cyberosa
commited on
Commit
Β·
c5bbc45
1
Parent(s):
efbada6
updating notebooks
Browse files
.DS_Store
DELETED
Binary file (6.15 kB)
|
|
.gitignore
CHANGED
@@ -3,6 +3,8 @@ __pycache__/
|
|
3 |
*.py[cod]
|
4 |
*$py.class
|
5 |
|
|
|
|
|
6 |
# C extensions
|
7 |
*.so
|
8 |
|
@@ -157,4 +159,4 @@ cython_debug/
|
|
157 |
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
|
158 |
# and can be added to the global gitignore or merged into this file. For a more nuclear
|
159 |
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
|
160 |
-
#.idea/
|
|
|
3 |
*.py[cod]
|
4 |
*$py.class
|
5 |
|
6 |
+
.DS_Store
|
7 |
+
|
8 |
# C extensions
|
9 |
*.so
|
10 |
|
|
|
159 |
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
|
160 |
# and can be added to the global gitignore or merged into this file. For a more nuclear
|
161 |
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
|
162 |
+
#.idea/
|
analysis.ipynb β notebooks/analysis.ipynb
RENAMED
File without changes
|
increase_zero_mech_calls.ipynb β notebooks/increase_zero_mech_calls.ipynb
RENAMED
File without changes
|
{nbs β notebooks}/test.ipynb
RENAMED
File without changes
|
{nbs β notebooks}/weekly_analysis.ipynb
RENAMED
@@ -52,7 +52,7 @@
|
|
52 |
},
|
53 |
{
|
54 |
"cell_type": "code",
|
55 |
-
"execution_count":
|
56 |
"metadata": {},
|
57 |
"outputs": [
|
58 |
{
|
@@ -60,31 +60,31 @@
|
|
60 |
"output_type": "stream",
|
61 |
"text": [
|
62 |
"<class 'pandas.core.frame.DataFrame'>\n",
|
63 |
-
"RangeIndex:
|
64 |
"Data columns (total 19 columns):\n",
|
65 |
" # Column Non-Null Count Dtype \n",
|
66 |
"--- ------ -------------- ----- \n",
|
67 |
-
" 0 trader_address
|
68 |
-
" 1 trade_id
|
69 |
-
" 2 creation_timestamp
|
70 |
-
" 3 title
|
71 |
-
" 4 market_status
|
72 |
-
" 5 collateral_amount
|
73 |
-
" 6 outcome_index
|
74 |
-
" 7 trade_fee_amount
|
75 |
-
" 8 outcomes_tokens_traded
|
76 |
-
" 9 current_answer
|
77 |
-
" 10 is_invalid
|
78 |
-
" 11 winning_trade
|
79 |
-
" 12 earnings
|
80 |
-
" 13 redeemed
|
81 |
-
" 14 redeemed_amount
|
82 |
-
" 15 num_mech_calls
|
83 |
-
" 16 mech_fee_amount
|
84 |
-
" 17 net_earnings
|
85 |
-
" 18 roi
|
86 |
"dtypes: bool(3), datetime64[ns, UTC](1), float64(8), int64(2), object(5)\n",
|
87 |
-
"memory usage: 11.
