{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import gc\n",
"sns.set_style(\"darkgrid\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"pd.set_option('display.float_format', lambda x: '%.3f' % x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preparation"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"tools = pd.read_parquet('../data/tools.parquet')\n",
"fpmms = pd.read_parquet('../data/fpmms.parquet')\n",
"summary_traders = pd.read_parquet('../data/summary_profitability.parquet')\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"all_trades = pd.read_parquet('../data/all_trades_profitability.parquet')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" trader_address | \n",
" trade_id | \n",
" creation_timestamp | \n",
" title | \n",
" market_status | \n",
" collateral_amount | \n",
" outcome_index | \n",
" trade_fee_amount | \n",
" outcomes_tokens_traded | \n",
" current_answer | \n",
" is_invalid | \n",
" winning_trade | \n",
" earnings | \n",
" redeemed | \n",
" redeemed_amount | \n",
" num_mech_calls | \n",
" mech_fee_amount | \n",
" net_earnings | \n",
" roi | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
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\n",
" \n",
" 2 | \n",
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" 0.672 | \n",
" 0.795 | \n",
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\n",
" \n",
" 3 | \n",
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" 3 | \n",
" 0.030 | \n",
" -0.495 | \n",
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\n",
" \n",
" 4 | \n",
" 0x022b36c50b85b8ae7addfb8a35d76c59d5814834 | \n",
" 0x027f6bc849e273477f4a63085192714084917fcc0x02... | \n",
" 2024-06-14 03:01:20+00:00 | \n",
" Will the 2D-animated Paramount Plus show 'Tale... | \n",
" CLOSED | \n",
" 0.704 | \n",
" 0 | \n",
" 0.014 | \n",
" 1.198 | \n",
" 1 | \n",
" False | \n",
" False | \n",
" 0.000 | \n",
" True | \n",
" 0.000 | \n",
" 3 | \n",
" 0.030 | \n",
" -0.748 | \n",
" -1.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" trader_address \\\n",
"0 0x022b36c50b85b8ae7addfb8a35d76c59d5814834 \n",
"1 0x022b36c50b85b8ae7addfb8a35d76c59d5814834 \n",
"2 0x022b36c50b85b8ae7addfb8a35d76c59d5814834 \n",
"3 0x022b36c50b85b8ae7addfb8a35d76c59d5814834 \n",
"4 0x022b36c50b85b8ae7addfb8a35d76c59d5814834 \n",
"\n",
" trade_id \\\n",
"0 0x017947579ab51313c31fe1cc562c0f1726ec09c90x02... \n",
"1 0x027f6bc849e273477f4a63085192714084917fcc0x02... \n",
"2 0x027f6bc849e273477f4a63085192714084917fcc0x02... \n",
"3 0x027f6bc849e273477f4a63085192714084917fcc0x02... \n",
"4 0x027f6bc849e273477f4a63085192714084917fcc0x02... \n",
"\n",
" creation_timestamp \\\n",
"0 2024-05-19 01:26:30+00:00 \n",
"1 2024-06-12 01:16:55+00:00 \n",
"2 2024-06-12 15:08:00+00:00 \n",
"3 2024-06-13 07:22:55+00:00 \n",
"4 2024-06-14 03:01:20+00:00 \n",
"\n",
" title market_status \\\n",
"0 Will Google's Pixel 9 lineup be officially rel... CLOSED \n",
"1 Will the 2D-animated Paramount Plus show 'Tale... CLOSED \n",
"2 Will the 2D-animated Paramount Plus show 'Tale... CLOSED \n",
"3 Will the 2D-animated Paramount Plus show 'Tale... CLOSED \n",
"4 Will the 2D-animated Paramount Plus show 'Tale... CLOSED \n",
"\n",
" collateral_amount outcome_index trade_fee_amount outcomes_tokens_traded \\\n",
"0 0.638 1 0.013 1.206 \n",
"1 1.000 1 0.020 1.840 \n",
"2 0.800 1 0.016 1.518 \n",
"3 0.456 0 0.009 1.003 \n",
"4 0.704 0 0.014 1.198 \n",
"\n",
" current_answer is_invalid winning_trade earnings redeemed \\\n",
"0 1 False True 1.206 True \n",
"1 1 False True 1.840 True \n",
"2 1 False True 1.518 True \n",
"3 1 False False 0.000 True \n",
"4 1 False False 0.000 True \n",
"\n",
" redeemed_amount num_mech_calls mech_fee_amount net_earnings roi \n",
"0 1.206 0 0.000 0.556 0.854 \n",
"1 1.840 3 0.030 0.790 0.752 \n",
"2 1.518 3 0.030 0.672 0.795 \n",
"3 0.000 3 0.030 -0.495 -1.000 \n",
"4 0.000 3 0.030 -0.748 -1.000 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_trades.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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\n",
" \n",
" \n",
" | \n",
" trader_address | \n",
" trade_id | \n",
" creation_timestamp | \n",
" title | \n",
" market_status | \n",
" collateral_amount | \n",
" outcome_index | \n",
" trade_fee_amount | \n",
" outcomes_tokens_traded | \n",
" current_answer | \n",
" is_invalid | \n",
" winning_trade | \n",
" earnings | \n",
" redeemed | \n",
" redeemed_amount | \n",
" num_mech_calls | \n",
" mech_fee_amount | \n",
" net_earnings | \n",
" roi | \n",
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\n",
" \n",
" \n",
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" 0.020 | \n",
" 0.078 | \n",
" 0.640 | \n",
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" \n",
" 18937 | \n",
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\n",
" \n",
" 18938 | \n",
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" 2024-05-22 20:00:05+00:00 | \n",
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\n",
" \n",
" 18939 | \n",
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"text/plain": [
" trader_address \\\n",
"18936 0xc8929dd39bb5f685435ab16345929a47caacc46b \n",
"18937 0xc8929dd39bb5f685435ab16345929a47caacc46b \n",
"18938 0xc8929dd39bb5f685435ab16345929a47caacc46b \n",
"18939 0xc8929dd39bb5f685435ab16345929a47caacc46b \n",
"18940 0x7b2e78d4dfaaba045a167a70da285e30e8fca196 \n",
"\n",
" trade_id \\\n",
"18936 0xd068383c5d6d1466d10db660f33524c2725f8fb60xc8... \n",
"18937 0xec5578e95c71ddbad6aabf8517dcd35cf53da4970xc8... \n",
"18938 0xf2c74ef39065ee2e239bf8551aedddd6b2d6add70xc8... \n",
"18939 0xfdf1a762eaae0a4472599f26aeafeae043b37d360xc8... \n",
"18940 0xaf8fa4b8e04bbbee6903fede1d27b3aad25b468e0x7b... \n",
"\n",
" creation_timestamp \\\n",
"18936 2024-05-22 19:05:00+00:00 \n",
"18937 2024-05-22 17:57:35+00:00 \n",
"18938 2024-05-22 20:00:05+00:00 \n",
"18939 2024-05-22 19:42:35+00:00 \n",
"18940 2024-07-05 09:10:40+00:00 \n",
"\n",
" title market_status \\\n",
"18936 Will Elon Musk's Neuralink successfully test i... CLOSED \n",
"18937 Will Kevin Spacey return to acting by 25 May 2... CLOSED \n",
"18938 Will Donald Trump testify in the hush money ca... CLOSED \n",
"18939 Will a new Marvel Cinematic Universe (MCU) mov... CLOSED \n",
"18940 Will Vice President Kamala Harris be the Democ... CLOSED \n",
"\n",
" collateral_amount outcome_index trade_fee_amount \\\n",
"18936 0.100 1 0.002 \n",
"18937 0.160 0 0.003 \n",
"18938 0.100 1 0.002 \n",
"18939 0.100 0 0.002 \n",
"18940 1.000 1 0.020 \n",
"\n",
" outcomes_tokens_traded current_answer is_invalid winning_trade \\\n",
"18936 0.200 1 False True \n",
"18937 0.309 0 False True \n",
"18938 0.200 1 False True \n",
"18939 0.211 1 False False \n",
"18940 1.717 1 False True \n",
"\n",
" earnings redeemed redeemed_amount num_mech_calls mech_fee_amount \\\n",
"18936 0.200 False 0.000 2 0.020 \n",
"18937 0.309 False 0.000 2 0.020 \n",
"18938 0.200 False 0.000 3 0.030 \n",
"18939 0.000 False 0.000 2 0.020 \n",
"18940 1.717 False 0.000 0 0.000 \n",
"\n",
" net_earnings roi \n",
"18936 0.078 0.640 \n",
"18937 0.126 0.686 \n",
"18938 0.068 0.518 \n",
"18939 -0.122 -1.000 \n",
"18940 0.697 0.684 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_trades.tail()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 17067 entries, 0 to 17066\n",
"Data columns (total 19 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 trader_address 17067 non-null object \n",
" 1 trade_id 17067 non-null object \n",
" 2 creation_timestamp 17067 non-null datetime64[ns, UTC]\n",
" 3 title 17067 non-null object \n",
" 4 market_status 17067 non-null object \n",
" 5 collateral_amount 17067 non-null float64 \n",
" 6 outcome_index 17067 non-null object \n",
" 7 trade_fee_amount 17067 non-null float64 \n",
" 8 outcomes_tokens_traded 17067 non-null float64 \n",
" 9 current_answer 17067 non-null int64 \n",
" 10 is_invalid 17067 non-null bool \n",
" 11 winning_trade 17067 non-null bool \n",
" 12 earnings 17067 non-null float64 \n",
" 13 redeemed 17067 non-null bool \n",
" 14 redeemed_amount 17067 non-null float64 \n",
" 15 num_mech_calls 17067 non-null int64 \n",
" 16 mech_fee_amount 17067 non-null float64 \n",
" 17 net_earnings 17067 non-null float64 \n",
" 18 roi 17067 non-null float64 \n",
"dtypes: bool(3), datetime64[ns, UTC](1), float64(8), int64(2), object(5)\n",
"memory usage: 2.1+ MB\n"
]
}
],
"source": [
"all_trades.info()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2024-05-12 00:04:25+0000', tz='UTC')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_trades.creation_timestamp.min()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2024-07-14 01:09:10+0000', tz='UTC')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_trades.creation_timestamp.max()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2024-07-08 02:29:40+0000', tz='UTC')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_trades.creation_timestamp.max()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(339443, 22)\n"
]
},
{
"data": {
"text/plain": [
"(28911882, 34138429)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(tools.shape)\n",
"tools.request_block.min(), tools.request_block.max()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(89518, 22)\n"
