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{
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"source": [
"Shows the data in pitches.csv right now. Hopefully soon will show off a bit more of the data, including using pitches.csv with games.csv to only get pitches thrown in a certain season. "
]
},
{
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"source": [
"import pandas as pd\n",
"import os, datetime\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
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"id": "765f4807",
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{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Num</th>\n",
" <th>Pitch</th>\n",
" <th>Type</th>\n",
" <th>MPH</th>\n",
" <th>play-hitzone</th>\n",
" <th>play-bases</th>\n",
" <th>play-field</th>\n",
" <th>Pitcher</th>\n",
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" <th>Batting Team</th>\n",
" <th>Inning</th>\n",
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" <th>Game</th>\n",
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" <tr>\n",
" <th>3787355</th>\n",
" <td>1</td>\n",
" <td>Strike Looking</td>\n",
" <td>Cutter</td>\n",
" <td>86</td>\n",
" <td>top: 14.71px; right: 24.56px;</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>Greene</td>\n",
" <td>LAD</td>\n",
" <td>SD</td>\n",
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" <td>179</td>\n",
" <td>401228956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3787356</th>\n",
" <td>2</td>\n",
" <td>Ball</td>\n",
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" <td>86</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Greene</td>\n",
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" <td>3</td>\n",
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" <td>LAD</td>\n",
" <td>SD</td>\n",
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" <td>179</td>\n",
" <td>401228956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3787358</th>\n",
" <td>4</td>\n",
" <td>Ball</td>\n",
" <td>Changeup</td>\n",
" <td>87</td>\n",
" <td>top: 22px; right: 30.87px;</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Greene</td>\n",
" <td>LAD</td>\n",
" <td>SD</td>\n",
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" <td>179</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3787359</th>\n",
" <td>5</td>\n",
" <td>Ground Out</td>\n",
" <td>Cutter</td>\n",
" <td>88</td>\n",
" <td>top: 9.29px; right: 14.78px;</td>\n",
" <td>2.0</td>\n",
" <td>top: 12.78px; right: 21.51px;</td>\n",
" <td>Greene</td>\n",
" <td>LAD</td>\n",
" <td>SD</td>\n",
" <td>Bottom 16th</td>\n",
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],
"text/plain": [
" Num Pitch Type MPH play-hitzone \\\n",
"3787355 1 Strike Looking Cutter 86 top: 14.71px; right: 24.56px; \n",
"3787356 2 Ball Changeup 86 top: 18.39px; right: 26.19px; \n",
"3787357 3 Strike Looking Changeup 86 top: 15.57px; right: 27.2px; \n",
"3787358 4 Ball Changeup 87 top: 22px; right: 30.87px; \n",
"3787359 5 Ground Out Cutter 88 top: 9.29px; right: 14.78px; \n",
"\n",
" play-bases play-field Pitcher Pitching Team \\\n",
"3787355 2.0 NaN Greene LAD \n",
"3787356 NaN NaN Greene LAD \n",
"3787357 NaN NaN Greene LAD \n",
"3787358 NaN NaN Greene LAD \n",
"3787359 2.0 top: 12.78px; right: 21.51px; Greene LAD \n",
"\n",
" Batting Team Inning Event Id Game \n",
"3787355 SD Bottom 16th 179 401228956 \n",
"3787356 SD Bottom 16th 179 401228956 \n",
"3787357 SD Bottom 16th 179 401228956 \n",
"3787358 SD Bottom 16th 179 401228956 \n",
"3787359 SD Bottom 16th 179 401228956 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_df = pd.read_csv('../input/mlb-game-data/pitches.csv')\n",
"p_df.tail()"
]
},
{
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" <th>757625</th>\n",
" <td>5</td>\n",
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" <th>757629</th>\n",
" <td>4</td>\n",
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" <td>4</td>\n",
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" <th>935325</th>\n",
" <td>4</td>\n",
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" <td>1.0</td>\n",
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" <th>993278</th>\n",
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" <th>1110978</th>\n",
" <td>3</td>\n",
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" <th>1126276</th>\n",
" <td>2</td>\n",
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" <td>100</td>\n",
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" <td>87</td>\n",
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" <th>1154719</th>\n",
" <td>2</td>\n",
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" <td>MIL</td>\n",
" <td>PHI</td>\n",
" <td>Bottom 9th</td>\n",
" <td>117</td>\n",
" <td>370722122</td>\n",
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" <tr>\n",
" <th>1172388</th>\n",
" <td>1</td>\n",
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" <td>100</td>\n",
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" <td>23.0</td>\n",
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" <td>83</td>\n",
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" <th>1251855</th>\n",
" <td>6</td>\n",
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" <td>100</td>\n",
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" <td>1.0</td>\n",
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" <td>92</td>\n",
" <td>370826119</td>\n",
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" <tr>\n",
" <th>1351951</th>\n",
" <td>5</td>\n",
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" <td>1.0</td>\n",
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" <td>Knebel</td>\n",
" <td>MIL</td>\n",
" <td>CHC</td>\n",
" <td>Bottom 9th</td>\n",
" <td>103</td>\n",
" <td>370908116</td>\n",
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" <th>1407162</th>\n",
" <td>3</td>\n",
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" <td>12.0</td>\n",
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" <td>NaN</td>\n",
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],
"text/plain": [
" Num Pitch Type MPH \\\n",
"757625 5 Fly Out Fastball 100 \n",
"757629 4 Foul Ball Fastball 100 \n",
"780903 4 Ball Fastball 100 \n",
"935325 4 Pop Out Fastball 100 \n",
"993278 4 Strike Swinging Fastball 100 \n",
"1110974 5 Foul Ball Fastball 100 \n",
"1110978 3 Ball Fastball 100 \n",
"1126276 2 Double Fastball 100 \n",
"1154719 2 Ball Fastball 100 \n",
"1172388 1 Ball Fastball 100 \n",
"1251853 4 Foul Ball Fastball 100 \n",
"1251855 6 Strike Swinging Fastball 100 \n",
"1299915 4 Strike Swinging Fastball 100 \n",
"1351951 5 Single Fastball 100 \n",
"1407162 3 Ball Fastball 100 \n",
"2532819 5 Foul Ball Four-seam FB 100 \n",
"2793343 6 Ball Four-seam FB 100 \n",
"2793347 4 Strike Swinging Four-seam FB 100 \n",
"2895340 3 Strike Swinging Four-seam FB 100 \n",
"2938168 1 Ball Four-seam FB 100 \n",
"\n",
" play-hitzone play-bases \\\n",
"757625 top: 16.22px; right: 20.89px; NaN \n",
"757629 top: 7.77px; right: 25.17px; NaN \n",
"780903 top: 13.84px; right: 30.67px; NaN \n",
