate="Количество клиентов: %{y}")
return fig
def get_history_segment_plot(data):
fig = px.histogram(
data['history_segment'],
color=data['history_segment'],
title='Распределение клиентов по количеству $, потраченных в прошлом году'
)
fig.update_xaxes(
title='',
categoryorder='total descending',
tickangle=45
)
fig.update_yaxes(
title='Количество клиентов'
)
fig.update_layout(
showlegend=False,
bargap=0.3,
margin=dict(l=20, r=10, t=80, b=10)
)
fig.update_traces(hovertemplate="Количество клиентов: %{y}")
return fig
def get_recency_plot(data):
fig = px.histogram(
data['recency'],
color=data['newbie'],
title='Распределение клиентов по количеству месяцев с последней покупки'
)
fig.update_xaxes(
title='Месяцев после покупки'
)
fig.update_yaxes(
title='Количество клиентов'
)
fig.update_layout(
showlegend=True,
legend_orientation="h",
legend=dict(x=.66, y=.99, title='Новый клиент'),
margin=dict(l=20, r=10, t=80, b=10),
hovermode="x",
bargap=0.3
)
fig.update_traces(hovertemplate="
".join(
[
"Месяцев: %{x}",
"Клиентов: %{y}"
]
)
)
return fig
def get_history_plot(data):
fig = px.histogram(
data['history'],
color=data['newbie'],
title='Распределение клиентов по сумме покупки'
)
fig.update_xaxes(
title='Сумма покупки'
)
fig.update_yaxes(
title='Количество клиентов'
)
fig.update_layout(
showlegend=True,
legend_orientation="h",
legend=dict(x=.66, y=.99, title='Новый клиент'),
margin=dict(l=20, r=10, t=80, b=10),
hovermode="x",
bargap=0.3
)
fig.update_traces(hovertemplate="
".join(
[
'Совершено покупок на: $%{x}',
'Количество клиентов: %{y}'
]
)
)
return fig