]
},
{
"data": {
"text/plain": [
"(33989007, 34993418)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(tools.shape)\n",
"tools.request_block.min(), tools.request_block.max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Error analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Error analysis only starts from block 321. We weren't capturing the error message prior"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['prediction-online', 'prediction-offline', 'normal-sme-generator',\n",
" 'strong-sme-generator', 'prediction-online-sme',\n",
" 'prediction-offline-sme', 'claude-prediction-offline', 'openai',\n",
" 'claude-prediction-online',\n",
" 'prediction-sentence-embedding-conservative',\n",
" 'prediction-online-summarized-info',\n",
" 'prediction-sentence-embedding-bold',\n",
" 'prediction-online-sum-url-content',\n",
" 'openai-gpt-3.5-turbo-instruct',\n",
" 'resolve-market-reasoning-gpt-3.5-turbo',\n",
" 'resolve-market-reasoning-gpt-4', 'prediction-request-rag',\n",
" 'prediction-request-reasoning',\n",
" 'prediction-request-reasoning-claude', 'prediction-url-cot-claude',\n",
" 'prediction-request-rag-claude', 'native_transfer_request',\n",
" 'native_transfer'], dtype=object)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tools.tool.unique()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 339443 entries, 0 to 339442\n",
"Data columns (total 22 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 request_id 339443 non-null object \n",
" 1 request_block 339443 non-null int64 \n",
" 2 prompt_request 339443 non-null object \n",
" 3 tool 339443 non-null object \n",
" 4 nonce 339443 non-null object \n",
" 5 trader_address 339443 non-null object \n",
" 6 deliver_block 339443 non-null int64 \n",
" 7 error 339440 non-null float64\n",
" 8 error_message 56715 non-null object \n",
" 9 prompt_response 252711 non-null object \n",
" 10 mech_address 286960 non-null object \n",
" 11 p_yes 282717 non-null float64\n",
" 12 p_no 282717 non-null float64\n",
" 13 confidence 282717 non-null float64\n",
" 14 info_utility 282717 non-null float64\n",
" 15 vote 256610 non-null object \n",
" 16 win_probability 282717 non-null float64\n",
" 17 title 329340 non-null object \n",
" 18 currentAnswer 267690 non-null object \n",
" 19 request_time 339443 non-null object \n",
" 20 request_month_year 339443 non-null object \n",
" 21 request_month_year_week 339443 non-null object \n",
"dtypes: float64(6), int64(2), object(14)\n",
"memory usage: 57.0+ MB\n"
]
}
],
"source": [
"tools.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2023-07-12 11:58:40'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tools.request_time.min()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"inc_tools = [\n",
" 'prediction-online', \n",
" 'prediction-offline', \n",
" 'claude-prediction-online', \n",
" 'claude-prediction-offline', \n",
" 'prediction-offline-sme',\n",
" 'prediction-online-sme',\n",
" 'prediction-request-rag',\n",
" 'prediction-request-reasoning',\n",
" 'prediction-url-cot-claude', \n",
" 'prediction-request-rag-claude',\n",
" 'prediction-request-reasoning-claude'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# include only tools that are in inc_tools\n",
"tools_inc = tools[tools['tool'].isin(inc_tools)]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"error = tools_inc.groupby(['tool', 'request_month_year_week', 'error']).size().unstack().fillna(0).reset_index()\n",
"error[\"error_perc\"] = (error[1] / (error[0] + error[1])) * 100\n",
"error[\"total_requests\"] = error[0] + error[1]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"error_total = (\n",
" error.groupby(\"request_month_year_week\")\n",
" .agg({\"total_requests\": \"sum\", 1: \"sum\", 0: \"sum\"})\n",
" .reset_index()\n",
")\n",
"error_total[\"error_perc\"] = (error_total[1] / error_total[\"total_requests\"]) * 100\n",
"error_total.columns = error_total.columns.astype(str)\n",
"error_total[\"error_perc\"] = error_total[\"error_perc\"].apply(lambda x: round(x, 4))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" error | \n",
" request_month_year_week | \n",
" total_requests | \n",
" 1 | \n",
" 0 | \n",
" error_perc | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2023-07-10/2023-07-16 | \n",
" 44.000 | \n",
" 31.000 | \n",
" 13.000 | \n",
" 70.454 | \n",
"
\n",
" \n",
" 1 | \n",
" 2023-07-17/2023-07-23 | \n",
" 56.000 | \n",
" 0.000 | \n",
" 56.000 | \n",
" 0.000 | \n",
"
\n",
" \n",
" 2 | \n",
" 2023-07-24/2023-07-30 | \n",
" 48.000 | \n",
" 5.000 | \n",
" 43.000 | \n",
" 10.417 | \n",
"
\n",
" \n",
" 3 | \n",
" 2023-07-31/2023-08-06 | \n",
" 922.000 | \n",
" 203.000 | \n",
" 719.000 | \n",
" 22.017 | \n",
"
\n",
" \n",
" 4 | \n",
" 2023-08-07/2023-08-13 | \n",
" 313.000 | \n",
" 9.000 | \n",
" 304.000 | \n",
" 2.875 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error request_month_year_week total_requests 1 0 error_perc\n",
"0 2023-07-10/2023-07-16 44.000 31.000 13.000 70.454\n",
"1 2023-07-17/2023-07-23 56.000 0.000 56.000 0.000\n",
"2 2023-07-24/2023-07-30 48.000 5.000 43.000 10.417\n",
"3 2023-07-31/2023-08-06 922.000 203.000 719.000 22.017\n",
"4 2023-08-07/2023-08-13 313.000 9.000 304.000 2.875"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"error_total.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Error Percentage by Month-Year')"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 6))\n",
"ax=sns.lineplot(error_total, x='request_month_year_week', y='error_perc',color=\"green\")\n",
"ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
"plt.xlabel('Month-Year')\n",
"plt.ylabel('Error Percentage')\n",
"plt.title('Error Percentage by Month-Year')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" error | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" tool | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" prediction-request-rag-claude | \n",
" 13.498 | \n",
" 1704.000 | \n",
"
\n",
" \n",
" prediction-request-rag | \n",
" 12.041 | \n",
" 490.000 | \n",
"
\n",
" \n",
" prediction-online-sme | \n",
" 9.076 | \n",
" 2457.000 | \n",
"
\n",
" \n",
" prediction-online | \n",
" 4.769 | \n",
" 2516.000 | \n",
"
\n",
" \n",
" prediction-request-reasoning | \n",
" 3.247 | \n",
" 5883.000 | \n",
"
\n",
" \n",
" prediction-request-reasoning-claude | \n",
" 1.408 | \n",
" 639.000 | \n",
"
\n",
" \n",
" claude-prediction-offline | \n",
" 0.000 | \n",
" 107.000 | \n",
"
\n",
" \n",
" claude-prediction-online | \n",
" 0.000 | \n",
" 241.000 | \n",
"
\n",
" \n",
" prediction-offline | \n",
" 0.000 | \n",
" 1453.000 | \n",
"
\n",
" \n",
" prediction-url-cot-claude | \n",
" 0.000 | \n",
" 293.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error error_perc total_requests\n",
"tool \n",
"prediction-request-rag-claude 13.498 1704.000\n",
"prediction-request-rag 12.041 490.000\n",
"prediction-online-sme 9.076 2457.000\n",
"prediction-online 4.769 2516.000\n",
"prediction-request-reasoning 3.247 5883.000\n",
"prediction-request-reasoning-claude 1.408 639.000\n",
"claude-prediction-offline 0.000 107.000\n",
"claude-prediction-online 0.000 241.000\n",
"prediction-offline 0.000 1453.000\n",
"prediction-url-cot-claude 0.000 293.000"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"error[error['request_month_year_week'] == '2024-05-20/2024-05-26'].groupby('tool').agg({'error_perc': 'mean', 'total_requests': 'sum'}).sort_values('error_perc', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" error | \n",
" tool | \n",
" request_month_year_week | \n",
" 0.000 | \n",
" 1.000 | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" \n",
" \n",
" 19 | \n",
" claude-prediction-offline | \n",
" 2024-05-20/2024-05-26 | \n",
" 107.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 107.000 | \n",
"
\n",
" \n",
" 18 | \n",
" claude-prediction-offline | \n",
" 2024-05-13/2024-05-19 | \n",
" 203.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 203.000 | \n",
"
\n",
" \n",
" 17 | \n",
" claude-prediction-offline | \n",
" 2024-05-06/2024-05-12 | \n",
" 156.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 156.000 | \n",
"
\n",
" \n",
" 16 | \n",
" claude-prediction-offline | \n",
" 2024-04-29/2024-05-05 | \n",
" 531.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 531.000 | \n",
"
\n",
" \n",
" 15 | \n",
" claude-prediction-offline | \n",
" 2024-04-22/2024-04-28 | \n",
" 816.000 | \n",
" 5.000 | \n",
" 0.609 | \n",
" 821.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error tool request_month_year_week 0.000 1.000 \\\n",
"19 claude-prediction-offline 2024-05-20/2024-05-26 107.000 0.000 \n",
"18 claude-prediction-offline 2024-05-13/2024-05-19 203.000 0.000 \n",
"17 claude-prediction-offline 2024-05-06/2024-05-12 156.000 0.000 \n",
"16 claude-prediction-offline 2024-04-29/2024-05-05 531.000 0.000 \n",
"15 claude-prediction-offline 2024-04-22/2024-04-28 816.000 5.000 \n",
"\n",
"error error_perc total_requests \n",
"19 0.000 107.000 \n",
"18 0.000 203.000 \n",
"17 0.000 156.000 \n",
"16 0.000 531.000 \n",
"15 0.609 821.000 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# clude-prediction-offline\n",
"claude_prediction_offline = error[error['tool'] == 'claude-prediction-offline']\n",