"935325 top: 9.94px; right: 25.57px; 1.0 \n",
"993278 top: 9.5px; right: 28.02px; 3.0 \n",
"1110974 top: 7.99px; right: 15.39px; NaN \n",
"1110978 top: 10.8px; right: 10.5px; NaN \n",
"1126276 top: 13.84px; right: 16.2px; 2.0 \n",
"1154719 top: 15.57px; right: 10.09px; NaN \n",
"1172388 top: 19.91px; right: 22.31px; 23.0 \n",
"1251853 top: 12.1px; right: 24.96px; NaN \n",
"1251855 top: 9.72px; right: 17.43px; NaN \n",
"1299915 top: 1.27px; right: 24.96px; 1.0 \n",
"1351951 top: 8.64px; right: 17.83px; 1.0 \n",
"1407162 top: 5.39px; right: 32.7px; NaN \n",
"2532819 top: 12.97px; right: 29.65px; NaN \n",
"2793343 top: 4.08px; right: 19.46px; 12.0 \n",
"2793347 top: 11.24px; right: 18.65px; 12.0 \n",
"2895340 top: 19.69px; right: 28.43px; NaN \n",
"2938168 top: 11.02px; right: 13.35px; NaN \n",
"\n",
" play-field Pitcher Pitching Team Batting Team \\\n",
"757625 top: 9.41px; right: 13.97px; Feliz MIL TOR \n",
"757629 NaN Feliz MIL TOR \n",
"780903 NaN Barnes MIL CHC \n",
"935325 top: 14.17px; right: 21.28px; Barnes MIL ARI \n",
"993278 NaN Knebel MIL ARI \n",
"1110974 NaN Knebel MIL NYY \n",
"1110978 NaN Knebel MIL NYY \n",
"1126276 top: 12.66px; right: 28.7px; Knebel MIL PHI \n",
"1154719 NaN Knebel MIL PHI \n",
"1172388 NaN Barnes MIL WSH \n",
"1251853 NaN Barnes MIL PIT \n",
"1251855 NaN Barnes MIL PIT \n",
"1299915 NaN Knebel MIL LAD \n",
"1351951 top: 12.08px; right: 26.84px; Knebel MIL CHC \n",
"1407162 NaN Knebel MIL CHC \n",
"2532819 NaN Woodruff MIL SD \n",
"2793343 NaN Black MIL ARI \n",
"2793347 NaN Black MIL ARI \n",
"2895340 NaN Black MIL SD \n",
"2938168 NaN Black MIL COL \n",
"\n",
" Inning Event Id Game \n",
"757625 Bottom 9th 97 370411114 \n",
"757629 Bottom 9th 99 370411114 \n",
"780903 Bottom 7th 75 370417116 \n",
"935325 Top 8th 78 370526108 \n",
"993278 Bottom 8th 112 370609129 \n",
"1110974 Bottom 9th 94 370708110 \n",
"1110978 Bottom 9th 95 370708110 \n",
"1126276 Top 9th 87 370715108 \n",
"1154719 Bottom 9th 117 370722122 \n",
"1172388 Bottom 8th 80 370726120 \n",
"1251853 Top 8th 83 370815108 \n",
"1251855 Top 8th 83 370815108 \n",
"1299915 Bottom 9th 92 370826119 \n",
"1351951 Bottom 9th 103 370908116 \n",
"1407162 Top 10th 104 370922108 \n",
"2532819 Bottom 1st 9 401075823 \n",
"2793343 Top 6th 66 401076682 \n",
"2793347 Top 6th 67 401076682 \n",
"2895340 Top 8th 95 401077019 \n",
"2938168 Bottom 5th 51 401077161 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 100+ MPH pitches thrown by the Brewers\n",
"p_df[(p_df['Pitching Team'] == 'MIL') & (p_df['MPH'] <= '100') & (p_df['MPH'] != '--')]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cc6ca289",
"metadata": {
"execution": {
"iopub.execute_input": "2021-08-26T22:49:56.605320Z",
"iopub.status.busy": "2021-08-26T22:49:56.604641Z",
"iopub.status.idle": "2021-08-26T22:49:56.611732Z",
"shell.execute_reply": "2021-08-26T22:49:56.612167Z",
"shell.execute_reply.started": "2021-08-26T22:30:21.837599Z"
},
"papermill": {
"duration": 0.95159,
"end_time": "2021-08-26T22:49:56.612370",
"exception": false,
"start_time": "2021-08-26T22:49:55.660780",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Four-seam FB 809912\n",
"Fastball 764700\n",
"Slider 661454\n",
"Changeup 395458\n",
"Curve 357039\n",
"Sinker 274932\n",
"Cutter 191539\n",
"Two-seam FB 149970\n",
"-- 65683\n",
"Splitter 53782\n",
"Knuckle Curve 47156\n",
"Knuckleball 8202\n",
"Unknown 3367\n",
"Intentional Ball 2926\n",
"Pitch Out 400\n",
"Eephus Pitch 328\n",
"Slow Curve 297\n",
"Forkball 148\n",
"Screwball 67\n",
"Name: Type, dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# All the different types of pitches thrown\n",
"p_df['Type'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8818ccea",
"metadata": {
"execution": {
"iopub.execute_input": "2021-08-26T22:49:57.572257Z",
"iopub.status.busy": "2021-08-26T22:49:57.571585Z",
"iopub.status.idle": "2021-08-26T22:49:58.791555Z",
"shell.execute_reply": "2021-08-26T22:49:58.792047Z",
"shell.execute_reply.started": "2021-08-26T22:30:22.209537Z"
},
"papermill": {
"duration": 2.162495,
"end_time": "2021-08-26T22:49:58.792210",
"exception": false,
"start_time": "2021-08-26T22:49:56.629715",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# gets a bit messy right now...\n",
"plt.pie(p_df['Type'].value_counts(), labels=p_df['Type'].value_counts().index.tolist(), autopct='%1.2f%%');"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b4f7f989",
"metadata": {
"execution": {
"iopub.execute_input": "2021-08-26T22:49:59.400576Z",
"iopub.status.busy": "2021-08-26T22:49:59.399909Z",
"iopub.status.idle": "2021-08-26T22:49:59.465985Z",
"shell.execute_reply": "2021-08-26T22:49:59.465388Z",
"shell.execute_reply.started": "2021-08-26T22:31:56.661786Z"
},
"papermill": {
"duration": 0.65089,
"end_time": "2021-08-26T22:49:59.466131",
"exception": false,
"start_time": "2021-08-26T22:49:58.815241",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Anderson 11934\n",
"Davies 9518\n",
"Peralta 8856\n",
"Woodruff 7411\n",
"Guerra 6866\n",
" ... \n",
"Z. Brown 5\n",
"Kenilvort 4\n",
"J. Guerra 3\n",
"D. Brown 3\n",
"Cedeno 2\n",
"Name: Pitcher, Length: 122, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_df[p_df['Pitching Team'] == 'MIL']['Pitcher'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f13a2eb1",
"metadata": {
"execution": {
"iopub.execute_input": "2021-08-26T22:50:00.033709Z",
"iopub.status.busy": "2021-08-26T22:50:00.032939Z",
"iopub.status.idle": "2021-08-26T22:50:00.044538Z",
"shell.execute_reply": "2021-08-26T22:50:00.043972Z",
"shell.execute_reply.started": "2021-08-26T22:32:07.079175Z"
},
"papermill": {
"duration": 0.559736,
"end_time": "2021-08-26T22:50:00.044677",
"exception": false,
"start_time": "2021-08-26T22:49:59.484941",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Cutter 1434\n",
"Four-seam FB 1081\n",
"Slider 810\n",
"Curve 559\n",
"Sinker 408\n",
"Changeup 338\n",
"-- 113\n",
"Two-seam FB 10\n",
"Name: Type, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_df[p_df['Pitcher'] == 'Burnes']['Type'].value_counts()"
]
},
{
"cell_type": "markdown",
"id": "a1deae16",
"metadata": {
"papermill": {
"duration": 0.018672,
"end_time": "2021-08-26T22:50:00.083436",
"exception": false,
"start_time": "2021-08-26T22:50:00.064764",
"status": "completed"
},
"tags": []
},
"source": [
"# Get Pitches Thrown in 2021"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8697dc92",
"metadata": {
"execution": {
"iopub.execute_input": "2021-08-26T22:50:00.128187Z",
"iopub.status.busy": "2021-08-26T22:50:00.127540Z",
"iopub.status.idle": "2021-08-26T22:50:00.367577Z",
"shell.execute_reply": "2021-08-26T22:50:00.366648Z",
"shell.execute_reply.started": "2021-08-26T22:36:24.059595Z"
},
"papermill": {
"duration": 0.264448,
"end_time": "2021-08-26T22:50:00.367724",
"exception": false,
"start_time": "2021-08-26T22:50:00.103276",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Game', 'away', 'away-record', 'awayaway-record', 'home', 'home-record',\n",
" 'homehome-record', 'away-score', 'home-score', 'postseason info',\n",
" 'Walks Issued - Away', 'Walks Issued - Home', 'Stolen Bases - Away',\n",
