exploration_app.py

# -*- coding: utf-8 -*-
import pickle
import pandas as pd
import streamlit as st
from scipy import stats

import plotly.express as px
import plotly.figure_factory as ff

import scipy
import numpy as np
from data_processing import load_data, process_data, get_monetary_dataframe, get_themes_per_year
from model import prepare_predictors, prepare_data, run_training, split, predict, features_importance, run_cv_training, \
    automl_training


def _max_width_():
    max_width_str = f"max-width: 1500px;"
    st.markdown(
        f"""
    <style>
    .reportview-container .main .block-container{{
        {max_width_str}
    }}
    </style>    
    """,
        unsafe_allow_html=True,
    )


# force screen width
_max_width_()

st.title("Data Analysis 🌎 📃")
st.write("by [Theolex](https://www.theolex.io/)")

# load and process data
data = load_data()
decisions, organizations, authorities = process_data(data)

col1, col2, col3, col4 = st.columns(4)

with col1:
    authorities_country = st.selectbox('Authority country', authorities.country.unique())

with col2:
    nb_years = st.selectbox('Number of years', range(1, 11), 4)

with col3:
    list_continents = decisions.org_continent.unique().tolist()
    org_continent = st.selectbox("Company's continent", list_continents, list_continents.index("europe"))

with col4:
    list_company_types = decisions.org_company_type.unique().tolist()
    org_company_type = st.selectbox("Company's activity", list_company_types,
                                    list_company_types.index("Banking & Finance"))

st.subheader(f"Which {authorities_country} regulators and prosecutors have been "
             f"the most active in enforcement actions against {org_continent} "
             f"{org_company_type} companies in the last {nb_years} years?")

# apply filters
select_auth = authorities[authorities.country == authorities_country].name.sort_values()
authority_filter = decisions.authorities_name.apply(lambda a: bool(set(select_auth) & set(a)))
year_filter = (decisions.year >= (2021 - nb_years))
org_continent_filter = (decisions.org_continent == org_continent)
org_company_type_filter = (decisions.org_company_type == org_company_type)
decision_scope = decisions[authority_filter & year_filter & org_continent_filter & org_company_type_filter]

decision_scope = decision_scope.explode("authorities_name")
top_auths = decision_scope.groupby(['authorities_name'])['authorities_name'].count().sort_values(ascending=False).head(
    5)

fig = px.bar(top_auths,
             template="simple_white",
             color_continuous_scale='RdBu',
             width=1200, height=600)
st.plotly_chart(fig)

with st.expander("Explore cases"):
    st.dataframe(decision_scope[['authorities_name', 'org_name', 'decision_date', 'monetary_sanction', 'org_country',
                                 'org_company_type']])

# st.subheader("What are the top 10 negotiated settlements in France "
#              "(involving French or foreign authorities) in the last 5 years?")
#
# st.subheader(
#     "What are the top 3 areas (sanctions, anti-corruption, fraud, market manipulation, tax, etc.) "
#     "of enforcement against banks in Germany in the last 3 years?")
#
# st.subheader("What are the largest enforcement actions involving French banks in the last 5 years?")
#
# st.subheader(
#     "Which US regulators have imposed the largest penalties against financial institutions in the last 3 years?")