"claude_prediction_offline = claude_prediction_offline.sort_values('request_month_year_week', ascending=False)\n",
"claude_prediction_offline.head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" error | \n",
" tool | \n",
" request_month_year_week | \n",
" 0.000 | \n",
" 1.000 | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" \n",
" \n",
" 54 | \n",
" claude-prediction-online | \n",
" 2024-05-20/2024-05-26 | \n",
" 241.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 241.000 | \n",
"
\n",
" \n",
" 53 | \n",
" claude-prediction-online | \n",
" 2024-05-13/2024-05-19 | \n",
" 37.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 37.000 | \n",
"
\n",
" \n",
" 52 | \n",
" claude-prediction-online | \n",
" 2024-05-06/2024-05-12 | \n",
" 176.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 176.000 | \n",
"
\n",
" \n",
" 51 | \n",
" claude-prediction-online | \n",
" 2024-04-29/2024-05-05 | \n",
" 192.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 192.000 | \n",
"
\n",
" \n",
" 50 | \n",
" claude-prediction-online | \n",
" 2024-04-22/2024-04-28 | \n",
" 1937.000 | \n",
" 155.000 | \n",
" 7.409 | \n",
" 2092.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error tool request_month_year_week 0.000 1.000 \\\n",
"54 claude-prediction-online 2024-05-20/2024-05-26 241.000 0.000 \n",
"53 claude-prediction-online 2024-05-13/2024-05-19 37.000 0.000 \n",
"52 claude-prediction-online 2024-05-06/2024-05-12 176.000 0.000 \n",
"51 claude-prediction-online 2024-04-29/2024-05-05 192.000 0.000 \n",
"50 claude-prediction-online 2024-04-22/2024-04-28 1937.000 155.000 \n",
"\n",
"error error_perc total_requests \n",
"54 0.000 241.000 \n",
"53 0.000 37.000 \n",
"52 0.000 176.000 \n",
"51 0.000 192.000 \n",
"50 7.409 2092.000 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# claude-prediction-online\n",
"claude_prediction_online = error[error['tool'] == 'claude-prediction-online']\n",
"claude_prediction_online = claude_prediction_online.sort_values('request_month_year_week', ascending=False)\n",
"claude_prediction_online.head()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" error | \n",
" tool | \n",
" request_month_year_week | \n",
" 0.000 | \n",
" 1.000 | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" \n",
" \n",
" 84 | \n",
" prediction-offline | \n",
" 2024-05-20/2024-05-26 | \n",
" 1453.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 1453.000 | \n",
"
\n",
" \n",
" 83 | \n",
" prediction-offline | \n",
" 2024-05-13/2024-05-19 | \n",
" 4270.000 | \n",
" 1.000 | \n",
" 0.023 | \n",
" 4271.000 | \n",
"
\n",
" \n",
" 82 | \n",
" prediction-offline | \n",
" 2024-05-06/2024-05-12 | \n",
" 2500.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 2500.000 | \n",
"
\n",
" \n",
" 81 | \n",
" prediction-offline | \n",
" 2024-04-29/2024-05-05 | \n",
" 1825.000 | \n",
" 2.000 | \n",
" 0.109 | \n",
" 1827.000 | \n",
"
\n",
" \n",
" 80 | \n",
" prediction-offline | \n",
" 2024-04-22/2024-04-28 | \n",
" 381.000 | \n",
" 375.000 | \n",
" 49.603 | \n",
" 756.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error tool request_month_year_week 0.000 1.000 \\\n",
"84 prediction-offline 2024-05-20/2024-05-26 1453.000 0.000 \n",
"83 prediction-offline 2024-05-13/2024-05-19 4270.000 1.000 \n",
"82 prediction-offline 2024-05-06/2024-05-12 2500.000 0.000 \n",
"81 prediction-offline 2024-04-29/2024-05-05 1825.000 2.000 \n",
"80 prediction-offline 2024-04-22/2024-04-28 381.000 375.000 \n",
"\n",
"error error_perc total_requests \n",
"84 0.000 1453.000 \n",
"83 0.023 4271.000 \n",
"82 0.000 2500.000 \n",
"81 0.109 1827.000 \n",
"80 49.603 756.000 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# prediction-offline\n",
"prediction_offline = error[error['tool'] == 'prediction-offline']\n",
"prediction_offline = prediction_offline.sort_values('request_month_year_week', ascending=False)\n",
"prediction_offline.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" error | \n",
" tool | \n",
" request_month_year_week | \n",
" 0.000 | \n",
" 1.000 | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" \n",
" \n",
" 139 | \n",
" prediction-online | \n",
" 2024-05-20/2024-05-26 | \n",
" 2396.000 | \n",
" 120.000 | \n",
" 4.769 | \n",
" 2516.000 | \n",
"
\n",
" \n",
" 138 | \n",
" prediction-online | \n",
" 2024-05-13/2024-05-19 | \n",
" 2642.000 | \n",
" 393.000 | \n",
" 12.949 | \n",
" 3035.000 | \n",
"
\n",
" \n",
" 137 | \n",
" prediction-online | \n",
" 2024-05-06/2024-05-12 | \n",
" 2840.000 | \n",
" 266.000 | \n",
" 8.564 | \n",
" 3106.000 | \n",
"
\n",
" \n",
" 136 | \n",
" prediction-online | \n",
" 2024-04-29/2024-05-05 | \n",
" 2155.000 | \n",
" 24.000 | \n",
" 1.101 | \n",
" 2179.000 | \n",
"
\n",
" \n",
" 135 | \n",
" prediction-online | \n",
" 2024-04-22/2024-04-28 | \n",
" 252.000 | \n",
" 153.000 | \n",
" 37.778 | \n",
" 405.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error tool request_month_year_week 0.000 1.000 \\\n",
"139 prediction-online 2024-05-20/2024-05-26 2396.000 120.000 \n",
"138 prediction-online 2024-05-13/2024-05-19 2642.000 393.000 \n",
"137 prediction-online 2024-05-06/2024-05-12 2840.000 266.000 \n",
"136 prediction-online 2024-04-29/2024-05-05 2155.000 24.000 \n",
"135 prediction-online 2024-04-22/2024-04-28 252.000 153.000 \n",
"\n",
"error error_perc total_requests \n",
"139 4.769 2516.000 \n",
"138 12.949 3035.000 \n",
"137 8.564 3106.000 \n",
"136 1.101 2179.000 \n",
"135 37.778 405.000 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# prediction-online\n",
"prediction_online = error[error['tool'] == 'prediction-online']\n",
"prediction_online = prediction_online.sort_values('request_month_year_week', ascending=False)\n",
"prediction_online.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" error | \n",
" tool | \n",
" request_month_year_week | \n",
" 0.000 | \n",
" 1.000 | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" \n",
" \n",
" 104 | \n",
" prediction-offline-sme | \n",
" 2024-04-29/2024-05-05 | \n",
" 8.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 8.000 | \n",
"
\n",
" \n",
" 103 | \n",
" prediction-offline-sme | \n",
" 2024-04-22/2024-04-28 | \n",
" 159.000 | \n",
" 2.000 | \n",
" 1.242 | \n",
" 161.000 | \n",
"
\n",
" \n",
" 102 | \n",
" prediction-offline-sme | \n",
" 2024-04-15/2024-04-21 | \n",
" 717.000 | \n",
" 2.000 | \n",
" 0.278 | \n",
" 719.000 | \n",
"
\n",
" \n",
" 101 | \n",
" prediction-offline-sme | \n",
" 2024-04-08/2024-04-14 | \n",
" 4.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 4.000 | \n",
"
\n",
" \n",
" 100 | \n",
" prediction-offline-sme | \n",
" 2024-04-01/2024-04-07 | \n",
" 197.000 | \n",
" 1.000 | \n",
" 0.505 | \n",
" 198.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error tool request_month_year_week 0.000 1.000 \\\n",
"104 prediction-offline-sme 2024-04-29/2024-05-05 8.000 0.000 \n",
"103 prediction-offline-sme 2024-04-22/2024-04-28 159.000 2.000 \n",
"102 prediction-offline-sme 2024-04-15/2024-04-21 717.000 2.000 \n",
"101 prediction-offline-sme 2024-04-08/2024-04-14 4.000 0.000 \n",
"100 prediction-offline-sme 2024-04-01/2024-04-07 197.000 1.000 \n",
"\n",
"error error_perc total_requests \n",
"104 0.000 8.000 \n",
"103 1.242 161.000 \n",
"102 0.278 719.000 \n",
"101 0.000 4.000 \n",
"100 0.505 198.000 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# prediction-offline-sme\n",
"prediction_offline_sme = error[error['tool'] == 'prediction-offline-sme']\n",
"prediction_offline_sme = prediction_offline_sme.sort_values('request_month_year_week', ascending=False)\n",
"prediction_offline_sme.head()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" error | \n",
" tool | \n",
" request_month_year_week | \n",
" 0.000 | \n",
" 1.000 | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" \n",
" \n",
" 175 | \n",
" prediction-online-sme | \n",
" 2024-05-20/2024-05-26 | \n",
" 2234.000 | \n",
" 223.000 | \n",
" 9.076 | \n",
" 2457.000 | \n",
"
\n",
" \n",
" 174 | \n",
" prediction-online-sme | \n",
" 2024-05-13/2024-05-19 | \n",
" 3141.000 | \n",
" 668.000 | \n",
" 17.537 | \n",
" 3809.000 | \n",
"
\n",
" \n",
" 173 | \n",
" prediction-online-sme | \n",
" 2024-05-06/2024-05-12 | \n",
" 3799.000 | \n",
" 562.000 | \n",
" 12.887 | \n",
" 4361.000 | \n",
"
\n",
" \n",
" 172 | \n",
" prediction-online-sme | \n",
" 2024-04-29/2024-05-05 | \n",
" 2534.000 | \n",
" 6.000 | \n",
" 0.236 | \n",
" 2540.000 | \n",
"
\n",
" \n",
" 171 | \n",
" prediction-online-sme | \n",
" 2024-04-22/2024-04-28 | \n",
" 2679.000 | \n",
" 1075.000 | \n",
" 28.636 | \n",
" 3754.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error tool request_month_year_week 0.000 1.000 \\\n",
"175 prediction-online-sme 2024-05-20/2024-05-26 2234.000 223.000 \n",
"174 prediction-online-sme 2024-05-13/2024-05-19 3141.000 668.000 \n",
"173 prediction-online-sme 2024-05-06/2024-05-12 3799.000 562.000 \n",
"172 prediction-online-sme 2024-04-29/2024-05-05 2534.000 6.000 \n",
"171 prediction-online-sme 2024-04-22/2024-04-28 2679.000 1075.000 \n",
"\n",
"error error_perc total_requests \n",
"175 9.076 2457.000 \n",
"174 17.537 3809.000 \n",
"173 12.887 4361.000 \n",
"172 0.236 2540.000 \n",
"171 28.636 3754.000 "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# prediction-online-sme\n",