" 'Stolen Bases - Home', 'Strikeouts Thrown - Away',\n",
" 'Strikeouts Thrown - Home', 'Total Bases - Away', 'Total Bases - Home',\n",
" 'Stadium', 'Date', 'Location', 'Odds', 'O/U', 'Attendance', 'Capacity',\n",
" 'Duration', 'Umpires', 'WIN - Pitcher - Stats', 'WIN - Pitcher - Id',\n",
" 'WIN - Pitcher - Name', 'WIN - Pitcher - AbbrName',\n",
" 'WIN - Pitcher - Record', 'LOSS - Pitcher - Stats',\n",
" 'LOSS - Pitcher - Id', 'LOSS - Pitcher - Name',\n",
" 'LOSS - Pitcher - AbbrName', 'LOSS - Pitcher - Record',\n",
" 'SAVE - Pitcher - Stats', 'SAVE - Pitcher - Id',\n",
" 'SAVE - Pitcher - Name', 'SAVE - Pitcher - AbbrName',\n",
" 'SAVE - Pitcher - Record', 'Extra Innings'],\n",
" dtype='object')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"games_df = pd.read_csv('../input/mlb-game-data/games.csv')\n",
"games_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c97b728a",
"metadata": {
"execution": {
"iopub.execute_input": "2021-08-26T22:50:00.474735Z",
"iopub.status.busy": "2021-08-26T22:50:00.474042Z",
"iopub.status.idle": "2021-08-26T22:50:00.505363Z",
"shell.execute_reply": "2021-08-26T22:50:00.504775Z",
"shell.execute_reply.started": "2021-08-26T22:47:23.716998Z"
},
"papermill": {
"duration": 0.118304,
"end_time": "2021-08-26T22:50:00.505507",
"exception": false,
"start_time": "2021-08-26T22:50:00.387203",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"10941 401227066\n",
"10942 401227056\n",
"10943 401227059\n",
"10944 401227061\n",
"10945 401227053\n",
" ... \n",
"12845 401228958\n",
"12846 401228960\n",
"12847 401228961\n",
"12848 401228952\n",
"12849 401228956\n",
"Name: Game, Length: 1909, dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"games_subset = games_df[pd.to_datetime(games_df['Date'], format='%Y-%m-%dT%H:%MZ', utc=True).dt.year == 2021]['Game']\n",
"games_subset"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5b0f91f7",
"metadata": {
"execution": {
"iopub.execute_input": "2021-08-26T22:50:00.549985Z",
"iopub.status.busy": "2021-08-26T22:50:00.549264Z",
"iopub.status.idle": "2021-08-26T22:50:00.651872Z",
"shell.execute_reply": "2021-08-26T22:50:00.651308Z",
"shell.execute_reply.started": "2021-08-26T22:48:07.655007Z"
},
"papermill": {
"duration": 0.126882,
"end_time": "2021-08-26T22:50:00.652018",
"exception": false,
"start_time": "2021-08-26T22:50:00.525136",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Num</th>\n",
" <th>Pitch</th>\n",
" <th>Type</th>\n",
" <th>MPH</th>\n",
" <th>play-hitzone</th>\n",
" <th>play-bases</th>\n",
" <th>play-field</th>\n",
" <th>Pitcher</th>\n",
" <th>Pitching Team</th>\n",
" <th>Batting Team</th>\n",
" <th>Inning</th>\n",
" <th>Event Id</th>\n",
" <th>Game</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3230259</th>\n",
" <td>1</td>\n",
" <td>Ball</td>\n",
" <td>Four-seam FB</td>\n",
" <td>97</td>\n",
" <td>top: 11px; right: 19px;</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Cole</td>\n",
" <td>NYY</td>\n",
" <td>TOR</td>\n",
" <td>Top 1st</td>\n",
" <td>1</td>\n",
" <td>401227066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3230260</th>\n",
" <td>2</td>\n",
" <td>Ground Out</td>\n",
" <td>Four-seam FB</td>\n",
" <td>95</td>\n",
" <td>top: 7.12px; right: 19.87px;</td>\n",
" <td>NaN</td>\n",
" <td>top: 14.05px; right: 23.02px;</td>\n",
" <td>Cole</td>\n",
" <td>NYY</td>\n",
" <td>TOR</td>\n",
" <td>Top 1st</td>\n",
" <td>1</td>\n",
" <td>401227066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3230261</th>\n",
" <td>1</td>\n",
" <td>Strike Looking</td>\n",
" <td>Four-seam FB</td>\n",
" <td>96</td>\n",
" <td>top: 11.89px; right: 18.65px;</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Cole</td>\n",
" <td>NYY</td>\n",
" <td>TOR</td>\n",
" <td>Top 1st</td>\n",
" <td>2</td>\n",
" <td>401227066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3230262</th>\n",
" <td>2</td>\n",
" <td>Ball</td>\n",
" <td>Knuckle Curve</td>\n",
" <td>82</td>\n",
" <td>top: 22px; right: 21.3px;</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Cole</td>\n",
" <td>NYY</td>\n",
" <td>TOR</td>\n",
" <td>Top 1st</td>\n",
" <td>2</td>\n",
" <td>401227066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3230263</th>\n",
" <td>3</td>\n",
" <td>Foul Ball</td>\n",
" <td>Changeup</td>\n",
" <td>87</td>\n",
" <td>top: 13.84px; right: 17.43px;</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Cole</td>\n",
" <td>NYY</td>\n",
" <td>TOR</td>\n",
" <td>Top 1st</td>\n",
" <td>2</td>\n",
" <td>401227066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3787355</th>\n",
" <td>1</td>\n",
" <td>Strike Looking</td>\n",
" <td>Cutter</td>\n",
" <td>86</td>\n",
" <td>top: 14.71px; right: 24.56px;</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>Greene</td>\n",
" <td>LAD</td>\n",
" <td>SD</td>\n",
" <td>Bottom 16th</td>\n",
" <td>179</td>\n",
" <td>401228956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3787356</th>\n",
" <td>2</td>\n",
" <td>Ball</td>\n",
" <td>Changeup</td>\n",
" <td>86</td>\n",
" <td>top: 18.39px; right: 26.19px;</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Greene</td>\n",
" <td>LAD</td>\n",
" <td>SD</td>\n",
" <td>Bottom 16th</td>\n",
" <td>179</td>\n",
" <td>401228956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3787357</th>\n",
" <td>3</td>\n",
" <td>Strike Looking</td>\n",
" <td>Changeup</td>\n",
" <td>86</td>\n",
" <td>top: 15.57px; right: 27.2px;</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Greene</td>\n",
" <td>LAD</td>\n",
" <td>SD</td>\n",
" <td>Bottom 16th</td>\n",
" <td>179</td>\n",
" <td>401228956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3787358</th>\n",
" <td>4</td>\n",
" <td>Ball</td>\n",
" <td>Changeup</td>\n",
" <td>87</td>\n",
" <td>top: 22px; right: 30.87px;</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Greene</td>\n",
" <td>LAD</td>\n",
" <td>SD</td>\n",
" <td>Bottom 16th</td>\n",
" <td>179</td>\n",
" <td>401228956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3787359</th>\n",
" <td>5</td>\n",
" <td>Ground Out</td>\n",
" <td>Cutter</td>\n",
" <td>88</td>\n",
" <td>top: 9.29px; right: 14.78px;</td>\n",
" <td>2.0</td>\n",
" <td>top: 12.78px; right: 21.51px;</td>\n",
" <td>Greene</td>\n",
" <td>LAD</td>\n",
" <td>SD</td>\n",
" <td>Bottom 16th</td>\n",
" <td>179</td>\n",
" <td>401228956</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>557101 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" Num Pitch Type MPH \\\n",
"3230259 1 Ball Four-seam FB 97 \n",
"3230260 2 Ground Out Four-seam FB 95 \n",
"3230261 1 Strike Looking Four-seam FB 96 \n",
"3230262 2 Ball Knuckle Curve 82 \n",
"3230263 3 Foul Ball Changeup 87 \n",
"... ... ... ... .. \n",
"3787355 1 Strike Looking Cutter 86 \n",
"3787356 2 Ball Changeup 86 \n",
"3787357 3 Strike Looking Changeup 86 \n",
"3787358 4 Ball Changeup 87 \n",
"3787359 5 Ground Out Cutter 88 \n",
"\n",
" play-hitzone play-bases \\\n",
"3230259 top: 11px; right: 19px; NaN \n",
"3230260 top: 7.12px; right: 19.87px; NaN \n",
"3230261 top: 11.89px; right: 18.65px; NaN \n",
"3230262 top: 22px; right: 21.3px; NaN \n",