|
88 |
]
|
89 |
}
|
90 |
],
|
@@ -94,7 +94,7 @@
|
|
94 |
},
|
95 |
{
|
96 |
"cell_type": "code",
|
97 |
-
"execution_count":
|
98 |
"metadata": {},
|
99 |
"outputs": [
|
100 |
{
|
@@ -103,7 +103,7 @@
|
|
103 |
"Timestamp('2023-07-12 15:17:25+0000', tz='UTC')"
|
104 |
]
|
105 |
},
|
106 |
-
"execution_count":
|
107 |
"metadata": {},
|
108 |
"output_type": "execute_result"
|
109 |
}
|
@@ -112,6 +112,26 @@
|
|
112 |
"all_trades.creation_timestamp.min()"
|
113 |
]
|
114 |
},
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
115 |
{
|
116 |
"cell_type": "code",
|
117 |
"execution_count": 4,
|
@@ -2363,7 +2383,7 @@
|
|
2363 |
"claude_prediction_online = claude_prediction_online[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
2364 |
"claude_prediction_online = claude_prediction_online.sort_values(by='request_month_year_week')\n",
|
2365 |
"\n",
|
2366 |
-
"claude_prediction_online"
|
2367 |
]
|
2368 |
},
|
2369 |
{
|
@@ -2494,7 +2514,7 @@
|
|
2494 |
"claude_prediction_offline = claude_prediction_offline[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
2495 |
"claude_prediction_offline = claude_prediction_offline.sort_values(by='request_month_year_week')\n",
|
2496 |
"\n",
|
2497 |
-
"claude_prediction_offline"
|
2498 |
]
|
2499 |
},
|
2500 |
{
|
@@ -2737,7 +2757,7 @@
|
|
2737 |
"prediction_online = prediction_online[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
2738 |
"prediction_online = prediction_online.sort_values(by='request_month_year_week')\n",
|
2739 |
"\n",
|
2740 |
-
"prediction_online"
|
2741 |
]
|
2742 |
},
|
2743 |
{
|
@@ -3368,7 +3388,7 @@
|
|
3368 |
"prediction_online_sme = prediction_online_sme[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
3369 |
"prediction_online_sme = prediction_online_sme.sort_values(by='request_month_year_week')\n",
|
3370 |
"\n",
|
3371 |
-
"prediction_online_sme"
|
3372 |
]
|
3373 |
},
|
3374 |
{
|
@@ -3471,7 +3491,7 @@
|
|
3471 |
"prediction_request_rag = prediction_request_rag[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
3472 |
"prediction_request_rag = prediction_request_rag.sort_values(by='request_month_year_week')\n",
|
3473 |
"\n",
|
3474 |
-
"prediction_request_rag"
|
3475 |
]
|
3476 |
},
|
3477 |
{
|
@@ -3739,7 +3759,7 @@
|
|
3739 |
"prediction_url_cot_claude = prediction_url_cot_claude[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
3740 |
"prediction_url_cot_claude = prediction_url_cot_claude.sort_values(by='request_month_year_week')\n",
|
3741 |
"\n",
|
3742 |
-
"prediction_url_cot_claude"
|
3743 |
]
|
3744 |
},
|
3745 |
{
|
|
|
52 |
},
|
53 |
{
|
54 |
"cell_type": "code",
|
55 |
+
"execution_count": 4,
|
56 |
"metadata": {},
|
57 |
"outputs": [
|
58 |
{
|
|
|
60 |
"output_type": "stream",
|
61 |
"text": [
|
62 |
"<class 'pandas.core.frame.DataFrame'>\n",
|
63 |
+
"RangeIndex: 95550 entries, 0 to 95549\n",
|
64 |
"Data columns (total 19 columns):\n",
|
65 |
" # Column Non-Null Count Dtype \n",
|
66 |
"--- ------ -------------- ----- \n",
|
67 |
+
" 0 trader_address 95550 non-null object \n",
|
68 |
+
" 1 trade_id 95550 non-null object \n",
|
69 |
+
" 2 creation_timestamp 95550 non-null datetime64[ns, UTC]\n",
|
70 |
+
" 3 title 95550 non-null object \n",
|
71 |
+
" 4 market_status 95550 non-null object \n",
|
72 |
+
" 5 collateral_amount 95550 non-null float64 \n",
|
73 |
+
" 6 outcome_index 95550 non-null object \n",
|
74 |
+
" 7 trade_fee_amount 95550 non-null float64 \n",
|
75 |
+
" 8 outcomes_tokens_traded 95550 non-null float64 \n",
|
76 |
+
