Add aggregations and renamed metagraph dashboard file

metadash.py

@@ -0,0 +1,114 @@
+import os
+import pandas as pd
+import streamlit as st
+from meta_utils import run_subprocess, load_metagraphs
+# from opendashboards.assets import io, inspect, metric, plot
+from meta_plotting import plot_trace, plot_cabals
+import asyncio
+
+## TODO: Read blocks from a big parquet file instead of loading all the pickles -- this is slow
+
+def get_or_create_eventloop():
+    try:
+        return asyncio.get_event_loop()
+    except RuntimeError as ex:
+        if "There is no current event loop in thread" in str(ex):
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            return asyncio.get_event_loop()
+
+loop = asyncio.new_event_loop()
+asyncio.set_event_loop(loop)
+import bittensor
+
+datadir='data/metagraph/1/'
+blockfiles = sorted(int(filename.split('.')[0]) for filename in os.listdir(datadir))
+DEFAULT_SRC = 'miner'
+DEFAULT_BLOCK_START = blockfiles[0]
+DEFAULT_BLOCK_END = blockfiles[-1]
+DEFAULT_BLOCK_STEP = 1000
+DEFAULT_NTOP = 10
+DEFAULT_UID_NTOP = 10
+
+# Set app config
+st.set_page_config(
+    page_title='Validator Dashboard',
+    menu_items={
+        'Report a bug': "https://github.com/opentensor/dashboards/issues",
+        'About': """
+        This dashboard is part of the OpenTensor project. \n
+        """
+    },
+    layout = "centered"
+    )
+
+st.title('Metagraph :red[Analysis] Dashboard :eyes:')
+# add vertical space
+st.markdown('#')
+st.markdown('#')
+
+subtensor = bittensor.subtensor(network='finney')
+current_block = subtensor.get_current_block()
+current_difficulty = subtensor.difficulty(1, block=current_block)
+
+bcol1, bcol2, bcol3 = st.columns([0.2, 0.6, 0.2])
+with bcol1:
+    st.metric('Current **block**', current_block, delta='+7200 [24hr]')
+    # st.metric('Current **difficulty**', f'{current_difficulty/10e12:.0}T', delta='?')
+    
+    
+block_start, block_end = bcol2.select_slider(
+    'Select a **block range**',
+    options=blockfiles,
+    value=(DEFAULT_BLOCK_START, DEFAULT_BLOCK_END),
+    format_func=lambda x: f'{x:,}'
+)
+
+bcol3.button('Refresh', on_click=run_subprocess)
+
+
+with st.spinner(text=f'Loading data...'):
+    # df = load_metagraphs(block_start=block_start, block_end=block_end, block_step=DEFAULT_BLOCK_STEP)
+    df = pd.read_parquet('blocks_600100_807300_100')
+
+blocks = df.block.unique()
+
+df_sel = df.loc[df.block.between(block_start, block_end)]
+
+
+# add vertical space
+st.markdown('#')
+st.markdown('#')
+
+tab1, tab2, tab3, tab4 = st.tabs(["Overview", "Miners", "Validators", "Block"])
+
+miner_choices = ['total_stake','ranks','incentive','emission','consensus','trust','validator_trust','dividends']
+cabal_choices = ['hotkey','ip','coldkey']
+
+### Overview  ###
+with tab1:
+    
+    x_col = st.radio('X-axis', ['block','timestamp'], index=0, horizontal=True)
+    
+    acol1, acol2 = st.columns([0.3, 0.7])
+    sel_ntop = acol1.slider('Number:', min_value=1, max_value=50, value=10, key='sel_ntop')    
+    #horizontal list
+    miner_choice = acol2.radio('Select:', miner_choices, horizontal=True, index=0)
+    st.plotly_chart(
+        plot_trace(df_sel, time_col=x_col,col=miner_choice, ntop=sel_ntop),
+        use_container_width=True
+    )
+
+    col1, col2 = st.columns(2)
+    count_col = col1.radio('Count', cabal_choices, index=0, horizontal=True)
+    y_col = col2.radio('Agg on', cabal_choices, index=2, horizontal=True)
+
+    st.plotly_chart(
+        plot_cabals(df_sel, time_col=x_col, count_col=count_col, sel_col=y_col, ntop=sel_ntop), 
+        use_container_width=True
+    )
+
+with tab2:
+    
+    # plot of miner weights versus time/block
+    pass

opendashboards/utils/aggregate.py

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+import pandas as pd
+
+def diversity(x):
+    return x.nunique()/len(x)
+
+def _nonempty(x):
+    return x[x.str.len()>0]
+
+def successful_diversity(x):
+    return diversity(_nonempty(x))
+
+def success_rate(x):
+    return len(_nonempty(x))/len(x)
+
+def threshold_rate(x, threshold):
+    return (x>threshold).sum()/len(x)
+
+def successful_nonzero_diversity(x):
+    # To be used with groupby.apply
+    return pd.Series({'completions_successful_nonzero_diversity': successful_diversity(x.loc[x['rewards']>0,'completions'])})
+    
+def completion_top_stats(x, exclude=None, ntop=1):
+    # To be used with groupby.apply
+    vc = x['completions'].value_counts()
+    if exclude is not None:
+        vc.drop(exclude, inplace=True, errors='ignore')
+        
+    rewards = x.loc[x['completions'].isin(vc.index[:ntop])].groupby('completions').rewards.agg(['mean','std'])
+    return pd.DataFrame({
+        'completions_rank':range(ntop),
+        'completions_top':rewards.index.tolist(),
+        'completions_freq':vc.values[:ntop],
+        'completions_reward_mean':rewards['mean'].values,
+        'completions_reward_std':rewards['std'].values
+        })
+
+def top(x, i=0, exclude=''):
+    return _nonempty(x).value_counts().drop(exclude, errors='ignore').index[i]
+
+def freq(x, i=0, exclude=''):
+    return _nonempty(x).value_counts().drop(exclude, errors='ignore').values[i]
+
+def nonzero_rate(x):
+    return (x>0).sum()/len(x)
+
+def nonzero_mean(x):
+    return x[x>0].mean()
+
+def nonzero_std(x):
+    return x[x>0].std()
+
+def nonzero_median(x):
+    return x[x>0].median()