"3230263 top: 13.84px; right: 17.43px; NaN \n",
"... ... ... \n",
"3787355 top: 14.71px; right: 24.56px; 2.0 \n",
"3787356 top: 18.39px; right: 26.19px; NaN \n",
"3787357 top: 15.57px; right: 27.2px; NaN \n",
"3787358 top: 22px; right: 30.87px; NaN \n",
"3787359 top: 9.29px; right: 14.78px; 2.0 \n",
"\n",
" play-field Pitcher Pitching Team Batting Team \\\n",
"3230259 NaN Cole NYY TOR \n",
"3230260 top: 14.05px; right: 23.02px; Cole NYY TOR \n",
"3230261 NaN Cole NYY TOR \n",
"3230262 NaN Cole NYY TOR \n",
"3230263 NaN Cole NYY TOR \n",
"... ... ... ... ... \n",
"3787355 NaN Greene LAD SD \n",
"3787356 NaN Greene LAD SD \n",
"3787357 NaN Greene LAD SD \n",
"3787358 NaN Greene LAD SD \n",
"3787359 top: 12.78px; right: 21.51px; Greene LAD SD \n",
"\n",
" Inning Event Id Game \n",
"3230259 Top 1st 1 401227066 \n",
"3230260 Top 1st 1 401227066 \n",
"3230261 Top 1st 2 401227066 \n",
"3230262 Top 1st 2 401227066 \n",
"3230263 Top 1st 2 401227066 \n",
"... ... ... ... \n",
"3787355 Bottom 16th 179 401228956 \n",
"3787356 Bottom 16th 179 401228956 \n",
"3787357 Bottom 16th 179 401228956 \n",
"3787358 Bottom 16th 179 401228956 \n",
"3787359 Bottom 16th 179 401228956 \n",
"\n",
"[557101 rows x 13 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# And here we are\n",
"p_df[p_df['Game'].isin(games_subset)]"
]
},
{
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"source": [
"### To Get CSVs with subset of modified data"
]
},
{
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"source": [
"strike_calls = ['Strike Looking', 'Strike Swinging', 'Foul Ball', \n",
" 'Ground Out', 'Fly Out', 'Line Out', 'Pop Out', \n",
" 'Foul Out', 'Bunted Foul', 'Sacrifice Fly', 'Sacrifice', \n",
" 'Batters Fielders Choice (runner Out)', 'Bunt Ground Out', \n",
" 'Bunt Pop Out', 'Strikeout Batter Safe, Passed Ball']\n",
"\n",
"hit_calls = ['Single', 'Double', 'Triple', 'Home Run', 'Ground Rule Double', \n",
" 'Bunt Single', 'Bunt Double', 'Inside The Park Home Run']\n",
"\n",
"ball_calls = ['Ball', 'Hit By Pitch', 'Wild Pitch; Runner Reached', 'Intentional Ball']\n",
"error_calls = ['Batter Reached On Error (batter To First)', 'Catchers Interference (batter To First/error)']\n",
"other_calls = ['Batters Fielders Choice (all Runners Safe)', 'Batters Interference (batter Out)', \n",
" 'Official Ruling Pending']\n",
"\n",
"def changeOutcome(x):\n",
" if x in strike_calls:\n",
" return 'Strike'\n",
" elif x in hit_calls:\n",
" return 'Hit'\n",
" elif x in ball_calls:\n",
" return 'Ball'\n",
" else:\n",
" return 'Other'"
]
},
{
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"source": [
"# could loop this over team abbreviations\n",
"# - only other thing to change would be folder the csvs are stored in (in next cell)\n",
"\n",
"team_abbr = 'MIL'\n",
"\n",
"brewers_df = p_df[p_df['Pitching Team'] == team_abbr]\n",
"\n",
"# write pitcher csvs\n",
"for pitcher in brewers_df['Pitcher'].unique():\n",
" temp = brewers_df[brewers_df['Pitcher']==pitcher][['Pitch', 'Type', 'MPH', 'Game']]\n",
" # temp['Inning'] = temp['Inning'].apply(lambda x: x.split(' ')[1][:-2])\n",
" \n",
" # to get the pitcher's pitch count in the game\n",
" pitch_counts = []\n",
" gameid = -1\n",
" for i, row in temp.iterrows():\n",
" if gameid != row['Game']:\n",
" pc = 1\n",
" gameid = row['Game']\n",
" else:\n",
" pc += 1\n",
" pitch_counts.append(pc)\n",
" \n",
" temp['Count'] = pitch_counts\n",
" temp['Pitch'] = temp['Pitch'].apply(lambda x: changeOutcome(x))\n",
" # temp[['Pitch', 'Type', 'MPH', 'Count']].to_csv(os.path.join('pitchers', 'brewers', pitcher+'.csv'), index=False, header=False)"
]
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{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'pitchers/brewers/pitchers.txt'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-e06ba8a85fed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# write list of pitchers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pitchers'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'brewers'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pitchers.txt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpitcher\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbrewers_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Pitcher'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mpass\u001b[0m \u001b[0;31m#f.write(pitcher+'\\n')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'pitchers/brewers/pitchers.txt'"
]
}
],
"source": [
"# write list of pitchers\n",
"with open(os.path.join('pitchers', 'brewers', 'pitchers.txt'), 'w') as f: \n",
" for pitcher in brewers_df['Pitcher'].unique().tolist():\n",
" pass #f.write(pitcher+'\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "885cdaaa",
"metadata": {
"execution": {
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"source": [
"# examples of what the data looks like after being modified\n",
"brewers_df[brewers_df['Pitcher']=='Burnes'][['Pitch', 'Type', 'MPH', 'Inning']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e3eb4f5",
"metadata": {
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"source": [
"brewers_df[brewers_df['Pitcher']=='Burnes']['Pitch'].apply(lambda x: changeOutcome(x))"
]
},
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| 0073/187/73187892.ipynb | s3://data-agents/kaggle-outputs/sharded/003_00073.jsonl.gz |
{
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{
"name": "stdout",
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"text": [
"\u001b[33mWARNING: You are using pip version 20.1; however, version 21.2.4 is available.\r\n",
"You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\r\n"
]
}
],
"source": [
"!pip install transformers -q"
]
},
{
"cell_type": "code",
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"metadata": {
"execution": {
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"source": [
"# Importing stock libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler\n",
"\n",
"# Importing the T5 modules from huggingface/transformers\n",
"from transformers import T5Tokenizer, T5ForConditionalGeneration"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"Thu Aug 26 23:03:48 2021 \r\n",
"+-----------------------------------------------------------------------------+\r\n",
"| NVIDIA-SMI 450.119.04 Driver Version: 450.119.04 CUDA Version: 11.0 |\r\n",
"|-------------------------------+----------------------+----------------------+\r\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n",
"| | | MIG M. |\r\n",
"|===============================+======================+======================|\r\n",
"| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\r\n",
"| N/A 36C P0 29W / 250W | 0MiB / 16280MiB | 0% Default |\r\n",
"| | | N/A |\r\n",
"+-------------------------------+----------------------+----------------------+\r\n",
" \r\n",
"+-----------------------------------------------------------------------------+\r\n",
"| Processes: |\r\n",
"| GPU GI CI PID Type Process name GPU Memory |\r\n",
"| ID ID Usage |\r\n",
"|=============================================================================|\r\n",
