This commit is contained in:
gokul
2023-06-14 18:04:27 +05:30
parent cfc91568fa
commit b62ab9168a
2 changed files with 597 additions and 16 deletions

View File

@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"id": "f604fe38",
"id": "13525c0a",
"metadata": {},
"source": [
"# Visualization Experiments"
@@ -10,7 +10,7 @@
},
{
"cell_type": "markdown",
"id": "cad594ed",
"id": "99b6a77c",
"metadata": {},
"source": [
"Lets load the data artefacts to local memory. These files are to be downloaded from S3 as the pipeline automatically uploads them to the pre-configured S3 bucket."
@@ -18,22 +18,33 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "dbd7b93d",
"execution_count": 24,
"id": "455ba9af",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2023-06-14 18:01:52.751\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mfile_util\u001b[0m:\u001b[36mdownload_files\u001b[0m:\u001b[36m36\u001b[0m - \u001b[1mDownloading file df.pkl\u001b[0m\n",
"\u001b[32m2023-06-14 18:01:56.546\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mfile_util\u001b[0m:\u001b[36mdownload_files\u001b[0m:\u001b[36m36\u001b[0m - \u001b[1mDownloading file mappings.pkl\u001b[0m\n"
]
}
],
"source": [
"from file_util import download_files\n",
"import pickle\n",
"\n",
"# Download files from S3 bucket. You can download multiple files at a time by passing a list of names\n",
"files_to_download = [\"df.pkl\", \"mapping.pkl\"]\n"
"files_to_download = [\"df.pkl\", \"mappings.pkl\"]\n",
"download_files(files_to_download)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f59ff46b",
"id": "fcef3f4d",
"metadata": {},
"outputs": [],
"source": [
@@ -44,7 +55,7 @@
{
"cell_type": "code",
"execution_count": 16,
"id": "61aee352",
"id": "01aa4dc3",
"metadata": {},
"outputs": [],
"source": [
@@ -57,7 +68,15 @@
},
{
"cell_type": "markdown",
"id": "5584c887",
"id": "2e344353",
"metadata": {},
"source": [
"## Example template 1"
]
},
{
"cell_type": "markdown",
"id": "ec7c2c55",
"metadata": {},
"source": [
"## Scatter plot of transcription with Topic modelling"
@@ -65,7 +84,7 @@
},
{
"cell_type": "markdown",
"id": "5fae1776",
"id": "71921ceb",
"metadata": {},
"source": [
"Change the values of \"category\", \"category_name\" to one agenda topic and change the value of \"not_category_name\" and see different plots."
@@ -108,16 +127,577 @@
},
{
"cell_type": "markdown",
"id": "e2d6ec49",
"id": "3c8ef97d",
"metadata": {},
"source": [
"## Timeline visualizer"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "fa95284d",
"metadata": {},
"outputs": [],
"source": [
"mappings = pickle.load(open(\"mappings.pkl\", \"rb\"))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "7d588df9",
"metadata": {},
"outputs": [
{
"data": {
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" (926.4, 933.2): 'AGENDA',\n",
" (933.2, 938.72): 'TAM',\n",
" (938.72, 944.0): 'TAM',\n",
" (944.0, 945.76): 'TAM',\n",
" (946.96, 951.96): 'Founders',\n",
" (952.24, 956.48): 'TAM',\n",
" (958.48, 969.2): 'TAM',\n",
" (969.8, 980.24): 'TAM',\n",
" (1025.28, 1029.6): 'TAM',\n",
" (1029.6, 1035.6): 'Founders',\n",
" (1036.32, 1043.84): 'Product market fit',\n",
" (1047.6, 1051.84): 'TAM',\n",
" (1051.84, 1056.16): 'TAM',\n",
" (1056.16, 1060.8): 'Founders',\n",
" (1060.8, 1069.76): 'TAM',\n",
" (1070.4, 1076.56): 'TAM',\n",
" (1076.56, 1080.8): 'Founders',\n",
" (1080.8, 1089.12): 'TAM',\n",
" (1089.12, 1094.32): 'AGENDA',\n",
" (1094.32, 1099.76): 'Product market fit',\n",
