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| # AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/book/LabellingTracker/14_Model.ipynb. | |
| # %% auto 0 | |
| __all__ = ['df', 'get_model_grp_data', 'get_model_summary', 'get_model_hist', 'get_step_df', 'get_gantt'] | |
| # %% ../../nbs/book/LabellingTracker/14_Model.ipynb 2 | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import plotly.express as px | |
| import seaborn as sns | |
| # %% ../../nbs/book/LabellingTracker/14_Model.ipynb 6 | |
| st.set_page_config( | |
| page_title="Model", | |
| page_icon="👋", | |
| layout='wide' | |
| ) | |
| # %% ../../nbs/book/LabellingTracker/14_Model.ipynb 8 | |
| # st.sidebar.success("Select a demo above.") | |
| # %% ../../nbs/book/LabellingTracker/14_Model.ipynb 11 | |
| def get_model_grp_data(df): | |
| grp_df = df.loc[df['TAG']=='MODEL', ['Trial_Num', 'AccountNumber', 'AccountName', 'CattleFolder/Frame','SubFolder', 'TAG', 'Assigned', | |
| 'Recording_Date', 'Video_Reception_Date', 'Assignment_Date', | |
| 'Target_Date', 'Labelling_Received_Date','Verification_Date', 'Completion/Rejection_Date']].groupby(['Trial_Num', | |
| 'AccountNumber', | |
| 'AccountName', | |
| 'TAG', | |
| 'CattleFolder/Frame', | |
| 'SubFolder' | |
| ], | |
| as_index=False).agg({'Recording_Date':['min','max'], | |
| 'Video_Reception_Date':'max', | |
| 'Assignment_Date':'min', | |
| 'Target_Date':'max', | |
| 'Labelling_Received_Date':'max', | |
| 'Verification_Date':'max', | |
| 'Completion/Rejection_Date':'max', | |
| 'Assigned': 'sum'}) | |
| flat_cols = ["_".join(i).rstrip('_') for i in grp_df.columns];# flat_cols | |
| grp_df.columns = flat_cols | |
| grp_df['Recording'] = (grp_df['Recording_Date_max'] - grp_df['Recording_Date_min']).dt.days+1 | |
| grp_df['Waiting4Video'] = (grp_df['Video_Reception_Date_max'] - grp_df['Recording_Date_max']).dt.days | |
| grp_df['Waiting4Assignment'] = (grp_df['Assignment_Date_min'] - grp_df['Video_Reception_Date_max']).dt.days | |
| grp_df['Labelling'] = (grp_df['Target_Date_max'] - grp_df['Assignment_Date_min']).dt.days | |
| grp_df['Waiting4Labels'] = (grp_df['Labelling_Received_Date_max'] - grp_df['Target_Date_max']).dt.days | |
| grp_df['Waiting4Verification'] = (grp_df['Verification_Date_max'] - grp_df['Labelling_Received_Date_max']).dt.days | |
| grp_df['Waiting4Completion'] = (grp_df['Completion/Rejection_Date_max'] - grp_df['Verification_Date_max']).dt.days | |
| grp_df['Labelling_Duration'] = grp_df['Labelling'] + grp_df['Waiting4Labels'].fillna(0) | |
| return grp_df | |
| # %% ../../nbs/book/LabellingTracker/14_Model.ipynb 14 | |
| def get_model_summary(df, kind='sum'): | |
| fig, ax = plt.subplots() | |
| grp_df = get_model_grp_data(df) | |
| states = ['Recording','Waiting4Video', 'Waiting4Assignment', 'Labelling', 'Waiting4Labels', 'Waiting4Verification','Waiting4Completion'] | |
| colors = dict(zip(states, ['blue', 'red', 'green', 'yellow', 'cyan', 'violet', 'pink'])) | |
| grp_df[['AccountNumber','AccountName', 'CattleFolder/Frame', | |
| 'Recording','Waiting4Video', 'Waiting4Assignment', 'Labelling', 'Waiting4Labels', 'Waiting4Verification','Waiting4Completion']].groupby(['AccountNumber', 'AccountName', 'CattleFolder/Frame'], as_index=False).agg(kind).set_index(['AccountNumber', 'AccountName', 'CattleFolder/Frame']).plot(kind='barh', stacked=True, ax=ax, color=colors, legend=False); | |
| fig.legend(loc='upper right') | |
| return fig | |
| # %% ../../nbs/book/LabellingTracker/14_Model.ipynb 16 | |
| def get_model_hist(df, col): | |
| grp_df = get_model_grp_data(df) | |
| fig, ax = plt.subplots() | |
| grp_df[col].plot(kind='hist', ax=ax, legend=True) | |