"| No running processes found |\r\n",
"+-----------------------------------------------------------------------------+\r\n"
]
}
],
"source": [
"# Checking out the GPU we have access to. This is output is from the google colab version. \n",
"!nvidia-smi"
]
},
{
"cell_type": "code",
"execution_count": 4,
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"source": [
"# # Setting up the device for GPU usage\n",
"from torch import cuda\n",
"device = 'cuda' if cuda.is_available() else 'cpu'"
]
},
{
"cell_type": "markdown",
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"source": [
"\n",
"### Preparing the Dataset for data processing: Class\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"source": [
"# Creating a custom dataset for reading the dataframe and loading it into the dataloader to pass it to the neural network at a later stage for finetuning the model and to prepare it for predictions\n",
"\n",
"class CustomDataset(Dataset):\n",
"\n",
" def __init__(self, dataframe, tokenizer, source_len, summ_len):\n",
" self.tokenizer = tokenizer\n",
" self.data = dataframe\n",
" self.source_len = source_len\n",
" self.summ_len = summ_len\n",
" self.text = self.data.text\n",
" self.ctext = self.data.ctext\n",
"\n",
" def __len__(self):\n",
" return len(self.text)\n",
"\n",
" def __getitem__(self, index):\n",
" ctext = str(self.ctext[index])\n",
" ctext = ' '.join(ctext.split())\n",
"\n",
" text = str(self.text[index])\n",
" text = ' '.join(text.split())\n",
"\n",
" source = self.tokenizer.batch_encode_plus([ctext], max_length= self.source_len, pad_to_max_length=True,return_tensors='pt')\n",
" target = self.tokenizer.batch_encode_plus([text], max_length= self.summ_len, pad_to_max_length=True,return_tensors='pt')\n",
"\n",
" source_ids = source['input_ids'].squeeze()\n",
" source_mask = source['attention_mask'].squeeze()\n",
" target_ids = target['input_ids'].squeeze()\n",
" target_mask = target['attention_mask'].squeeze()\n",
"\n",
" return {\n",
" 'source_ids': source_ids.to(dtype=torch.long), \n",
" 'source_mask': source_mask.to(dtype=torch.long), \n",
" 'target_ids': target_ids.to(dtype=torch.long),\n",
" 'target_ids_y': target_ids.to(dtype=torch.long)\n",
" }"
]
},
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"source": [
"### Fine Tuning the Model: Function"
]
},
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"source": [
"\n",
"### Validating the Model Performance: Function"
]
},
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"source": [
"def validate(epoch, tokenizer, model, device, loader):\n",
" model.eval()\n",
" predictions = []\n",
" actuals = []\n",
" with torch.no_grad():\n",
" for _, data in enumerate(loader, 0):\n",
" y = data['target_ids'].to(device, dtype = torch.long)\n",
" ids = data['source_ids'].to(device, dtype = torch.long)\n",
" mask = data['source_mask'].to(device, dtype = torch.long)\n",
"\n",
" generated_ids = model.generate(\n",
" input_ids = ids,\n",
" attention_mask = mask, \n",
" max_length=30, \n",
" num_beams=2,\n",
" repetition_penalty=2.5, \n",
" length_penalty=1.0, \n",
" early_stopping=True\n",
" )\n",
" preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n",
" target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True)for t in y]\n",
" if _%100==0:\n",
" print(f'Completed {_}')\n",
"\n",
" predictions.extend(preds)\n",
" actuals.extend(target)\n",
" return predictions, actuals"
]
},
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"source": [
"\n",
"### Main Function"
]
},
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" ctext \\\n",
"0 summarize: This paper discusses the benefits... \n",
"1 summarize: We present in this article a deta... \n",
"2 summarize: The purpose of this article is to... \n",
"3 summarize: The aim of the KArlsruhe TRItium ... \n",
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"\n",
"Now generating summaries on our fine tuned model for the validation dataset and saving it in a dataframe\n",
"Completed 0\n",
"Completed 100\n",
"Completed 200\n",
"Completed 300\n",
"Completed 400\n",
"Completed 500\n",
"Completed 600\n",
"Completed 700\n",
"Completed 800\n",
"Completed 900\n",
"Completed 1000\n",
"Completed 1100\n",
"Completed 1200\n",
"Completed 1300\n",
"Completed 1400\n",
"Completed 1500\n",
"Completed 1600\n",
"Completed 1700\n",
"Completed 1800\n",
"Completed 1900\n",
"Output Files generated for review\n",
"saving the model weights\n"
]
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"source": [
"def main():\n",
"\n",
" TRAIN_BATCH_SIZE = 4 # input batch size for training (default: 64)\n",
" VALID_BATCH_SIZE = 4 # input batch size for testing (default: 1000)\n",
" TRAIN_EPOCHS = 3 # number of epochs to train (default: 10)\n",
" VAL_EPOCHS = 1 \n",
" LEARNING_RATE = 3e-5 # learning rate (default: 0.01)\n",
" SEED = 42 # random seed (default: 42)\n",
" MAX_LEN = 512\n",
" SUMMARY_LEN = 15 \n",
"\n",
" # Set random seeds and deterministic pytorch for reproducibility\n",
" torch.manual_seed(SEED) # pytorch random seed\n",
" np.random.seed(SEED) # numpy random seed\n",
" torch.backends.cudnn.deterministic = True\n",
"\n",
" # tokenzier for encoding the text\n",
" tokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\n",
" \n",
"\n",
" # Importing and Pre-Processing the domain data\n",
" # Selecting the needed columns only. \n",
" # Adding the summarzie text in front of the text. This is to format the dataset similar to how T5 model was trained for summarization task. \n",
" df = pd.read_csv('../input/graduation-proj-summarization-using-pretraned-t5/summarization_data.csv')\n",
" df = df[['ctext','text']]\n",
" df['ctext'] = 'summarize: ' + df['ctext']\n",
" print(df.head())\n",
"\n",
" \n",
" # Creation of Dataset and Dataloader\n",
" # Defining the train size. So 80% of the data will be used for training and the rest will be used for validation. \n",
" train_size = 0.8\n",
" train_dataset=df.sample(frac=train_size, random_state = SEED).reset_index(drop=True)\n",
" val_dataset=df.drop(train_dataset.index).reset_index(drop=True)\n",
"\n",
"# print(\"FULL Dataset: {}\".format(df.shape))\n",
"# print(\"TRAIN Dataset: {}\".format(train_dataset.shape))\n",
" print(\"TEST Dataset: {}\".format(val_dataset.shape))\n",
"\n",
"\n",
" # Creating the Training and Validation dataset for further creation of Dataloader\n",
"# training_set = CustomDataset(train_dataset, tokenizer, MAX_LEN, SUMMARY_LEN)\n",
" val_set = CustomDataset(val_dataset, tokenizer, MAX_LEN, SUMMARY_LEN)\n",
"\n",
" # Defining the parameters for creation of dataloaders\n",
" train_params = {\n",
" 'batch_size': TRAIN_BATCH_SIZE,\n",
" 'shuffle': True,\n",
" 'num_workers': 0\n",
" }\n",
"\n",
" val_params = {\n",
" 'batch_size': VALID_BATCH_SIZE,\n",
" 'shuffle': False,\n",
" 'num_workers': 0\n",
" }\n",
"\n",
" # Creation of Dataloaders for testing and validation. This will be used down for training and validation stage for the model.\n",
"# training_loader = DataLoader(training_set, **train_params)\n",
" val_loader = DataLoader(val_set, **val_params)\n",
"\n",