"prediction_online_sme = error[error['tool'] == 'prediction-online-sme']\n",
"prediction_online_sme = prediction_online_sme.sort_values('request_month_year_week', ascending=False)\n",
"prediction_online_sme.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" error | \n",
" tool | \n",
" request_month_year_week | \n",
" 0.000 | \n",
" 1.000 | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" \n",
" \n",
" 188 | \n",
" prediction-request-rag | \n",
" 2024-05-20/2024-05-26 | \n",
" 431.000 | \n",
" 59.000 | \n",
" 12.041 | \n",
" 490.000 | \n",
"
\n",
" \n",
" 187 | \n",
" prediction-request-rag | \n",
" 2024-05-13/2024-05-19 | \n",
" 355.000 | \n",
" 55.000 | \n",
" 13.415 | \n",
" 410.000 | \n",
"
\n",
" \n",
" 186 | \n",
" prediction-request-rag | \n",
" 2024-05-06/2024-05-12 | \n",
" 470.000 | \n",
" 125.000 | \n",
" 21.008 | \n",
" 595.000 | \n",
"
\n",
" \n",
" 185 | \n",
" prediction-request-rag | \n",
" 2024-04-29/2024-05-05 | \n",
" 544.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 544.000 | \n",
"
\n",
" \n",
" 184 | \n",
" prediction-request-rag | \n",
" 2024-04-22/2024-04-28 | \n",
" 2011.000 | \n",
" 881.000 | \n",
" 30.463 | \n",
" 2892.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error tool request_month_year_week 0.000 1.000 \\\n",
"188 prediction-request-rag 2024-05-20/2024-05-26 431.000 59.000 \n",
"187 prediction-request-rag 2024-05-13/2024-05-19 355.000 55.000 \n",
"186 prediction-request-rag 2024-05-06/2024-05-12 470.000 125.000 \n",
"185 prediction-request-rag 2024-04-29/2024-05-05 544.000 0.000 \n",
"184 prediction-request-rag 2024-04-22/2024-04-28 2011.000 881.000 \n",
"\n",
"error error_perc total_requests \n",
"188 12.041 490.000 \n",
"187 13.415 410.000 \n",
"186 21.008 595.000 \n",
"185 0.000 544.000 \n",
"184 30.463 2892.000 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# prediction-request-rag\n",
"prediction_request_rag = error[error['tool'] == 'prediction-request-rag']\n",
"prediction_request_rag = prediction_request_rag.sort_values('request_month_year_week', ascending=False)\n",
"prediction_request_rag.head()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" error | \n",
" tool | \n",
" request_month_year_week | \n",
" 0.000 | \n",
" 1.000 | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" \n",
" \n",
" 214 | \n",
" prediction-request-reasoning-claude | \n",
" 2024-05-20/2024-05-26 | \n",
" 630.000 | \n",
" 9.000 | \n",
" 1.408 | \n",
" 639.000 | \n",
"
\n",
" \n",
" 213 | \n",
" prediction-request-reasoning-claude | \n",
" 2024-05-13/2024-05-19 | \n",
" 309.000 | \n",
" 205.000 | \n",
" 39.883 | \n",
" 514.000 | \n",
"
\n",
" \n",
" 212 | \n",
" prediction-request-reasoning-claude | \n",
" 2024-05-06/2024-05-12 | \n",
" 478.000 | \n",
" 54.000 | \n",
" 10.150 | \n",
" 532.000 | \n",
"
\n",
" \n",
" 211 | \n",
" prediction-request-reasoning-claude | \n",
" 2024-04-29/2024-05-05 | \n",
" 218.000 | \n",
" 8.000 | \n",
" 3.540 | \n",
" 226.000 | \n",
"
\n",
" \n",
" 210 | \n",
" prediction-request-reasoning-claude | \n",
" 2024-04-22/2024-04-28 | \n",
" 2053.000 | \n",
" 575.000 | \n",
" 21.880 | \n",
" 2628.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error tool request_month_year_week 0.000 \\\n",
"214 prediction-request-reasoning-claude 2024-05-20/2024-05-26 630.000 \n",
"213 prediction-request-reasoning-claude 2024-05-13/2024-05-19 309.000 \n",
"212 prediction-request-reasoning-claude 2024-05-06/2024-05-12 478.000 \n",
"211 prediction-request-reasoning-claude 2024-04-29/2024-05-05 218.000 \n",
"210 prediction-request-reasoning-claude 2024-04-22/2024-04-28 2053.000 \n",
"\n",
"error 1.000 error_perc total_requests \n",
"214 9.000 1.408 639.000 \n",
"213 205.000 39.883 514.000 \n",
"212 54.000 10.150 532.000 \n",
"211 8.000 3.540 226.000 \n",
"210 575.000 21.880 2628.000 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# prediction-request-reasoning-claude\n",
"prediction_request_reasoning_claude = error[error['tool'] == 'prediction-request-reasoning-claude']\n",
"prediction_request_reasoning_claude = prediction_request_reasoning_claude.sort_values('request_month_year_week', ascending=False)\n",
"prediction_request_reasoning_claude.head()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" error | \n",
" tool | \n",
" request_month_year_week | \n",
" 0.000 | \n",
" 1.000 | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" \n",
" \n",
" 196 | \n",
" prediction-request-rag-claude | \n",
" 2024-05-20/2024-05-26 | \n",
" 1474.000 | \n",
" 230.000 | \n",
" 13.498 | \n",
" 1704.000 | \n",
"
\n",
" \n",
" 195 | \n",
" prediction-request-rag-claude | \n",
" 2024-05-13/2024-05-19 | \n",
" 2378.000 | \n",
" 274.000 | \n",
" 10.332 | \n",
" 2652.000 | \n",
"
\n",
" \n",
" 194 | \n",
" prediction-request-rag-claude | \n",
" 2024-05-06/2024-05-12 | \n",
" 2850.000 | \n",
" 777.000 | \n",
" 21.423 | \n",
" 3627.000 | \n",
"
\n",
" \n",
" 193 | \n",
" prediction-request-rag-claude | \n",
" 2024-04-29/2024-05-05 | \n",
" 1313.000 | \n",
" 8.000 | \n",
" 0.606 | \n",
" 1321.000 | \n",
"
\n",
" \n",
" 192 | \n",
" prediction-request-rag-claude | \n",
" 2024-04-22/2024-04-28 | \n",
" 1113.000 | \n",
" 345.000 | \n",
" 23.663 | \n",
" 1458.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error tool request_month_year_week 0.000 \\\n",
"196 prediction-request-rag-claude 2024-05-20/2024-05-26 1474.000 \n",
"195 prediction-request-rag-claude 2024-05-13/2024-05-19 2378.000 \n",
"194 prediction-request-rag-claude 2024-05-06/2024-05-12 2850.000 \n",
"193 prediction-request-rag-claude 2024-04-29/2024-05-05 1313.000 \n",
"192 prediction-request-rag-claude 2024-04-22/2024-04-28 1113.000 \n",
"\n",
"error 1.000 error_perc total_requests \n",
"196 230.000 13.498 1704.000 \n",
"195 274.000 10.332 2652.000 \n",
"194 777.000 21.423 3627.000 \n",
"193 8.000 0.606 1321.000 \n",
"192 345.000 23.663 1458.000 "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prediction_request_rag_claude = error[error['tool'] == 'prediction-request-rag-claude']\n",
"prediction_request_rag_claude = prediction_request_rag_claude.sort_values('request_month_year_week', ascending=False)\n",
"prediction_request_rag_claude.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" error | \n",
" tool | \n",
" request_month_year_week | \n",
" 0.000 | \n",
" 1.000 | \n",
" error_perc | \n",
" total_requests | \n",
"
\n",
" \n",
" \n",
" \n",
" 222 | \n",
" prediction-url-cot-claude | \n",
" 2024-05-20/2024-05-26 | \n",
" 293.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 293.000 | \n",
"
\n",
" \n",
" 221 | \n",
" prediction-url-cot-claude | \n",
" 2024-05-13/2024-05-19 | \n",
" 93.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 93.000 | \n",
"
\n",
" \n",
" 220 | \n",
" prediction-url-cot-claude | \n",
" 2024-05-06/2024-05-12 | \n",
" 225.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 225.000 | \n",
"
\n",
" \n",
" 219 | \n",
" prediction-url-cot-claude | \n",
" 2024-04-29/2024-05-05 | \n",
" 270.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 270.000 | \n",
"
\n",
" \n",
" 218 | \n",
" prediction-url-cot-claude | \n",
" 2024-04-22/2024-04-28 | \n",
" 1506.000 | \n",
" 65.000 | \n",
" 4.137 | \n",
" 1571.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"error tool request_month_year_week 0.000 1.000 \\\n",
"222 prediction-url-cot-claude 2024-05-20/2024-05-26 293.000 0.000 \n",
"221 prediction-url-cot-claude 2024-05-13/2024-05-19 93.000 0.000 \n",
"220 prediction-url-cot-claude 2024-05-06/2024-05-12 225.000 0.000 \n",
"219 prediction-url-cot-claude 2024-04-29/2024-05-05 270.000 0.000 \n",
"218 prediction-url-cot-claude 2024-04-22/2024-04-28 1506.000 65.000 \n",
"\n",
"error error_perc total_requests \n",
"222 0.000 293.000 \n",
"221 0.000 93.000 \n",
"220 0.000 225.000 \n",
"219 0.000 270.000 \n",
"218 4.137 1571.000 "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prediction_url_cot_claude = error[error['tool'] == 'prediction-url-cot-claude']\n",
"prediction_url_cot_claude = prediction_url_cot_claude.sort_values('request_month_year_week', ascending=False)\n",
"prediction_url_cot_claude.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'tools_inc' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m tools\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[43mtools_inc\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m error\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m error_total\n",
"\u001b[0;31mNameError\u001b[0m: name 'tools_inc' is not defined"
]
}
],
"source": [
"del tools\n",
"del tools_inc\n",
"del error\n",
"del error_total\n",
"\n",
"gc.collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Win analysis"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"# only include non error requests\n",
"tools_non_error = tools_inc[tools_inc['error'] != 1]\n",
"tools_non_error['currentAnswer'].replace('no', 'No', inplace=True)\n",
"tools_non_error['currentAnswer'].replace('yes', 'Yes', inplace=True)\n",
"tools_non_error = tools_non_error[tools_non_error['currentAnswer'].isin(['Yes', 'No'])]\n",
"tools_non_error = tools_non_error[tools_non_error['vote'].isin(['Yes', 'No'])]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"tools_non_error['win'] = tools_non_error['currentAnswer'] == tools_non_error['vote']\n",