| avg = grp_df[col].mean() | |
| count = grp_df[col].count() | |
| ax.axvline(avg, color='red', label=f'mean={avg}') | |
| ax.set_title(f'{col}[ mean={avg:.2f}, count={count:.2f} ]') | |
| return fig | |
| # get_floating_hist(df, 'Recording_Duration') | |
| # %% ../../nbs/book/LabellingTracker/14_Model.ipynb 17 | |
| def get_step_df(grp_df, start_step, end_step, step_name): | |
| df_e = grp_df[['AccountNumber', 'AccountName','Assigned_sum', 'CattleFolder/Frame', 'SubFolder']].copy() | |
| df_e['Start'] = grp_df[start_step] | |
| df_e['End'] = grp_df[end_step] | |
| df_e['Step'] = step_name | |
| return df_e | |
| # %% ../../nbs/book/LabellingTracker/14_Model.ipynb 19 | |
| def get_gantt(df): | |
| steps = [ | |
| {'start_step': 'Recording_Date_min', 'end_step' :'Recording_Date_max', 'step_name' : 'Recording'}, | |
| {'start_step': 'Recording_Date_max', 'end_step' :'Video_Reception_Date_max', 'step_name' : 'Waiting4Video'}, | |
| {'start_step': 'Video_Reception_Date_max', 'end_step' :'Assignment_Date_min', 'step_name' : 'Waiting4Assignment'}, | |
| {'start_step': 'Assignment_Date_min', 'end_step' :'Target_Date_max', 'step_name' : 'Labelling'}, | |
| {'start_step': 'Target_Date_max', 'end_step' :'Labelling_Received_Date_max', 'step_name' : 'Waiting4Labels'}, | |
| {'start_step': 'Labelling_Received_Date_max', 'end_step' :'Verification_Date_max', 'step_name' : 'Waiting4Verification'}, | |
| {'start_step': 'Verification_Date_max', 'end_step' :'Completion/Rejection_Date_max', 'step_name' : 'Waiting4Completion'}, | |
| ] | |
| grp_df = get_model_grp_data(df) | |
| df_concat = pd.concat(get_step_df(grp_df, start_step=step['start_step'], end_step=step['end_step'], step_name=step['step_name']) for step in steps) | |
| df_concat['label'] = df_concat[['AccountNumber', 'AccountName', 'CattleFolder/Frame']].apply(lambda row: "_".join(row), axis=1) | |
| df_concat.loc[df_concat['Step']=='Recording', 'Start'] = df_concat.loc[df_concat['Step']=='Recording', 'Start'] - pd.Timedelta(days=1) | |
| # # df_concat | |
| states = [s['step_name'] for s in steps]; # states | |
| colors = dict(zip(states, ['blue', 'red', 'green', 'yellow', 'cyan', 'violet', 'pink'])) | |
| fig = px.timeline(data_frame=df_concat, x_start='Start', x_end='End', y='label', color='Step', color_discrete_map=colors, hover_data=['Assigned_sum', 'AccountName', 'AccountNumber']) | |
| # fig.update_layout(legend=dict( | |
| # orientation="h", | |
| # )) | |
| return fig | |
| # %% ../../nbs/book/LabellingTracker/14_Model.ipynb 23 | |
| df = None | |
| st.write("# Model Details") | |
| if 'processed_df' not in st.session_state: | |
| st.write("Please go to andon page and upload data") | |
| else: | |
| df = st.session_state['processed_df'] | |
| st.markdown("## Summary Model Durations") | |
| st.markdown(" Below plot includes a summary of all the frames in different model farms. Process includes recording and sharing video everyday with non sequential sharing schedule") | |
| with st.container(border=True): | |
| tab_sum, tab_mean = st.tabs(['Sum', 'Mean']) | |
| with tab_sum: | |
| st.pyplot(get_model_summary(df)) | |
| with tab_mean: | |
| st.pyplot(get_model_summary(df, kind='mean')) | |
| ncols = 3 | |
| dcols = st.columns(ncols) | |
| for i, col in enumerate(['Recording','Waiting4Video', 'Waiting4Assignment', 'Labelling_Duration', 'Waiting4Verification', 'Waiting4Completion']): | |
| with dcols[i%ncols]: | |
| st.pyplot(get_model_hist(df, col), use_container_width=True) | |
| st.markdown("## Timeline") | |
| with st.container(border=True): | |
| st.plotly_chart(get_gantt(df), theme="streamlit", use_container_width=True) | |