"\n",
" \n",
" # Defining the model. We are using t5-base model and added a Language model layer on top for generation of Summary. \n",
" # Further this model is sent to device (GPU/TPU) for using the hardware.\n",
" model = T5ForConditionalGeneration.from_pretrained(\"t5-base\")\n",
" model = model.to(device)\n",
"\n",
" # Defining the optimizer that will be used to tune the weights of the network in the training session. \n",
" optimizer = torch.optim.Adam(params = model.parameters(), lr=LEARNING_RATE)\n",
"\n",
" # Training loop\n",
"# print('Initiating Fine-Tuning for the model on our dataset')\n",
"\n",
"# for epoch in range(TRAIN_EPOCHS):\n",
"# train(epoch, tokenizer, model, device, training_loader, optimizer)\n",
"\n",
"\n",
" # Validation loop and saving the resulting file with predictions and acutals in a dataframe.\n",
" # Saving the dataframe as predictions.csv\n",
" !mkdir \"./output\"\n",
" print('Now generating summaries on our fine tuned model for the validation dataset and saving it in a dataframe')\n",
" for epoch in range(VAL_EPOCHS):\n",
" predictions, actuals = validate(epoch, tokenizer, model, device, val_loader)\n",
" final_df = pd.DataFrame({'Generated Text':predictions,'Actual Text':actuals})\n",
" final_df.to_csv('predictions_and_actual_for_finetuned_model.csv')\n",
" print('Output Files generated for review')\n",
" print('saving the model weights')\n",
" \n",
" model.save_pretrained(\"./output\")\n",
"\n",
"if __name__ == '__main__':\n",
" main()"
]
}
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| 0073/188/73188408.ipynb | s3://data-agents/kaggle-outputs/sharded/003_00073.jsonl.gz |
{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"---\nDownsize https://www.kaggle.com/xhlulu/vinbigdata-process-and-resize-to-jpg\n\nto 608px and normalize+CLIHE images","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport pydicom\nimport os\nimport cv2\nfrom pydicom.pixel_data_handlers.util import apply_voi_lut\nfrom PIL import Image\nfrom skimage import exposure\nfrom tqdm.auto import tqdm","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-08-26T13:47:58.280903Z","iopub.execute_input":"2021-08-26T13:47:58.281293Z","iopub.status.idle":"2021-08-26T13:47:58.286512Z","shell.execute_reply.started":"2021-08-26T13:47:58.281259Z","shell.execute_reply":"2021-08-26T13:47:58.285521Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"def read_xray(path, voi_lut = True, fix_monochrome = True):\n dicom = pydicom.read_file(path)\n \n # VOI LUT (if available by DICOM device) is used to transform raw DICOM data to \"human-friendly\" view\n if voi_lut:\n data = apply_voi_lut(dicom.pixel_array, dicom)\n else:\n data = dicom.pixel_array\n \n # depending on this value, X-ray may look inverted - fix that:\n if fix_monochrome and dicom.PhotometricInterpretation == \"MONOCHROME1\":\n data = np.amax(data) - data\n \n data = data - np.min(data)\n\n # added\n data = data / np.max(data)\n data = (data * 255).astype(np.uint8)\n \n return data","metadata":{"execution":{"iopub.status.busy":"2021-08-26T13:48:00.027750Z","iopub.execute_input":"2021-08-26T13:48:00.028353Z","iopub.status.idle":"2021-08-26T13:48:00.036182Z","shell.execute_reply.started":"2021-08-26T13:48:00.028317Z","shell.execute_reply":"2021-08-26T13:48:00.034781Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"def resize(img, size, padColor=0):\n\n h, w = img.shape[:2]\n sh, sw = size\n\n # interpolation method\n if h > sh or w > sw: # shrinking image\n interp = cv2.INTER_AREA\n else: # stretching image\n interp = cv2.INTER_CUBIC\n\n # aspect ratio of image\n aspect = w/h # if on Python 2, you might need to cast as a float: float(w)/h\n\n # compute scaling and pad sizing\n if aspect > 1: # horizontal image\n new_w = sw\n new_h = np.round(new_w/aspect).astype(int)\n pad_vert = (sh-new_h)/2\n pad_top, pad_bot = np.floor(pad_vert).astype(int), np.ceil(pad_vert).astype(int)\n pad_left, pad_right = 0, 0\n elif aspect < 1: # vertical image\n new_h = sh\n new_w = np.round(new_h*aspect).astype(int)\n pad_horz = (sw-new_w)/2\n pad_left, pad_right = np.floor(pad_horz).astype(int), np.ceil(pad_horz).astype(int)\n pad_top, pad_bot = 0, 0\n else: # square image\n new_h, new_w = sh, sw\n pad_left, pad_right, pad_top, pad_bot = 0, 0, 0, 0\n\n # set pad color\n if len(img.shape) is 3 and not isinstance(padColor, (list, tuple, np.ndarray)): # color image but only one color provided\n padColor = [padColor]*3\n\n # scale and pad\n scaled_img = cv2.resize(img, (new_w, new_h), interpolation=interp)\n # keep aspect ratio (no padding)\n # scaled_img = cv2.copyMakeBorder(scaled_img, pad_top, pad_bot, pad_left, pad_right, borderType=cv2.BORDER_CONSTANT, value=padColor)\n\n return scaled_img","metadata":{"execution":{"iopub.status.busy":"2021-08-26T13:48:01.774498Z","iopub.execute_input":"2021-08-26T13:48:01.774932Z","iopub.status.idle":"2021-08-26T13:48:01.786959Z","shell.execute_reply.started":"2021-08-26T13:48:01.774898Z","shell.execute_reply":"2021-08-26T13:48:01.785773Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"# test 1 img\nimage_id = []\norig_height = []\norig_width = []\nre_height = []\nre_width = []\n\nfor split in ['train']:\n load_dir = f'../input/vinbigdata-chest-xray-abnormalities-detection/{split}/'\n save_dir = f'/kaggle/working/{split}/'\n \n os.makedirs(save_dir, exist_ok=True)\n\n for file in tqdm(os.listdir(load_dir)):\n xray = read_xray(load_dir + file)\n im = resize(xray, (608,608)) # yolov4 default 608\n im = exposure.equalize_hist(im) # histogram normalization\n im = exposure.equalize_adapthist(im/np.max(im)) #clahe\n cv2.imwrite(save_dir + file.replace('dicom', 'jpg'), im*255)\n \n # shape[0] = height, 1 = width\n if split == 'train':\n image_id.append(file.replace('.dicom', ''))\n re_height.append(im.shape[0])\n re_width.append(im.shape[1])\n orig_height.append(xray.shape[0])\n orig_width.append(xray.shape[1])\n \n break\n break","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls /kaggle/working/train/","metadata":{"execution":{"iopub.status.busy":"2021-08-26T11:34:46.757312Z","iopub.execute_input":"2021-08-26T11:34:46.757754Z","iopub.status.idle":"2021-08-26T11:34:47.512648Z","shell.execute_reply.started":"2021-08-26T11:34:46.757717Z","shell.execute_reply":"2021-08-26T11:34:47.511479Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"2229148faa205e881cf0d932755c9e40.jpg 7ecd6f67f649f26c05805c8359f9e528.jpg\n289f69f6462af4933308c275d07060f0.jpg 8c87779485ae5e21e25cb57e9510e149.jpg\n4d390e07733ba06e5ff07412f09c0a92.jpg aa6475267e83947ee5078281a7ff3df3.jpg\n68335ee73e67706aa59b8b55b54b11a4.jpg ba1795ee5daae1ed415756c3f4f21b48.jpg\n76b23891649862f2b3b95f9bebf0a70c.jpg\n","output_type":"stream"}]},{"cell_type":"code","source":"df_resized = pd.DataFrame.from_dict({\n 'image_id': image_id, \n 're_height': re_height, \n 're_width': re_width,\n 'orig_height': orig_height,\n 'orig_width': orig_width\n})\ndf_resized","metadata":{"execution":{"iopub.status.busy":"2021-08-26T11:35:27.967896Z","iopub.execute_input":"2021-08-26T11:35:27.968293Z","iopub.status.idle":"2021-08-26T11:35:27.989480Z","shell.execute_reply.started":"2021-08-26T11:35:27.968252Z","shell.execute_reply":"2021-08-26T11:35:27.988315Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":" image_id