"tools_non_error['win'] = tools_non_error['win'].astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"wins = tools_non_error.groupby(['tool', 'request_month_year_week', 'win']).size().unstack().fillna(0)\n",
"wins['win_perc'] = (wins[1] / (wins[0] + wins[1]))*100\n",
"wins.reset_index(inplace=True)\n",
"wins['total_request'] = wins[0] + wins[1]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['claude-prediction-offline', 'claude-prediction-online',\n",
" 'prediction-offline', 'prediction-offline-sme',\n",
" 'prediction-online', 'prediction-online-sme',\n",
" 'prediction-request-rag', 'prediction-request-rag-claude',\n",
" 'prediction-request-reasoning',\n",
" 'prediction-request-reasoning-claude', 'prediction-url-cot-claude'],\n",
" dtype=object)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wins['tool'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" win | \n",
" 0 | \n",
" 1 | \n",
" win_perc | \n",
" total_request | \n",
"
\n",
" \n",
" request_month_year_week | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 2023-07-17/2023-07-23 | \n",
" 1.000 | \n",
" 1.000 | \n",
" 50.000 | \n",
" 2.000 | \n",
"
\n",
" \n",
" 2023-07-24/2023-07-30 | \n",
" 12.000 | \n",
" 12.000 | \n",
" 50.000 | \n",
" 24.000 | \n",
"
\n",
" \n",
" 2023-07-31/2023-08-06 | \n",
" 360.000 | \n",
" 229.000 | \n",
" 41.979 | \n",
" 589.000 | \n",
"
\n",
" \n",
" 2023-08-07/2023-08-13 | \n",
" 177.000 | \n",
" 110.000 | \n",
" 44.123 | \n",
" 287.000 | \n",
"
\n",
" \n",
" 2023-08-14/2023-08-20 | \n",
" 784.000 | \n",
" 559.000 | \n",
" 41.623 | \n",
" 1343.000 | \n",
"
\n",
" \n",
" 2023-08-21/2023-08-27 | \n",
" 596.000 | \n",
" 502.000 | \n",
" 45.719 | \n",
" 1098.000 | \n",
"
\n",
" \n",
" 2023-08-28/2023-09-03 | \n",
" 958.000 | \n",
" 502.000 | \n",
" 34.384 | \n",
" 1460.000 | \n",
"
\n",
" \n",
" 2023-09-04/2023-09-10 | \n",
" 1609.000 | \n",
" 1418.000 | \n",
" 46.845 | \n",
" 3027.000 | \n",
"
\n",
" \n",
" 2023-09-11/2023-09-17 | \n",
" 1171.000 | \n",
" 1380.000 | \n",
" 54.096 | \n",
" 2551.000 | \n",
"
\n",
" \n",
" 2023-09-18/2023-09-24 | \n",
" 2150.000 | \n",
" 2307.000 | \n",
" 60.968 | \n",
" 4457.000 | \n",
"
\n",
" \n",
" 2023-09-25/2023-10-01 | \n",
" 992.000 | \n",
" 817.000 | \n",
" 47.635 | \n",
" 1809.000 | \n",
"
\n",
" \n",
" 2023-10-02/2023-10-08 | \n",
" 1625.000 | \n",
" 1842.000 | \n",
" 54.240 | \n",
" 3467.000 | \n",
"
\n",
" \n",
" 2023-10-09/2023-10-15 | \n",
" 1594.000 | \n",
" 2096.000 | \n",
" 57.281 | \n",
" 3690.000 | \n",
"
\n",
" \n",
" 2023-10-16/2023-10-22 | \n",
" 1291.000 | \n",
" 1623.000 | \n",
" 55.496 | \n",
" 2914.000 | \n",
"
\n",
" \n",
" 2023-10-23/2023-10-29 | \n",
" 1018.000 | \n",
" 1084.000 | \n",
" 50.802 | \n",
" 2102.000 | \n",
"
\n",
" \n",
" 2023-10-30/2023-11-05 | \n",
" 541.000 | \n",
" 825.000 | \n",
" 64.848 | \n",
" 1366.000 | \n",
"
\n",
" \n",
" 2023-11-06/2023-11-12 | \n",
" 1545.000 | \n",
" 1776.000 | \n",
" 69.014 | \n",
" 3321.000 | \n",
"
\n",
" \n",
" 2023-11-13/2023-11-19 | \n",
" 1825.000 | \n",
" 2056.000 | \n",
" 55.202 | \n",
" 3881.000 | \n",
"
\n",
" \n",
" 2023-11-20/2023-11-26 | \n",
" 1567.000 | \n",
" 1874.000 | \n",
" 58.482 | \n",
" 3441.000 | \n",
"
\n",
" \n",
" 2023-11-27/2023-12-03 | \n",
" 1555.000 | \n",
" 1773.000 | \n",
" 67.721 | \n",
" 3328.000 | \n",
"
\n",
" \n",
" 2023-12-04/2023-12-10 | \n",
" 1245.000 | \n",
" 1470.000 | \n",
" 33.705 | \n",
" 2715.000 | \n",
"
\n",
" \n",
" 2023-12-11/2023-12-17 | \n",
" 1462.000 | \n",
" 1788.000 | \n",
" 52.404 | \n",
" 3250.000 | \n",
"
\n",
" \n",
" 2023-12-18/2023-12-24 | \n",
" 1332.000 | \n",
" 1557.000 | \n",
" 46.687 | \n",
" 2889.000 | \n",
"
\n",
" \n",
" 2023-12-25/2023-12-31 | \n",
" 1397.000 | \n",
" 1257.000 | \n",
" 48.222 | \n",
" 2654.000 | \n",
"
\n",
" \n",
" 2024-01-01/2024-01-07 | \n",
" 2159.000 | \n",
" 1713.000 | \n",
" 43.436 | \n",
" 3872.000 | \n",
"
\n",
" \n",
" 2024-01-08/2024-01-14 | \n",
" 1034.000 | \n",
" 890.000 | \n",
" 41.597 | \n",
" 1924.000 | \n",
"
\n",
" \n",
" 2024-01-15/2024-01-21 | \n",
" 2228.000 | \n",
" 1758.000 | \n",
" 40.827 | \n",
" 3986.000 | \n",
"
\n",
" \n",
" 2024-01-22/2024-01-28 | \n",
" 2036.000 | \n",
" 1970.000 | \n",
" 31.617 | \n",
" 4006.000 | \n",
"
\n",
" \n",
" 2024-01-29/2024-02-04 | \n",
" 2303.000 | \n",
" 1791.000 | \n",
" 37.106 | \n",
" 4094.000 | \n",
"
\n",
" \n",
" 2024-02-05/2024-02-11 | \n",
" 2149.000 | \n",
" 2189.000 | \n",
" 49.808 | \n",
" 4338.000 | \n",
"
\n",
" \n",
" 2024-02-12/2024-02-18 | \n",
" 1979.000 | \n",
" 1956.000 | \n",
" 55.949 | \n",
" 3935.000 | \n",
"
\n",
" \n",
" 2024-02-19/2024-02-25 | \n",
" 1788.000 | \n",
" 2002.000 | \n",
" 57.697 | \n",
" 3790.000 | \n",
"
\n",
" \n",
" 2024-02-26/2024-03-03 | \n",
" 2299.000 | \n",
" 2350.000 | \n",
" 42.051 | \n",
" 4649.000 | \n",
"
\n",
" \n",
" 2024-03-04/2024-03-10 | \n",
" 4523.000 | \n",
" 3500.000 | \n",
" 44.989 | \n",
" 8023.000 | \n",
"
\n",
" \n",
" 2024-03-11/2024-03-17 | \n",
" 4516.000 | \n",
" 4705.000 | \n",
" 56.713 | \n",
" 9221.000 | \n",
"
\n",
" \n",
" 2024-03-18/2024-03-24 | \n",
" 5561.000 | \n",
" 5581.000 | \n",
" 52.903 | \n",
" 11142.000 | \n",
"
\n",
" \n",
" 2024-03-25/2024-03-31 | \n",
" 5200.000 | \n",
" 6965.000 | \n",
" 54.644 | \n",
" 12165.000 | \n",
"
\n",
" \n",
" 2024-04-01/2024-04-07 | \n",
" 2923.000 | \n",
" 4258.000 | \n",
" 61.323 | \n",
" 7181.000 | \n",
"
\n",
" \n",
" 2024-04-08/2024-04-14 | \n",
" 1331.000 | \n",
" 3412.000 | \n",
" 69.522 | \n",
" 4743.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win 0 1 win_perc total_request\n",
"request_month_year_week \n",
"2023-07-17/2023-07-23 1.000 1.000 50.000 2.000\n",
"2023-07-24/2023-07-30 12.000 12.000 50.000 24.000\n",
"2023-07-31/2023-08-06 360.000 229.000 41.979 589.000\n",
"2023-08-07/2023-08-13 177.000 110.000 44.123 287.000\n",
"2023-08-14/2023-08-20 784.000 559.000 41.623 1343.000\n",
"2023-08-21/2023-08-27 596.000 502.000 45.719 1098.000\n",
"2023-08-28/2023-09-03 958.000 502.000 34.384 1460.000\n",
"2023-09-04/2023-09-10 1609.000 1418.000 46.845 3027.000\n",
"2023-09-11/2023-09-17 1171.000 1380.000 54.096 2551.000\n",
"2023-09-18/2023-09-24 2150.000 2307.000 60.968 4457.000\n",
"2023-09-25/2023-10-01 992.000 817.000 47.635 1809.000\n",
"2023-10-02/2023-10-08 1625.000 1842.000 54.240 3467.000\n",
"2023-10-09/2023-10-15 1594.000 2096.000 57.281 3690.000\n",
"2023-10-16/2023-10-22 1291.000 1623.000 55.496 2914.000\n",
"2023-10-23/2023-10-29 1018.000 1084.000 50.802 2102.000\n",
"2023-10-30/2023-11-05 541.000 825.000 64.848 1366.000\n",
"2023-11-06/2023-11-12 1545.000 1776.000 69.014 3321.000\n",
"2023-11-13/2023-11-19 1825.000 2056.000 55.202 3881.000\n",
"2023-11-20/2023-11-26 1567.000 1874.000 58.482 3441.000\n",
"2023-11-27/2023-12-03 1555.000 1773.000 67.721 3328.000\n",
"2023-12-04/2023-12-10 1245.000 1470.000 33.705 2715.000\n",
"2023-12-11/2023-12-17 1462.000 1788.000 52.404 3250.000\n",
"2023-12-18/2023-12-24 1332.000 1557.000 46.687 2889.000\n",
"2023-12-25/2023-12-31 1397.000 1257.000 48.222 2654.000\n",
"2024-01-01/2024-01-07 2159.000 1713.000 43.436 3872.000\n",
"2024-01-08/2024-01-14 1034.000 890.000 41.597 1924.000\n",
"2024-01-15/2024-01-21 2228.000 1758.000 40.827 3986.000\n",
"2024-01-22/2024-01-28 2036.000 1970.000 31.617 4006.000\n",
"2024-01-29/2024-02-04 2303.000 1791.000 37.106 4094.000\n",
"2024-02-05/2024-02-11 2149.000 2189.000 49.808 4338.000\n",
"2024-02-12/2024-02-18 1979.000 1956.000 55.949 3935.000\n",
"2024-02-19/2024-02-25 1788.000 2002.000 57.697 3790.000\n",
"2024-02-26/2024-03-03 2299.000 2350.000 42.051 4649.000\n",
"2024-03-04/2024-03-10 4523.000 3500.000 44.989 8023.000\n",
"2024-03-11/2024-03-17 4516.000 4705.000 56.713 9221.000\n",
"2024-03-18/2024-03-24 5561.000 5581.000 52.903 11142.000\n",
"2024-03-25/2024-03-31 5200.000 6965.000 54.644 12165.000\n",
"2024-04-01/2024-04-07 2923.000 4258.000 61.323 7181.000\n",
"2024-04-08/2024-04-14 1331.000 3412.000 69.522 4743.000"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wins.groupby('request_month_year_week').agg({\n",
" 0: 'sum',\n",
" 1: 'sum',\n",
" 'win_perc': 'mean',\n",
" 'total_request': 'sum'\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
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" \n",
" \n",
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" 11 | \n",
" 2023-09-18/2023-09-24 | \n",
" 100.000 | \n",
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"
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" 12 | \n",
" 2023-09-25/2023-10-01 | \n",
" 58.333 | \n",
" 48.000 | \n",
"
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" 13 | \n",
" 2023-10-02/2023-10-08 | \n",
" 61.783 | \n",
" 157.000 | \n",
"
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" 14 | \n",
" 2023-10-09/2023-10-15 | \n",
" 60.588 | \n",
" 680.000 | \n",
"
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" 15 | \n",
" 2023-10-16/2023-10-22 | \n",
" 58.791 | \n",
" 364.000 | \n",
"
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" \n",
" 16 | \n",
" 2023-10-23/2023-10-29 | \n",
" 47.143 | \n",
" 70.000 | \n",
"
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" \n",
" 17 | \n",
" 2023-10-30/2023-11-05 | \n",
" 67.647 | \n",
" 34.000 | \n",
"
