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# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/book/EventTracker/11_Andon.ipynb.
# %% auto 0
__all__ = ['df', 'farms', 'selected_farms', 'filter_df', 'generate_discrete_colors', 'get_discrete_colormap', 'get_summary',
'get_event_metrics', 'get_algo_performance', 'get_pie', 'get_data', 'get_farms', 'kpi']
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 2
import streamlit as st
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 6
st.set_page_config(
page_title="Andon",
layout='wide'
)
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 11
def generate_discrete_colors(n):
colors = px.colors.qualitative.Plotly
return [colors[i % len(colors)] for i in range(n)]
generate_discrete_colors(5)
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 12
def get_discrete_colormap(classes):
colors = generate_discrete_colors(len(classes))
color_discrete_map = dict(zip(classes, colors))
return color_discrete_map
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 13
def get_summary(df, col_a='Type of Alert', col_b='Ground Truth'):
cal_df = df[[col_a, col_b]].groupby([col_a, col_b]).value_counts().reset_index()
color_discrete_map = get_discrete_colormap(df[col_b].dropna().unique())
fig = px.bar(cal_df, y=col_a,
x='count',
color=col_b,
color_discrete_map=color_discrete_map,
orientation='h',
title=f'Summary: {col_a}|{col_b}')
fig.update_layout(
margin=dict(l=10, r=10, t=40, b=40),
yaxis=dict(automargin=True)
)
return fig
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 16
def get_event_metrics(validated_df,
event_type_cols=['Heat', 'Missed Heat'],
event_type='Heat'):
val_event_df = validated_df[validated_df['Type of Alert'].isin(event_type_cols)]
a = val_event_df['Ground Truth'].value_counts()
recall = a.get('TRUE', 0)*100/(a.get('TRUE', 0)+a.get('MISSED', np.finfo(float).eps))
precision = a.get('TRUE', 0)*100/(a.get('TRUE', 0)+a.get('FALSE', np.finfo(float).eps))
return {
f'Validated_{event_type}_Recall': recall,
f'Validated_{event_type}_Precision': precision,
}
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 17
def get_algo_performance(df):
confirmed_df = df[df['Ground Truth'].isin(['TRUE','FALSE', 'MISSED', 'EXCLUDE', 'OTHER'])]
validated_df = df[df['Ground Truth'].isin(['TRUE','FALSE', 'MISSED'])]
missed_df = df[df['Ground Truth'].isin(['MISSED'])]
heat_metrics = get_event_metrics(validated_df,
event_type_cols=['Heat', 'Missed Heat'],
event_type='Heat')
health_metrics = get_event_metrics(validated_df,
event_type_cols=['Health', 'Missed Health'],
event_type='Health')
data_coverage_metrics = {'Total':len(df),
'Confirmed': len(confirmed_df),
'Validated': len(validated_df),
'Missed': len(missed_df),
'Confirmed_Percentage': len(confirmed_df)*100/len(df),
'Validated_Percentage': len(validated_df)*100/len(df),
'Val_On_Confirm_Percentage': len(validated_df)*100/len(confirmed_df),
'Missed_On_Val_Percentage': len(missed_df)*100/len(validated_df) }
performance_metrics = {**data_coverage_metrics,**heat_metrics, **health_metrics}
return performance_metrics
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 20
def get_pie(df, colname):
df_dist = df[colname].value_counts().reset_index()
fig = px.pie(df_dist, values='count',
names=colname,
title=f'{colname}',
color = colname,
color_discrete_map = get_discrete_colormap(df[colname].dropna().unique())
)
return fig
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 35
def get_data(url="https://docs.google.com/spreadsheets/d/1TbqmmSzXtY8DwolVo9rN7M1QdZxLG30nro2cqrvn2CA/export?format=csv&#gid=294763682"):
df = pd.read_csv(url).dropna(how='all')
df['Alert Date'] = df['Alert Date'].str.replace("//", "-")
df = df.dropna(subset=['Alert Date'])
df['Type of Alert'] = df['Type of Alert'].str.strip()
return df
# colors
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 36
def get_farms(df):
return df['Dairy Farm'].dropna().unique().tolist()
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 39
def kpi(df, ncols = 7):
metrics = get_algo_performance(df)
dcols = st.columns(ncols)
for (i, (k, v)) in enumerate(metrics.items()):
with dcols[i%ncols]:
st.metric(k, np.round(v, 2))
# %% ../../nbs/book/EventTracker/11_Andon.ipynb 40
#|eval: false
df = get_data()
farms = get_farms(df)
selected_farms = st.sidebar.multiselect('Select farms', farms, max_selections=len(farms))
filter_df = df
if selected_farms:
str_farms = "|".join(selected_farms)
st.write(f"Selected_farms: {str_farms}")
filter_df = df[df['Dairy Farm'].isin(selected_farms)]
else:
st.write("Selected_farms: All")
# st.write(farms)
kpi(filter_df,ncols=4)
st.plotly_chart(get_pie(filter_df, colname = 'Type of Alert'))
st.plotly_chart(get_pie(filter_df, colname = 'Ground Truth'))
st.plotly_chart(get_pie(filter_df, colname = 'Dairy Farm'))
st.plotly_chart(get_summary(filter_df, col_a='Type of Alert', col_b='Ground Truth'))
st.plotly_chart(get_summary(filter_df, col_b='Type of Alert', col_a='Dairy Farm'))
st.plotly_chart(get_summary(filter_df, col_b='Ground Truth', col_a='Dairy Farm'))
#
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