re_height re_width orig_height \\\n0 4d390e07733ba06e5ff07412f09c0a92 608 608 3000 \n1 289f69f6462af4933308c275d07060f0 608 608 3072 \n2 68335ee73e67706aa59b8b55b54b11a4 608 501 2836 \n3 7ecd6f67f649f26c05805c8359f9e528 608 565 2952 \n4 2229148faa205e881cf0d932755c9e40 608 486 2880 \n5 8c87779485ae5e21e25cb57e9510e149 608 507 3170 \n6 76b23891649862f2b3b95f9bebf0a70c 608 549 2819 \n7 ba1795ee5daae1ed415756c3f4f21b48 608 592 3408 \n8 aa6475267e83947ee5078281a7ff3df3 608 463 3408 \n\n orig_width \n0 3000 \n1 3072 \n2 2336 \n3 2744 \n4 2304 \n5 2642 \n6 2545 \n7 3320 \n8 2597 ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>image_id</th>\n <th>re_height</th>\n <th>re_width</th>\n <th>orig_height</th>\n <th>orig_width</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>4d390e07733ba06e5ff07412f09c0a92</td>\n <td>608</td>\n <td>608</td>\n <td>3000</td>\n <td>3000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>289f69f6462af4933308c275d07060f0</td>\n <td>608</td>\n <td>608</td>\n <td>3072</td>\n <td>3072</td>\n </tr>\n <tr>\n <th>2</th>\n <td>68335ee73e67706aa59b8b55b54b11a4</td>\n <td>608</td>\n <td>501</td>\n <td>2836</td>\n <td>2336</td>\n </tr>\n <tr>\n <th>3</th>\n <td>7ecd6f67f649f26c05805c8359f9e528</td>\n <td>608</td>\n <td>565</td>\n <td>2952</td>\n <td>2744</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2229148faa205e881cf0d932755c9e40</td>\n <td>608</td>\n <td>486</td>\n <td>2880</td>\n <td>2304</td>\n </tr>\n <tr>\n <th>5</th>\n <td>8c87779485ae5e21e25cb57e9510e149</td>\n <td>608</td>\n <td>507</td>\n <td>3170</td>\n <td>2642</td>\n </tr>\n <tr>\n <th>6</th>\n <td>76b23891649862f2b3b95f9bebf0a70c</td>\n <td>608</td>\n <td>549</td>\n <td>2819</td>\n <td>2545</td>\n </tr>\n <tr>\n <th>7</th>\n <td>ba1795ee5daae1ed415756c3f4f21b48</td>\n <td>608</td>\n <td>592</td>\n <td>3408</td>\n <td>3320</td>\n </tr>\n <tr>\n <th>8</th>\n <td>aa6475267e83947ee5078281a7ff3df3</td>\n <td>608</td>\n <td>463</td>\n <td>3408</td>\n <td>2597</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# resize\nimage_id = []\norig_height = []\norig_width = []\nre_height = []\nre_width = []\n\nfor split in ['train', 'test']:\n load_dir = f'../input/vinbigdata-chest-xray-abnormalities-detection/{split}/'\n save_dir = f'/kaggle/tmp/{split}/'\n# save_dir = f'/kaggle/working/{split}/'\n\n\n os.makedirs(save_dir, exist_ok=True)\n\n for file in tqdm(os.listdir(load_dir)):\n xray = read_xray(load_dir + file)\n im = resize(xray, (608,608)) # yolov4 default 608\n im = exposure.equalize_hist(im) # histogram normalization\n im = exposure.equalize_adapthist(im/np.max(im)) #clahe\n cv2.imwrite(save_dir + file.replace('dicom', 'jpg'), im*255)\n \n # shape[0] = height, 1 = width\n if split == 'train':\n image_id.append(file.replace('.dicom', ''))\n re_height.append(im.shape[0])\n re_width.append(im.shape[1])\n orig_height.append(xray.shape[0])\n orig_width.append(xray.shape[1])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_resized = pd.DataFrame.from_dict({\n 'image_id': image_id, \n 're_height': re_height, \n 're_width': re_width,\n 'orig_height': orig_height,\n 'orig_width': orig_width\n})\n","metadata":{"execution":{"iopub.status.busy":"2021-08-26T11:48:07.656296Z","iopub.execute_input":"2021-08-26T11:48:07.656926Z","iopub.status.idle":"2021-08-26T11:48:07.667152Z","shell.execute_reply.started":"2021-08-26T11:48:07.656880Z","shell.execute_reply":"2021-08-26T11:48:07.665888Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"---\nclean up","metadata":{}},{"cell_type":"code","source":"%cd /kaggle/tmp/\n!ls","metadata":{"execution":{"iopub.status.busy":"2021-08-26T11:59:35.679224Z","iopub.execute_input":"2021-08-26T11:59:35.679686Z","iopub.status.idle":"2021-08-26T11:59:36.421508Z","shell.execute_reply.started":"2021-08-26T11:59:35.679628Z","shell.execute_reply":"2021-08-26T11:59:36.420271Z"},"trusted":true},"execution_count":43,"outputs":[{"name":"stdout","text":"/kaggle/tmp\ntest train\n","output_type":"stream"}]},{"cell_type":"code","source":"%cd /kaggle/tmp/train/\ndir = f'/kaggle/tmp/train/'\n\nfor file in os.listdir(dir):\n im_id = file[:-4]\n print(file)\n break\nim_id","metadata":{"execution":{"iopub.status.busy":"2021-08-26T11:59:38.247117Z","iopub.execute_input":"2021-08-26T11:59:38.247503Z","iopub.status.idle":"2021-08-26T11:59:38.270722Z","shell.execute_reply.started":"2021-08-26T11:59:38.247463Z","shell.execute_reply":"2021-08-26T11:59:38.269975Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stdout","text":"/kaggle/tmp/train\n5ca7d0473877e6d03d94a171d55cec89.jpg\n","output_type":"stream"},{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"'5ca7d0473877e6d03d94a171d55cec89'"},"metadata":{}}]},{"cell_type":"code","source":"# shape[0] = height, 1 = width\n# resize\nimage_id = []\n# orig_height = []\n# orig_width = []\nre_height = []\nre_width = []\n\ndir = f'/kaggle/tmp/train/'\nfor file in tqdm(os.listdir(dir)):\n im_id = file[:-4]\n im = cv2.imread(file)\n\n image_id.append(im_id)\n re_height.append(im.shape[0])\n re_width.append(im.shape[1])","metadata":{"execution":{"iopub.status.busy":"2021-08-26T11:59:46.771982Z","iopub.execute_input":"2021-08-26T11:59:46.772529Z","iopub.status.idle":"2021-08-26T12:01:04.113985Z","shell.execute_reply.started":"2021-08-26T11:59:46.772478Z","shell.execute_reply":"2021-08-26T12:01:04.112959Z"},"trusted":true},"execution_count":45,"outputs":[{"output_type":"display_data","data":{"text/plain":" 0%| | 0/15000 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d232e466aedf4fe190211a020e1f97f2"}},"metadata":{}}]},{"cell_type":"code","source":"df_resized = pd.DataFrame.from_dict({\n 'image_id': image_id, \n 're_height': re_height, \n 're_width': re_width\n})","metadata":{"execution":{"iopub.status.busy":"2021-08-26T12:01:26.519085Z","iopub.execute_input":"2021-08-26T12:01:26.519450Z","iopub.status.idle":"2021-08-26T12:01:26.544335Z","shell.execute_reply.started":"2021-08-26T12:01:26.519418Z","shell.execute_reply":"2021-08-26T12:01:26.542772Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"markdown","source":"---\nCreate yolov4 txt files\n\nhttps://www.kaggle.com/jackpodkim/vbd-convert-labels-to-yolo-yolov4/edit","metadata":{}},{"cell_type":"code","source":"%cd /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2021-08-26T22:57:39.001772Z","iopub.execute_input":"2021-08-26T22:57:39.002280Z","iopub.status.idle":"2021-08-26T22:57:39.013506Z","shell.execute_reply.started":"2021-08-26T22:57:39.002185Z","shell.execute_reply":"2021-08-26T22:57:39.012229Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/working\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\n\nimport pydicom\nimport glob\n\ndf = pd.read_csv(\"../input/vinbigdata-chest-xray-abnormalities-detection/train.csv\")\n\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-26T22:57:41.967049Z","iopub.execute_input":"2021-08-26T22:57:41.967330Z","iopub.status.idle":"2021-08-26T22:57:42.478183Z","shell.execute_reply.started":"2021-08-26T22:57:41.967306Z","shell.execute_reply":"2021-08-26T22:57:42.476618Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":" image_id