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" 18 | \n",
" 2023-11-20/2023-11-26 | \n",
" 100.000 | \n",
" 1.000 | \n",
"
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" 19 | \n",
" 2023-11-27/2023-12-03 | \n",
" 57.143 | \n",
" 7.000 | \n",
"
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" 20 | \n",
" 2023-12-04/2023-12-10 | \n",
" 66.667 | \n",
" 6.000 | \n",
"
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" 21 | \n",
" 2023-12-11/2023-12-17 | \n",
" 50.000 | \n",
" 2.000 | \n",
"
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" \n",
" 22 | \n",
" 2023-12-25/2023-12-31 | \n",
" 55.814 | \n",
" 43.000 | \n",
"
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" \n",
" 23 | \n",
" 2024-01-01/2024-01-07 | \n",
" 28.400 | \n",
" 250.000 | \n",
"
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" \n",
" 24 | \n",
" 2024-01-08/2024-01-14 | \n",
" 35.789 | \n",
" 190.000 | \n",
"
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" \n",
" 25 | \n",
" 2024-01-15/2024-01-21 | \n",
" 36.986 | \n",
" 292.000 | \n",
"
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" \n",
" 26 | \n",
" 2024-01-22/2024-01-28 | \n",
" 45.387 | \n",
" 271.000 | \n",
"
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" 27 | \n",
" 2024-01-29/2024-02-04 | \n",
" 29.555 | \n",
" 247.000 | \n",
"
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" 28 | \n",
" 2024-02-05/2024-02-11 | \n",
" 49.064 | \n",
" 267.000 | \n",
"
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" 29 | \n",
" 2024-02-12/2024-02-18 | \n",
" 63.300 | \n",
" 297.000 | \n",
"
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" \n",
" 30 | \n",
" 2024-02-19/2024-02-25 | \n",
" 65.362 | \n",
" 690.000 | \n",
"
\n",
" \n",
" 31 | \n",
" 2024-03-18/2024-03-24 | \n",
" 71.575 | \n",
" 781.000 | \n",
"
\n",
" \n",
" 32 | \n",
" 2024-03-25/2024-03-31 | \n",
" 69.052 | \n",
" 3648.000 | \n",
"
\n",
" \n",
" 33 | \n",
" 2024-04-01/2024-04-07 | \n",
" 60.991 | \n",
" 2402.000 | \n",
"
\n",
" \n",
" 34 | \n",
" 2024-04-08/2024-04-14 | \n",
" 62.205 | \n",
" 635.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"11 2023-09-18/2023-09-24 100.000 1.000\n",
"12 2023-09-25/2023-10-01 58.333 48.000\n",
"13 2023-10-02/2023-10-08 61.783 157.000\n",
"14 2023-10-09/2023-10-15 60.588 680.000\n",
"15 2023-10-16/2023-10-22 58.791 364.000\n",
"16 2023-10-23/2023-10-29 47.143 70.000\n",
"17 2023-10-30/2023-11-05 67.647 34.000\n",
"18 2023-11-20/2023-11-26 100.000 1.000\n",
"19 2023-11-27/2023-12-03 57.143 7.000\n",
"20 2023-12-04/2023-12-10 66.667 6.000\n",
"21 2023-12-11/2023-12-17 50.000 2.000\n",
"22 2023-12-25/2023-12-31 55.814 43.000\n",
"23 2024-01-01/2024-01-07 28.400 250.000\n",
"24 2024-01-08/2024-01-14 35.789 190.000\n",
"25 2024-01-15/2024-01-21 36.986 292.000\n",
"26 2024-01-22/2024-01-28 45.387 271.000\n",
"27 2024-01-29/2024-02-04 29.555 247.000\n",
"28 2024-02-05/2024-02-11 49.064 267.000\n",
"29 2024-02-12/2024-02-18 63.300 297.000\n",
"30 2024-02-19/2024-02-25 65.362 690.000\n",
"31 2024-03-18/2024-03-24 71.575 781.000\n",
"32 2024-03-25/2024-03-31 69.052 3648.000\n",
"33 2024-04-01/2024-04-07 60.991 2402.000\n",
"34 2024-04-08/2024-04-14 62.205 635.000"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select only claude-prediction-online and plot request_month_year_week vs win_perc\n",
"claude_prediction_online = wins[wins['tool'] == 'claude-prediction-online']\n",
"claude_prediction_online = claude_prediction_online[['request_month_year_week', 'win_perc', 'total_request']]\n",
"claude_prediction_online = claude_prediction_online.sort_values(by='request_month_year_week')\n",
"\n",
"claude_prediction_online.head()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2023-09-18/2023-09-24 | \n",
" 16.667 | \n",
" 6.000 | \n",
"
\n",
" \n",
" 1 | \n",
" 2023-09-25/2023-10-01 | \n",
" 53.205 | \n",
" 156.000 | \n",
"
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" \n",
" 2 | \n",
" 2023-10-02/2023-10-08 | \n",
" 53.333 | \n",
" 285.000 | \n",
"
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" \n",
" 3 | \n",
" 2023-10-09/2023-10-15 | \n",
" 60.477 | \n",
" 377.000 | \n",
"
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" \n",
" 4 | \n",
" 2023-10-16/2023-10-22 | \n",
" 57.854 | \n",
" 522.000 | \n",
"
\n",
" \n",
" 5 | \n",
" 2023-10-23/2023-10-29 | \n",
" 56.383 | \n",
" 376.000 | \n",
"
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" \n",
" 6 | \n",
" 2023-10-30/2023-11-05 | \n",
" 72.000 | \n",
" 75.000 | \n",
"
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" \n",
" 7 | \n",
" 2023-11-06/2023-11-12 | \n",
" 100.000 | \n",
" 1.000 | \n",
"
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" \n",
" 8 | \n",
" 2023-11-13/2023-11-19 | \n",
" 100.000 | \n",
" 2.000 | \n",
"
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" \n",
" 9 | \n",
" 2023-12-18/2023-12-24 | \n",
" 20.000 | \n",
" 5.000 | \n",
"
\n",
" \n",
" 10 | \n",
" 2024-03-25/2024-03-31 | \n",
" 100.000 | \n",
" 2.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"0 2023-09-18/2023-09-24 16.667 6.000\n",
"1 2023-09-25/2023-10-01 53.205 156.000\n",
"2 2023-10-02/2023-10-08 53.333 285.000\n",
"3 2023-10-09/2023-10-15 60.477 377.000\n",
"4 2023-10-16/2023-10-22 57.854 522.000\n",
"5 2023-10-23/2023-10-29 56.383 376.000\n",
"6 2023-10-30/2023-11-05 72.000 75.000\n",
"7 2023-11-06/2023-11-12 100.000 1.000\n",
"8 2023-11-13/2023-11-19 100.000 2.000\n",
"9 2023-12-18/2023-12-24 20.000 5.000\n",
"10 2024-03-25/2024-03-31 100.000 2.000"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select only claude-prediction-offline and plot request_month_year_week vs win_perc\n",
"claude_prediction_offline = wins[wins['tool'] == 'claude-prediction-offline']\n",
"claude_prediction_offline = claude_prediction_offline[['request_month_year_week', 'win_perc', 'total_request']]\n",
"claude_prediction_offline = claude_prediction_offline.sort_values(by='request_month_year_week')\n",
"\n",
"claude_prediction_offline.head()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
\n",
" \n",
" \n",
" \n",
" 72 | \n",
" 2023-07-17/2023-07-23 | \n",
" 50.000 | \n",
" 2.000 | \n",
"
\n",
" \n",
" 73 | \n",
" 2023-07-24/2023-07-30 | \n",
" 50.000 | \n",
" 24.000 | \n",
"
\n",
" \n",
" 74 | \n",
" 2023-07-31/2023-08-06 | \n",
" 38.306 | \n",
" 543.000 | \n",
"
\n",
" \n",
" 75 | \n",
" 2023-08-07/2023-08-13 | \n",
" 38.246 | \n",
" 285.000 | \n",
"
\n",
" \n",
" 76 | \n",
" 2023-08-14/2023-08-20 | \n",
" 41.623 | \n",
" 1343.000 | \n",
"
\n",
" \n",
" 77 | \n",
" 2023-08-21/2023-08-27 | \n",
" 45.719 | \n",
" 1098.000 | \n",
"
\n",
" \n",
" 78 | \n",
" 2023-08-28/2023-09-03 | \n",
" 34.384 | \n",
" 1460.000 | \n",
"
\n",
" \n",
" 79 | \n",
" 2023-09-04/2023-09-10 | \n",
" 46.845 | \n",
" 3027.000 | \n",
"
\n",
" \n",
" 80 | \n",
" 2023-09-11/2023-09-17 | \n",
" 54.096 | \n",
" 2551.000 | \n",
"
\n",
" \n",
" 81 | \n",
" 2023-09-18/2023-09-24 | \n",
" 51.602 | \n",
" 4246.000 | \n",
"
\n",
" \n",
" 82 | \n",
" 2023-09-25/2023-10-01 | \n",
" 43.876 | \n",
" 743.000 | \n",
"
\n",
" \n",
" 83 | \n",
" 2023-10-02/2023-10-08 | \n",
" 50.538 | \n",
" 837.000 | \n",
"
\n",
" \n",
" 84 | \n",
" 2023-10-09/2023-10-15 | \n",
" 50.976 | \n",
" 973.000 | \n",
"
\n",
" \n",
" 85 | \n",
" 2023-10-16/2023-10-22 | \n",
" 56.146 | \n",
" 903.000 | \n",
"
\n",
" \n",
" 86 | \n",
" 2023-10-23/2023-10-29 | \n",
" 48.822 | \n",
" 594.000 | \n",
"
\n",
" \n",
" 87 | \n",
" 2023-10-30/2023-11-05 | \n",
" 60.392 | \n",
" 664.000 | \n",
"
\n",
" \n",
" 88 | \n",
" 2023-11-06/2023-11-12 | \n",
" 52.533 | \n",
" 1757.000 | \n",
"
\n",
" \n",
" 89 | \n",
" 2023-11-13/2023-11-19 | \n",
" 53.892 | \n",
" 2004.000 | \n",
"
\n",
" \n",
" 90 | \n",
" 2023-11-20/2023-11-26 | \n",
" 53.202 | \n",
" 1780.000 | \n",
"
\n",
" \n",
" 91 | \n",
" 2023-11-27/2023-12-03 | \n",
" 54.253 | \n",
" 1058.000 | \n",
"
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" \n",
" 92 | \n",
" 2023-12-04/2023-12-10 | \n",
" 47.500 | \n",
" 80.000 | \n",
"
\n",
" \n",
" 93 | \n",
" 2023-12-11/2023-12-17 | \n",
" 52.174 | \n",
" 23.000 | \n",
"
\n",
" \n",
" 94 | \n",
" 2023-12-18/2023-12-24 | \n",
" 69.863 | \n",
" 73.000 | \n",
"
\n",
" \n",
" 95 | \n",
" 2023-12-25/2023-12-31 | \n",
" 41.509 | \n",
" 53.000 | \n",
"
\n",
" \n",
" 96 | \n",
" 2024-01-01/2024-01-07 | \n",
" 0.000 | \n",
" 2.000 | \n",
"
\n",
" \n",
" 97 | \n",
" 2024-01-22/2024-01-28 | \n",
" 0.000 | \n",
" 1.000 | \n",
"
\n",
" \n",
" 98 | \n",
" 2024-03-25/2024-03-31 | \n",
" 0.000 | \n",
" 1.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"72 2023-07-17/2023-07-23 50.000 2.000\n",
"73 2023-07-24/2023-07-30 50.000 24.000\n",
"74 2023-07-31/2023-08-06 38.306 543.000\n",
"75 2023-08-07/2023-08-13 38.246 285.000\n",
"76 2023-08-14/2023-08-20 41.623 1343.000\n",
"77 2023-08-21/2023-08-27 45.719 1098.000\n",
"78 2023-08-28/2023-09-03 34.384 1460.000\n",
"79 2023-09-04/2023-09-10 46.845 3027.000\n",
"80 2023-09-11/2023-09-17 54.096 2551.000\n",
"81 2023-09-18/2023-09-24 51.602 4246.000\n",
"82 2023-09-25/2023-10-01 43.876 743.000\n",
"83 2023-10-02/2023-10-08 50.538 837.000\n",
"84 2023-10-09/2023-10-15 50.976 973.000\n",
"85 2023-10-16/2023-10-22 56.146 903.000\n",