" (1099.76, 1103.04): 'AGENDA',\n",
" (1103.92, 1105.44): 'AGENDA',\n",
" (1105.48, 1110.96): 'TAM',\n",
" (1111.28, 1117.76): 'Founders',\n",
" (1118.4, 1126.0): 'Founders',\n",
" (1126.0, 1131.28): 'TAM',\n",
" (1131.28, 1136.48): 'AGENDA',\n",
" (1136.48, 1140.72): 'TAM',\n",
" (1140.72, 1188.4): 'TAM',\n",
" (1188.4, 1199.52): 'AGENDA',\n",
" (1210.48, 1211.08): 'Founders',\n",
" (1213.32, 1216.36): 'TAM',\n",
" (1223.72, 1224.28): 'TAM',\n",
" (1239.0, 1255.0): 'TAM',\n",
" (1255.0, 1268.0): 'Founders',\n",
" (1268.0, 1277.0): 'TAM',\n",
" (1277.0, 1285.36): 'TAM',\n",
" (1286.48, 1290.96): 'Founders',\n",
" (1290.96, 1296.56): 'Churn',\n",
" (1297.36, 1303.04): 'TAM',\n",
" (1303.04, 1310.0): 'TAM',\n",
" (1310.0, 1346.8): 'TAM',\n",
" (1347.2, 1351.2): 'TAM',\n",
" (1351.7, 1362.2): 'Founders',\n",
" (1366.0, 1372.0): 'TAM',\n",
" (1372.0, 1376.0): 'Founders',\n",
" (1376.0, 1382.0): 'TAM',\n",
" (1382.0, 1386.0): 'Founders',\n",
" (1400.56, 1401.36): 'TAM',\n",
" (1406.08, 1410.64): 'Founders',\n",
" (1410.64, 1416.48): 'AGENDA',\n",
" (1416.48, 1422.72): 'TAM',\n",
" (1422.72, 1449.0): 'TAM',\n",
" (1449.0, 1455.0): 'Founders',\n",
" (1455.0, 1458.0): 'Founders',\n",
" (1458.0, 1464.0): 'TAM',\n",
" (1464.0, 1466.4): 'Founders',\n",
" (1469.92, 1475.84): 'TAM',\n",
" (1476.4, 1481.76): 'TAM',\n",
" (1483.12, 1486.64): 'Founders',\n",
" (1486.64, 1491.6): 'TAM',\n",
" (1491.6, 1497.28): 'AGENDA',\n",
" (1497.28, 1503.04): 'TAM',\n",
" (1503.04, 1530.08): 'TAM',\n",
" (1530.08, 1539.92): 'Founders',\n",
" (1539.92, 1630.0): 'Founders',\n",
" (1630.0, 1637.0): 'Product market fit',\n",
" (1637.0, 1665.04): 'TAM',\n",
" (1665.04, 1670.72): 'Product market fit',\n",
" (1670.72, 1674.72): 'Churn',\n",
" (1674.72, 1681.2): 'TAM',\n",
" (1681.2, 1689.0): 'AGENDA',\n",
" (1689.0, 1696.0): 'TAM',\n",
" (1696.0, 1705.12): 'Founders',\n",
" (1705.12, 1710.0): 'TAM',\n",
" (1710.0, 1715.36): 'Product market fit',\n",
" (1715.36, 1720.4): 'TAM',\n",
" (1721.68, 1726.48): 'Founders',\n",
" (1726.48, 1732.0): 'Founders',\n",
" (1732.0, 1737.84): 'Product market fit',\n",
" (1737.84, 1742.0): 'Founders',\n",
" (1742.0, 1745.2): 'Founders',\n",
" (1745.2, 1752.4): 'TAM',\n",
" (1752.4, 1758.4): 'Founders',\n",
" (1758.4, 1789.6): 'TAM',\n",
" (1789.6, 1792.12): 'AGENDA',\n",
" (1792.12, 1796.52): 'AGENDA',\n",
" (1796.52, 1800.2): 'Founders',\n",
" (1800.2, 1804.52): 'Product market fit',\n",
" (1804.52, 1806.8): 'AGENDA',\n",
" (1807.2, 1812.26): 'TAM',\n",
" (1812.26, 1817.76): 'TAM',\n",
" (1818.84, 1823.6): 'AGENDA',\n",
" (1827.6, 1834.08): 'TAM',\n",
" (1834.08, 1839.12): 'AGENDA',\n",
" (1839.12, 1843.68): 'Founders',\n",
" (1843.68, 1845.48): 'AGENDA',\n",
" (1846.48, 1869.36): 'AGENDA',\n",
" (1869.36, 1874.08): 'TAM',\n",
" (1874.08, 1877.76): 'TAM',\n",
" (1877.76, 1881.28): 'AGENDA',\n",
" (1881.28, 1927.2): 'Founders',\n",
" (1927.2, 1931.92): 'AGENDA',\n",
" (1931.92, 1935.92): 'Founders',\n",
" (1937.12, 1941.44): 'Product market fit',\n",
" (1941.44, 1966.08): 'TAM',\n",
" (1966.08, 1971.52): 'TAM',\n",
" (1971.52, 1976.32): 'TAM',\n",
" (1976.32, 1980.48): 'TAM',\n",
" (1980.48, 1984.56): 'Founders',\n",
" (2007.38, 2021.0): 'Product market fit',\n",
" (2027.42, 2034.26): 'TAM',\n",
" (2034.26, 2065.92): 'Founders',\n",
" (2065.92, 2073.52): 'Product market fit',\n",
" (2073.52, 2077.28): 'Founders',\n",
" (2077.84, 2081.12): 'Founders',\n",