" 9 current_answer 95550 non-null int64 \n",
|
77 |
+
" 10 is_invalid 95550 non-null bool \n",
|
78 |
+
" 11 winning_trade 95550 non-null bool \n",
|
79 |
+
" 12 earnings 95550 non-null float64 \n",
|
80 |
+
" 13 redeemed 95550 non-null bool \n",
|
81 |
+
" 14 redeemed_amount 95550 non-null float64 \n",
|
82 |
+
" 15 num_mech_calls 95550 non-null int64 \n",
|
83 |
+
" 16 mech_fee_amount 95550 non-null float64 \n",
|
84 |
+
" 17 net_earnings 95550 non-null float64 \n",
|
85 |
+
" 18 roi 95550 non-null float64 \n",
|
86 |
"dtypes: bool(3), datetime64[ns, UTC](1), float64(8), int64(2), object(5)\n",
|
87 |
+
"memory usage: 11.9+ MB\n"
|
88 |
]
|
89 |
}
|
90 |
],
|
|
|
94 |
},
|
95 |
{
|
96 |
"cell_type": "code",
|
97 |
+
"execution_count": 5,
|
98 |
"metadata": {},
|
99 |
"outputs": [
|
100 |
{
|
|
|
103 |
"Timestamp('2023-07-12 15:17:25+0000', tz='UTC')"
|
104 |
]
|
105 |
},
|
106 |
+
"execution_count": 5,
|
107 |
"metadata": {},
|
108 |
"output_type": "execute_result"
|
109 |
}
|
|
|
112 |
"all_trades.creation_timestamp.min()"
|
113 |
]
|
114 |
},
|
115 |
+
{
|
116 |
+
"cell_type": "code",
|
117 |
+
"execution_count": 6,
|
118 |
+
"metadata": {},
|
119 |
+
"outputs": [
|
120 |
+
{
|
121 |
+
"data": {
|
122 |
+
"text/plain": [
|
123 |
+
"Timestamp('2024-05-27 02:13:05+0000', tz='UTC')"
|
124 |
+
]
|
125 |
+
},
|
126 |
+
"execution_count": 6,
|
127 |
+
"metadata": {},
|
128 |
+
"output_type": "execute_result"
|
129 |
+
}
|
130 |
+
],
|
131 |
+
"source": [
|
132 |
+
"all_trades.creation_timestamp.max()"
|
133 |
+
]
|
134 |
+
},
|
135 |
{
|
136 |
"cell_type": "code",
|
137 |
"execution_count": 4,
|
|
|
2383 |
"claude_prediction_online = claude_prediction_online[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
2384 |
"claude_prediction_online = claude_prediction_online.sort_values(by='request_month_year_week')\n",
|
2385 |
"\n",
|
2386 |
+
"claude_prediction_online.head()"
|
2387 |
]
|
2388 |
},
|
2389 |
{
|
|
|
2514 |
"claude_prediction_offline = claude_prediction_offline[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
2515 |
"claude_prediction_offline = claude_prediction_offline.sort_values(by='request_month_year_week')\n",
|
2516 |
"\n",
|
2517 |
+
"claude_prediction_offline.head()"
|
2518 |
]
|
2519 |
},
|
2520 |
{
|
|
|
2757 |
"prediction_online = prediction_online[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
2758 |
"prediction_online = prediction_online.sort_values(by='request_month_year_week')\n",
|
2759 |
"\n",
|
2760 |
+
"prediction_online.head()"
|
2761 |
]
|
2762 |
},
|
2763 |
{
|
|
|
3388 |
"prediction_online_sme = prediction_online_sme[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
3389 |
"prediction_online_sme = prediction_online_sme.sort_values(by='request_month_year_week')\n",
|
3390 |
"\n",
|
3391 |
+
"prediction_online_sme.head()"
|
3392 |
]
|
3393 |
},
|
3394 |
{
|
|
|
3491 |
"prediction_request_rag = prediction_request_rag[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
3492 |
"prediction_request_rag = prediction_request_rag.sort_values(by='request_month_year_week')\n",
|
3493 |
"\n",
|
3494 |
+
"prediction_request_rag.head()"
|
3495 |
]
|
3496 |
},
|
3497 |
{
|
|
|
3759 |
"prediction_url_cot_claude = prediction_url_cot_claude[['request_month_year_week', 'win_perc', 'total_request']]\n",
|
3760 |
"prediction_url_cot_claude = prediction_url_cot_claude.sort_values(by='request_month_year_week')\n",
|
3761 |
"\n",
|
3762 |
+
"prediction_url_cot_claude.head()"
|
3763 |
]
|
3764 |
},
|
3765 |
{
|