"86 2023-10-23/2023-10-29 48.822 594.000\n",
"87 2023-10-30/2023-11-05 60.392 664.000\n",
"88 2023-11-06/2023-11-12 52.533 1757.000\n",
"89 2023-11-13/2023-11-19 53.892 2004.000\n",
"90 2023-11-20/2023-11-26 53.202 1780.000\n",
"91 2023-11-27/2023-12-03 54.253 1058.000\n",
"92 2023-12-04/2023-12-10 47.500 80.000\n",
"93 2023-12-11/2023-12-17 52.174 23.000\n",
"94 2023-12-18/2023-12-24 69.863 73.000\n",
"95 2023-12-25/2023-12-31 41.509 53.000\n",
"96 2024-01-01/2024-01-07 0.000 2.000\n",
"97 2024-01-22/2024-01-28 0.000 1.000\n",
"98 2024-03-25/2024-03-31 0.000 1.000"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select only prediction-online and plot request_month_year_week vs win_perc\n",
"prediction_online = wins[wins['tool'] == 'prediction-online']\n",
"prediction_online = prediction_online[['request_month_year_week', 'win_perc', 'total_request']]\n",
"prediction_online = prediction_online.sort_values(by='request_month_year_week')\n",
"\n",
"prediction_online.head()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
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" \n",
" \n",
" \n",
" 35 | \n",
" 2023-07-31/2023-08-06 | \n",
" 45.652 | \n",
" 46.000 | \n",
"
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" 36 | \n",
" 2023-08-07/2023-08-13 | \n",
" 50.000 | \n",
" 2.000 | \n",
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" 37 | \n",
" 2023-09-18/2023-09-24 | \n",
" 51.128 | \n",
" 133.000 | \n",
"
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" 38 | \n",
" 2023-09-25/2023-10-01 | \n",
" 36.864 | \n",
" 236.000 | \n",
"
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" \n",
" 39 | \n",
" 2023-10-02/2023-10-08 | \n",
" 50.077 | \n",
" 651.000 | \n",
"
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" \n",
" 40 | \n",
" 2023-10-09/2023-10-15 | \n",
" 52.392 | \n",
" 418.000 | \n",
"
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" \n",
" 41 | \n",
" 2023-10-16/2023-10-22 | \n",
" 52.658 | \n",
" 395.000 | \n",
"
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" \n",
" 42 | \n",
" 2023-10-23/2023-10-29 | \n",
" 45.503 | \n",
" 189.000 | \n",
"
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" 43 | \n",
" 2023-10-30/2023-11-05 | \n",
" 75.000 | \n",
" 40.000 | \n",
"
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" \n",
" 44 | \n",
" 2023-11-13/2023-11-19 | \n",
" 50.000 | \n",
" 2.000 | \n",
"
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" \n",
" 45 | \n",
" 2023-11-20/2023-11-26 | \n",
" 33.333 | \n",
" 3.000 | \n",
"
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" \n",
" 46 | \n",
" 2023-11-27/2023-12-03 | \n",
" 88.235 | \n",
" 17.000 | \n",
"
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" \n",
" 47 | \n",
" 2023-12-04/2023-12-10 | \n",
" 0.000 | \n",
" 1.000 | \n",
"
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" \n",
" 48 | \n",
" 2023-12-18/2023-12-24 | \n",
" 50.000 | \n",
" 6.000 | \n",
"
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" \n",
" 49 | \n",
" 2024-01-01/2024-01-07 | \n",
" 100.000 | \n",
" 1.000 | \n",
"
\n",
" \n",
" 50 | \n",
" 2024-03-11/2024-03-17 | \n",
" 62.808 | \n",
" 406.000 | \n",
"
\n",
" \n",
" 51 | \n",
" 2024-03-18/2024-03-24 | \n",
" 54.453 | \n",
" 2448.000 | \n",
"
\n",
" \n",
" 52 | \n",
" 2024-03-25/2024-03-31 | \n",
" 58.729 | \n",
" 2360.000 | \n",
"
\n",
" \n",
" 53 | \n",
" 2024-04-01/2024-04-07 | \n",
" 57.055 | \n",
" 652.000 | \n",
"
\n",
" \n",
" 54 | \n",
" 2024-04-08/2024-04-14 | \n",
" 75.641 | \n",
" 468.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"35 2023-07-31/2023-08-06 45.652 46.000\n",
"36 2023-08-07/2023-08-13 50.000 2.000\n",
"37 2023-09-18/2023-09-24 51.128 133.000\n",
"38 2023-09-25/2023-10-01 36.864 236.000\n",
"39 2023-10-02/2023-10-08 50.077 651.000\n",
"40 2023-10-09/2023-10-15 52.392 418.000\n",
"41 2023-10-16/2023-10-22 52.658 395.000\n",
"42 2023-10-23/2023-10-29 45.503 189.000\n",
"43 2023-10-30/2023-11-05 75.000 40.000\n",
"44 2023-11-13/2023-11-19 50.000 2.000\n",
"45 2023-11-20/2023-11-26 33.333 3.000\n",
"46 2023-11-27/2023-12-03 88.235 17.000\n",
"47 2023-12-04/2023-12-10 0.000 1.000\n",
"48 2023-12-18/2023-12-24 50.000 6.000\n",
"49 2024-01-01/2024-01-07 100.000 1.000\n",
"50 2024-03-11/2024-03-17 62.808 406.000\n",
"51 2024-03-18/2024-03-24 54.453 2448.000\n",
"52 2024-03-25/2024-03-31 58.729 2360.000\n",
"53 2024-04-01/2024-04-07 57.055 652.000\n",
"54 2024-04-08/2024-04-14 75.641 468.000"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select only prediction-offline and plot request_month_year_week vs win_perc\n",
"prediction_offline = wins[wins['tool'] == 'prediction-offline']\n",
"prediction_offline = prediction_offline[['request_month_year_week', 'win_perc', 'total_request']]\n",
"prediction_offline = prediction_offline.sort_values(by='request_month_year_week')\n",
"\n",
"prediction_offline"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
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" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
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" \n",
" \n",
" \n",
" 55 | \n",
" 2023-09-18/2023-09-24 | \n",
" 83.333 | \n",
" 6.000 | \n",
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" 56 | \n",
" 2023-09-25/2023-10-01 | \n",
" 45.545 | \n",
" 303.000 | \n",
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" 57 | \n",
" 2023-10-02/2023-10-08 | \n",
" 54.208 | \n",
" 701.000 | \n",
"
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" 58 | \n",
" 2023-10-09/2023-10-15 | \n",
" 58.883 | \n",
" 591.000 | \n",
"
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" \n",
" 59 | \n",
" 2023-10-16/2023-10-22 | \n",
" 54.407 | \n",
" 329.000 | \n",
"
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" \n",
" 60 | \n",
" 2023-10-23/2023-10-29 | \n",
" 51.064 | \n",
" 517.000 | \n",
"
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" \n",
" 61 | \n",
" 2023-10-30/2023-11-05 | \n",
" 60.265 | \n",
" 302.000 | \n",
"
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" \n",
" 62 | \n",
" 2023-11-13/2023-11-19 | \n",
" 20.000 | \n",
" 10.000 | \n",
"
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" \n",
" 63 | \n",
" 2023-11-20/2023-11-26 | \n",
" 50.000 | \n",
" 14.000 | \n",
"
\n",
" \n",
" 64 | \n",
" 2023-11-27/2023-12-03 | \n",
" 86.667 | \n",
" 15.000 | \n",
"
\n",
" \n",
" 65 | \n",
" 2023-12-04/2023-12-10 | \n",
" 0.000 | \n",
" 1.000 | \n",
"
\n",
" \n",
" 66 | \n",
" 2023-12-18/2023-12-24 | \n",
" 40.000 | \n",
" 5.000 | \n",
"
\n",
" \n",
" 67 | \n",
" 2024-03-11/2024-03-17 | \n",
" 60.947 | \n",
" 169.000 | \n",
"
\n",
" \n",
" 68 | \n",
" 2024-03-18/2024-03-24 | \n",
" 44.016 | \n",
" 493.000 | \n",
"
\n",
" \n",
" 69 | \n",
" 2024-03-25/2024-03-31 | \n",
" 60.000 | \n",
" 10.000 | \n",
"
\n",
" \n",
" 70 | \n",
" 2024-04-01/2024-04-07 | \n",
" 61.039 | \n",
" 77.000 | \n",
"
\n",
" \n",
" 71 | \n",
" 2024-04-08/2024-04-14 | \n",
" 50.000 | \n",
" 2.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"55 2023-09-18/2023-09-24 83.333 6.000\n",
"56 2023-09-25/2023-10-01 45.545 303.000\n",
"57 2023-10-02/2023-10-08 54.208 701.000\n",
"58 2023-10-09/2023-10-15 58.883 591.000\n",
"59 2023-10-16/2023-10-22 54.407 329.000\n",
"60 2023-10-23/2023-10-29 51.064 517.000\n",
"61 2023-10-30/2023-11-05 60.265 302.000\n",
"62 2023-11-13/2023-11-19 20.000 10.000\n",
"63 2023-11-20/2023-11-26 50.000 14.000\n",
"64 2023-11-27/2023-12-03 86.667 15.000\n",
"65 2023-12-04/2023-12-10 0.000 1.000\n",
"66 2023-12-18/2023-12-24 40.000 5.000\n",
"67 2024-03-11/2024-03-17 60.947 169.000\n",
"68 2024-03-18/2024-03-24 44.016 493.000\n",
"69 2024-03-25/2024-03-31 60.000 10.000\n",
"70 2024-04-01/2024-04-07 61.039 77.000\n",
"71 2024-04-08/2024-04-14 50.000 2.000"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select only prediction-offline-sme and plot request_month_year_week vs win_perc\n",
"prediction_offline_sme = wins[wins['tool'] == 'prediction-offline-sme']\n",
"prediction_offline_sme = prediction_offline_sme[['request_month_year_week', 'win_perc', 'total_request']]\n",
"prediction_offline_sme = prediction_offline_sme.sort_values(by='request_month_year_week')\n",
"\n",
"prediction_offline_sme"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
\n",
" \n",
" \n",
" \n",
" 99 | \n",
" 2023-09-18/2023-09-24 | \n",
" 63.077 | \n",
" 65.000 | \n",
"
\n",
" \n",
" 100 | \n",
" 2023-09-25/2023-10-01 | \n",
" 47.988 | \n",
" 323.000 | \n",
"
\n",
" \n",
" 101 | \n",
" 2023-10-02/2023-10-08 | \n",
" 55.502 | \n",
" 836.000 | \n",
"
\n",
" \n",
" 102 | \n",
" 2023-10-09/2023-10-15 | \n",
" 60.369 | \n",
" 651.000 | \n",
"
\n",
" \n",
" 103 | \n",
" 2023-10-16/2023-10-22 | \n",
" 53.117 | \n",
" 401.000 | \n",
"
\n",
" \n",
" 104 | \n",
" 2023-10-23/2023-10-29 | \n",
" 55.899 | \n",
" 356.000 | \n",
"
\n",
" \n",
" 105 | \n",
" 2023-10-30/2023-11-05 | \n",
" 53.785 | \n",
" 251.000 | \n",
"
\n",
" \n",
" 106 | \n",
" 2023-11-06/2023-11-12 | \n",
" 54.511 | \n",
" 1563.000 | \n",
"
\n",
" \n",
" 107 | \n",
" 2023-11-13/2023-11-19 | \n",
" 52.120 | \n",
" 1863.000 | \n",
"
\n",
" \n",
" 108 | \n",
" 2023-11-20/2023-11-26 | \n",
" 55.873 | \n",
" 1643.000 | \n",
"
\n",
" \n",
" 109 | \n",
" 2023-11-27/2023-12-03 | \n",