" (2081.12, 2089.84): 'Founders',\n",
" (2089.84, 2094.32): 'Founders',\n",
" (2094.32, 2100.88): 'Founders',\n",
" (2100.88, 2104.88): 'AGENDA',\n",
" (2104.88, 2110.4): 'Business',\n",
" (2110.4, 2115.0): 'AGENDA',\n",
" (2115.5, 2120.8): 'AGENDA',\n",
" (2120.8, 2122.7): 'TAM',\n",
" (2122.9, 2127.0): 'AGENDA',\n",
" (2127.0, 2131.0): 'Founders',\n",
" (2131.0, 2135.0): 'TAM',\n",
" (2135.0, 2141.0): 'Founders',\n",
" (2141.0, 2205.0): 'TAM',\n",
" (2205.64, 2208.84): 'AGENDA',\n",
" (2208.84, 2211.64): 'AGENDA',\n",
" (2211.64, 2214.76): 'AGENDA',\n",
" (2214.76, 2217.64): 'AGENDA',\n",
" (2217.64, 2220.36): 'TAM',\n",
" (2220.36, 2222.12): 'Product market fit',\n",
" (2222.12, 2224.84): 'AGENDA',\n",
" (2224.84, 2257.0): 'TAM',\n",
" (2257.0, 2268.88): 'TAM',\n",
" (2268.88, 2273.2): 'AGENDA',\n",
" (2273.2, 2278.72): 'Founders',\n",
" (2278.72, 2284.0): 'TAM',\n",
" (2284.0, 2285.12): 'TAM',\n",
" (2285.12, 2289.04): 'Founders',\n",
" (2289.04, 2292.16): 'Founders',\n",
" (2292.16, 2296.4): 'AGENDA',\n",
" (2297.44, 2303.12): 'TAM',\n",
" (2303.12, 2370.0): 'TAM',\n",
" (2370.0, 2377.0): 'Founders',\n",
" (2377.0, 2387.44): 'Churn',\n",
" (2387.44, 2393.36): 'TAM',\n",
" (2393.36, 2397.84): 'TAM',\n",
" (2397.84, 2403.04): 'AGENDA',\n",
" (2403.04, 2442.0): 'TAM',\n",
" (2467.56, 2468.52): 'TAM',\n",
" (2469.88, 2472.44): 'TAM',\n",
" (2472.44, 2474.96): 'AGENDA',\n",
" (2474.96, 2478.8): 'TAM',\n",
" (2478.8, 2481.16): 'Product market fit',\n",
" (2481.16, 2484.8): 'Product market fit',\n",
" (2484.8, 2526.44): 'TAM',\n",
" (2521.59, 2527.91): 'AGENDA'},\n",
" {'TAM': (2370.0, 2377.0),\n",
" 'Founders': (2521.59, 2527.91),\n",
" 'AGENDA': (2481.16, 2484.8),\n",
" 'Product market fit': (2472.44, 2474.96),\n",
" 'Unit economics': (1290.96, 1296.56),\n",
" 'Churn': (1036.32, 1043.84),\n",
" 'Business': (2211.64, 2214.76)},\n",
" {'Founders': (2370.0, 2377.0),\n",
" 'TAM': (2484.8, 2526.44),\n",
" 'AGENDA': (2521.59, 2527.91),\n",
" 'Product market fit': (2481.16, 2484.8),\n",
" 'Churn': (2377.0, 2387.44),\n",
" 'Business': (2104.88, 2110.4)}]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mappings"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08e83128",
"id": "edb167a7",
"metadata": {},
"outputs": [],
"source": []

View File

@@ -6,6 +6,7 @@
import argparse
import ast
import collections
import configparser
import jax.numpy as jnp
import matplotlib.pyplot as plt
@@ -159,8 +160,8 @@ def create_talk_diff_scatter_viz():
ts_to_topic_mapping_top_2 = {}
# Also create a mapping of the different timestamps in which each topic was covered
topic_to_ts_mapping_top_1 = {}
topic_to_ts_mapping_top_2 = {}
topic_to_ts_mapping_top_1 = collections.defaultdict(list)
topic_to_ts_mapping_top_2 = collections.defaultdict(list)
similarity_threshold = 0.7
@@ -181,11 +182,11 @@ def create_talk_diff_scatter_viz():
# top1 match
if i == 0:
ts_to_topic_mapping_top_1[c["timestamp"]] = agenda_topics[topic_similarities[i][0]]
topic_to_ts_mapping_top_1[agenda_topics[topic_similarities[i][0]]] = c["timestamp"]
topic_to_ts_mapping_top_1[agenda_topics[topic_similarities[i][0]]].append(c["timestamp"])
# top2 match
else:
ts_to_topic_mapping_top_2[c["timestamp"]] = agenda_topics[topic_similarities[i][0]]
topic_to_ts_mapping_top_2[agenda_topics[topic_similarities[i][0]]] = c["timestamp"]
topic_to_ts_mapping_top_2[agenda_topics[topic_similarities[i][0]]].append(c["timestamp"])
def create_new_columns(record):
"""