" 52.308 | \n",
" 2231.000 | \n",
"
\n",
" \n",
" 110 | \n",
" 2023-12-04/2023-12-10 | \n",
" 54.359 | \n",
" 2627.000 | \n",
"
\n",
" \n",
" 111 | \n",
" 2023-12-11/2023-12-17 | \n",
" 55.039 | \n",
" 3225.000 | \n",
"
\n",
" \n",
" 112 | \n",
" 2023-12-18/2023-12-24 | \n",
" 53.571 | \n",
" 2800.000 | \n",
"
\n",
" \n",
" 113 | \n",
" 2023-12-25/2023-12-31 | \n",
" 47.342 | \n",
" 2558.000 | \n",
"
\n",
" \n",
" 114 | \n",
" 2024-01-01/2024-01-07 | \n",
" 45.344 | \n",
" 3619.000 | \n",
"
\n",
" \n",
" 115 | \n",
" 2024-01-08/2024-01-14 | \n",
" 47.405 | \n",
" 1734.000 | \n",
"
\n",
" \n",
" 116 | \n",
" 2024-01-15/2024-01-21 | \n",
" 44.667 | \n",
" 3694.000 | \n",
"
\n",
" \n",
" 117 | \n",
" 2024-01-22/2024-01-28 | \n",
" 49.464 | \n",
" 3734.000 | \n",
"
\n",
" \n",
" 118 | \n",
" 2024-01-29/2024-02-04 | \n",
" 44.658 | \n",
" 3847.000 | \n",
"
\n",
" \n",
" 119 | \n",
" 2024-02-05/2024-02-11 | \n",
" 50.553 | \n",
" 4071.000 | \n",
"
\n",
" \n",
" 120 | \n",
" 2024-02-12/2024-02-18 | \n",
" 48.598 | \n",
" 3638.000 | \n",
"
\n",
" \n",
" 121 | \n",
" 2024-02-19/2024-02-25 | \n",
" 50.032 | \n",
" 3100.000 | \n",
"
\n",
" \n",
" 122 | \n",
" 2024-02-26/2024-03-03 | \n",
" 51.717 | \n",
" 4368.000 | \n",
"
\n",
" \n",
" 123 | \n",
" 2024-03-04/2024-03-10 | \n",
" 54.806 | \n",
" 3454.000 | \n",
"
\n",
" \n",
" 124 | \n",
" 2024-03-11/2024-03-17 | \n",
" 55.848 | \n",
" 3044.000 | \n",
"
\n",
" \n",
" 125 | \n",
" 2024-03-18/2024-03-24 | \n",
" 48.639 | \n",
" 2535.000 | \n",
"
\n",
" \n",
" 126 | \n",
" 2024-03-25/2024-03-31 | \n",
" 41.345 | \n",
" 1398.000 | \n",
"
\n",
" \n",
" 127 | \n",
" 2024-04-01/2024-04-07 | \n",
" 59.435 | \n",
" 1097.000 | \n",
"
\n",
" \n",
" 128 | \n",
" 2024-04-08/2024-04-14 | \n",
" 68.281 | \n",
" 413.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"99 2023-09-18/2023-09-24 63.077 65.000\n",
"100 2023-09-25/2023-10-01 47.988 323.000\n",
"101 2023-10-02/2023-10-08 55.502 836.000\n",
"102 2023-10-09/2023-10-15 60.369 651.000\n",
"103 2023-10-16/2023-10-22 53.117 401.000\n",
"104 2023-10-23/2023-10-29 55.899 356.000\n",
"105 2023-10-30/2023-11-05 53.785 251.000\n",
"106 2023-11-06/2023-11-12 54.511 1563.000\n",
"107 2023-11-13/2023-11-19 52.120 1863.000\n",
"108 2023-11-20/2023-11-26 55.873 1643.000\n",
"109 2023-11-27/2023-12-03 52.308 2231.000\n",
"110 2023-12-04/2023-12-10 54.359 2627.000\n",
"111 2023-12-11/2023-12-17 55.039 3225.000\n",
"112 2023-12-18/2023-12-24 53.571 2800.000\n",
"113 2023-12-25/2023-12-31 47.342 2558.000\n",
"114 2024-01-01/2024-01-07 45.344 3619.000\n",
"115 2024-01-08/2024-01-14 47.405 1734.000\n",
"116 2024-01-15/2024-01-21 44.667 3694.000\n",
"117 2024-01-22/2024-01-28 49.464 3734.000\n",
"118 2024-01-29/2024-02-04 44.658 3847.000\n",
"119 2024-02-05/2024-02-11 50.553 4071.000\n",
"120 2024-02-12/2024-02-18 48.598 3638.000\n",
"121 2024-02-19/2024-02-25 50.032 3100.000\n",
"122 2024-02-26/2024-03-03 51.717 4368.000\n",
"123 2024-03-04/2024-03-10 54.806 3454.000\n",
"124 2024-03-11/2024-03-17 55.848 3044.000\n",
"125 2024-03-18/2024-03-24 48.639 2535.000\n",
"126 2024-03-25/2024-03-31 41.345 1398.000\n",
"127 2024-04-01/2024-04-07 59.435 1097.000\n",
"128 2024-04-08/2024-04-14 68.281 413.000"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select only prediction-online-sme and plot request_month_year_week vs win_perc\n",
"prediction_online_sme = wins[wins['tool'] == 'prediction-online-sme']\n",
"prediction_online_sme = prediction_online_sme[['request_month_year_week', 'win_perc', 'total_request']]\n",
"prediction_online_sme = prediction_online_sme.sort_values(by='request_month_year_week')\n",
"\n",
"prediction_online_sme.head()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
\n",
" \n",
" \n",
" \n",
" 129 | \n",
" 2024-02-26/2024-03-03 | \n",
" 32.384 | \n",
" 281.000 | \n",
"
\n",
" \n",
" 130 | \n",
" 2024-03-04/2024-03-10 | \n",
" 35.172 | \n",
" 4569.000 | \n",
"
\n",
" \n",
" 131 | \n",
" 2024-03-11/2024-03-17 | \n",
" 47.251 | \n",
" 5602.000 | \n",
"
\n",
" \n",
" 132 | \n",
" 2024-03-18/2024-03-24 | \n",
" 45.834 | \n",
" 4885.000 | \n",
"
\n",
" \n",
" 133 | \n",
" 2024-03-25/2024-03-31 | \n",
" 51.273 | \n",
" 4006.000 | \n",
"
\n",
" \n",
" 134 | \n",
" 2024-04-01/2024-04-07 | \n",
" 57.021 | \n",
" 1289.000 | \n",
"
\n",
" \n",
" 135 | \n",
" 2024-04-08/2024-04-14 | \n",
" 70.376 | \n",
" 692.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"129 2024-02-26/2024-03-03 32.384 281.000\n",
"130 2024-03-04/2024-03-10 35.172 4569.000\n",
"131 2024-03-11/2024-03-17 47.251 5602.000\n",
"132 2024-03-18/2024-03-24 45.834 4885.000\n",
"133 2024-03-25/2024-03-31 51.273 4006.000\n",
"134 2024-04-01/2024-04-07 57.021 1289.000\n",
"135 2024-04-08/2024-04-14 70.376 692.000"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select only prediction-request-rag and plot request_month_year_week vs win_perc\n",
"prediction_request_rag = wins[wins['tool'] == 'prediction-request-rag']\n",
"prediction_request_rag = prediction_request_rag[['request_month_year_week', 'win_perc', 'total_request']]\n",
"prediction_request_rag = prediction_request_rag.sort_values(by='request_month_year_week')\n",
"\n",
"prediction_request_rag.head()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
\n",
" \n",
" \n",
" \n",
" 138 | \n",
" 2024-03-25/2024-03-31 | \n",
" 56.757 | \n",
" 740.000 | \n",
"
\n",
" \n",
" 139 | \n",
" 2024-04-01/2024-04-07 | \n",
" 58.025 | \n",
" 1458.000 | \n",
"
\n",
" \n",
" 140 | \n",
" 2024-04-08/2024-04-14 | \n",
" 73.679 | \n",
" 1003.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"138 2024-03-25/2024-03-31 56.757 740.000\n",
"139 2024-04-01/2024-04-07 58.025 1458.000\n",
"140 2024-04-08/2024-04-14 73.679 1003.000"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prediction_request_reasoning = wins[wins['tool'] == 'prediction-request-reasoning']\n",
"prediction_request_reasoning = prediction_request_reasoning[['request_month_year_week', 'win_perc', 'total_request']]\n",
"prediction_request_reasoning = prediction_request_reasoning.sort_values(by='request_month_year_week')\n",
"\n",
"prediction_request_reasoning"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
\n",
" \n",
" \n",
" \n",
" 141 | \n",
" 2024-04-01/2024-04-07 | \n",
" 68.387 | \n",
" 155.000 | \n",
"
\n",
" \n",
" 142 | \n",
" 2024-04-08/2024-04-14 | \n",
" 78.514 | \n",
" 619.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"141 2024-04-01/2024-04-07 68.387 155.000\n",
"142 2024-04-08/2024-04-14 78.514 619.000"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prediction_request_reasoning_claude = wins[wins['tool'] == 'prediction-request-reasoning-claude']\n",
"prediction_request_reasoning_claude = prediction_request_reasoning_claude[['request_month_year_week', 'win_perc', 'total_request']]\n",
"prediction_request_reasoning_claude = prediction_request_reasoning_claude.sort_values(by='request_month_year_week')\n",
"\n",
"prediction_request_reasoning_claude"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
\n",
" \n",
" \n",
" \n",
" 136 | \n",
" 2024-04-01/2024-04-07 | \n",
" 68.627 | \n",
" 51.000 | \n",
"
\n",
" \n",
" 137 | \n",
" 2024-04-08/2024-04-14 | \n",
" 74.184 | \n",
" 337.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"136 2024-04-01/2024-04-07 68.627 51.000\n",
"137 2024-04-08/2024-04-14 74.184 337.000"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prediction_request_rag_claude = wins[wins['tool'] == 'prediction-request-rag-claude']\n",
"prediction_request_rag_claude = prediction_request_rag_claude[['request_month_year_week', 'win_perc', 'total_request']]\n",
"prediction_request_rag_claude = prediction_request_rag_claude.sort_values(by='request_month_year_week')\n",
"\n",
"prediction_request_rag_claude"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" win | \n",
" request_month_year_week | \n",
" win_perc | \n",
" total_request | \n",
"
\n",
" \n",
" \n",
" \n",
" 143 | \n",
" 2024-04-08/2024-04-14 | \n",
" 72.822 | \n",
" 574.000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"win request_month_year_week win_perc total_request\n",
"143 2024-04-08/2024-04-14 72.822 574.000"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prediction_url_cot_claude = wins[wins['tool'] == 'prediction-url-cot-claude']\n",
"prediction_url_cot_claude = prediction_url_cot_claude[['request_month_year_week', 'win_perc', 'total_request']]\n",
"prediction_url_cot_claude = prediction_url_cot_claude.sort_values(by='request_month_year_week')\n",
"\n",
"prediction_url_cot_claude.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Profitability analysis"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"all_trades['creation_timestamp'] = pd.to_datetime(all_trades['creation_timestamp'])\n",
"all_trades = all_trades[all_trades['current_answer'].isin([0., 1., -1.])].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of traders: 184\n",
"Number of trades: 18,941\n"
]
}
],
"source": [
"print(f\"Number of traders: {len(summary_traders):,}\")\n",
"print(f\"Number of trades: {all_trades['trade_id'].nunique():,}\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Number of trades')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
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