class_name class_id rad_id \\\n0 50a418190bc3fb1ef1633bf9678929b3 No finding 14 R11 \n1 21a10246a5ec7af151081d0cd6d65dc9 No finding 14 R7 \n2 9a5094b2563a1ef3ff50dc5c7ff71345 Cardiomegaly 3 R10 \n3 051132a778e61a86eb147c7c6f564dfe Aortic enlargement 0 R10 \n4 063319de25ce7edb9b1c6b8881290140 No finding 14 R10 \n\n x_min y_min x_max y_max \n0 NaN NaN NaN NaN \n1 NaN NaN NaN NaN \n2 691.0 1375.0 1653.0 1831.0 \n3 1264.0 743.0 1611.0 1019.0 \n4 NaN NaN NaN NaN ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>image_id</th>\n <th>class_name</th>\n <th>class_id</th>\n <th>rad_id</th>\n <th>x_min</th>\n <th>y_min</th>\n <th>x_max</th>\n <th>y_max</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>50a418190bc3fb1ef1633bf9678929b3</td>\n <td>No finding</td>\n <td>14</td>\n <td>R11</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1</th>\n <td>21a10246a5ec7af151081d0cd6d65dc9</td>\n <td>No finding</td>\n <td>14</td>\n <td>R7</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>9a5094b2563a1ef3ff50dc5c7ff71345</td>\n <td>Cardiomegaly</td>\n <td>3</td>\n <td>R10</td>\n <td>691.0</td>\n <td>1375.0</td>\n <td>1653.0</td>\n <td>1831.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>051132a778e61a86eb147c7c6f564dfe</td>\n <td>Aortic enlargement</td>\n <td>0</td>\n <td>R10</td>\n <td>1264.0</td>\n <td>743.0</td>\n <td>1611.0</td>\n <td>1019.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>063319de25ce7edb9b1c6b8881290140</td>\n <td>No finding</td>\n <td>14</td>\n <td>R10</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"dicom_metadata = [pydicom.filereader.dcmread(f\"../input/vinbigdata-chest-xray-abnormalities-detection/train/{image_id}.dicom\", stop_before_pixels=True) for image_id in df['image_id']]","metadata":{"execution":{"iopub.status.busy":"2021-08-26T22:57:56.592691Z","iopub.execute_input":"2021-08-26T22:57:56.592977Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df['orig_width'] = [i.Columns for i in dicom_metadata]\ndf['orig_height'] = [i.Rows for i in dicom_metadata]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = df[df.class_id!=14].reset_index(drop = True)\n\nprint(\"We have {} unique images with boxes.\".format(len(df.image_id.unique())))\nunique_img_ids = df.image_id.unique()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = df.merge(df_resized, how='left', on='image_id')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head().T","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # resized reindex\n# df['x'] = df.apply(lambda row: row.x_min*(row.re_width/row.orig_width), axis =1)\n# df['y'] = df.apply(lambda row: row.y_min*(row.re_height/row.orig_height), axis =1)\n\n# df['x_re_max'] = df.apply(lambda row: row.x_max*(row.re_width/row.orig_width), axis =1)\n# df['y_re_max'] = df.apply(lambda row: row.y_max*(row.re_height/row.orig_height), axis =1)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # resized reindex\n# df['x_re_min'] = df.apply(lambda row: row.x_min*(row.re_width_x/row.orig_width), axis =1)\n# df['y_re_min'] = df.apply(lambda row: row.y_min*(row.re_height_x/row.orig_height), axis =1)\n\n# df['x_re_max'] = df.apply(lambda row: row.x_max*(row.re_width_x/row.orig_width), axis =1)\n# df['y_re_max'] = df.apply(lambda row: row.y_max*(row.re_height_x/row.orig_height), axis =1)","metadata":{"execution":{"iopub.status.busy":"2021-08-26T12:51:50.882555Z","iopub.execute_input":"2021-08-26T12:51:50.883026Z","iopub.status.idle":"2021-08-26T12:51:56.269501Z","shell.execute_reply.started":"2021-08-26T12:51:50.882985Z","shell.execute_reply":"2021-08-26T12:51:56.268445Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"# yolov4 format\ndf['x_mid'] = df.apply(lambda row: (row.x_min+row.x_max)/2, axis =1)\ndf['y_mid'] = df.apply(lambda row: (row.y_re_max+row.y_re_min)/2, axis =1)\n\n# df['w'] = df.apply(lambda row: (row.x_re_max-row.x_re_min), axis =1)\n# df['h'] = df.apply(lambda row: (row.y_re_max-row.y_re_min), axis =1)\n\n# df['area'] = df['w']*df['h']\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-26T12:52:07.497565Z","iopub.execute_input":"2021-08-26T12:52:07.498020Z","iopub.status.idle":"2021-08-26T12:52:11.515576Z","shell.execute_reply.started":"2021-08-26T12:52:07.497982Z","shell.execute_reply":"2021-08-26T12:52:11.513984Z"},"trusted":true},"execution_count":80,"outputs":[{"execution_count":80,"output_type":"execute_result","data":{"text/plain":" image_id class_name class_id rad_id \\\n0 9a5094b2563a1ef3ff50dc5c7ff71345 Cardiomegaly 3 R10 \n1 051132a778e61a86eb147c7c6f564dfe Aortic enlargement 0 R10 \n2 1c32170b4af4ce1a3030eb8167753b06 Pleural thickening 11 R9 \n3 0c7a38f293d5f5e4846aa4ca6db4daf1 ILD 5 R17 \n4 47ed17dcb2cbeec15182ed335a8b5a9e Nodule/Mass 8 R9 \n\n x_min y_min x_max y_max orig_width orig_height ... y_re_min \\\n0 691.0 1375.0 1653.0 1831.0 2080 2336 ... 357.876712 \n1 1264.0 743.0 1611.0 1019.0 2304 2880 ... 156.855556 \n2 627.0 357.0 947.0 433.0 2540 3072 ... 70.656250 \n3 1347.0 245.0 2188.0 2169.0 2285 2555 ... 58.301370 \n4 557.0 2352.0 675.0 2484.0 2568 3353 ... 426.488518 \n\n x_re_max y_re_max x_mid y_mid w h \\\n0 429.938942 476.561644 304.832692 417.219178 250.212500 118.684932 \n1 339.820312 215.122222 303.222656 185.988889 73.195312 58.266667 \n2 187.535827 85.697917 155.850787 78.177083 63.370079 15.041667 \n3 520.906783 516.145597 420.796499 287.223483 200.220569 457.844227 \n4 122.488318 450.424098 111.781931 438.456308 21.412773 23.935580 \n\n area re_height_y re_width_y \n0 29696.453425 608 541 \n1 4264.846875 608 486 \n2 953.191601 608 503 \n3 91669.831611 608 544 \n4 512.527133 608 466 \n\n[5 rows x 23 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>image_id</th>\n <th>class_name</th>\n <th>class_id</th>\n <th>rad_id</th>\n <th>x_min</th>\n <th>y_min</th>\n <th>x_max</th>\n <th>y_max</th>\n <th>orig_width</th>\n <th>orig_height</th>\n <th>...</th>\n <th>y_re_min</th>\n <th>x_re_max</th>\n <th>y_re_max</th>\n <th>x_mid</th>\n <th>y_mid</th>\n <th>w</th>\n <th>h</th>\n <th>area</th>\n <th>re_height_y</th>\n <th>re_width_y</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>9a5094b2563a1ef3ff50dc5c7ff71345</td>\n <td>Cardiomegaly</td>\n <td>3</td>\n <td>R10</td>\n <td>691.0</td>\n <td>1375.0</td>\n <td>1653.0</td>\n <td>1831.0</td>\n <td>2080</td>\n <td>2336</td>\n <td>...</td>\n <td>357.876712</td>\n <td>429.938942</td>\n <td>476.561644</td>\n <td>304.832692</td>\n <td>417.219178</td>\n <td>250.212500</td>\n <td>118.684932</td>\n <td>29696.453425</td>\n <td>608</td>\n <td>541</td>\n </tr>\n <tr>\n <th>1</th>\n <td>051132a778e61a86eb147c7c6f564dfe</td>\n <td>Aortic enlargement</td>\n <td>0</td>\n <td>R10</td>\n <td>1264.0</td>\n <td>743.0</td>\n <td>1611.0</td>\n <td>1019.0</td>\n <td>2304</td>\n <td>2880</td>\n <td>...</td>\n <td>156.855556</td>\n <td>339.820312</td>\n <td>215.122222</td>\n <td>303.222656</td>\n <td>185.988889</td>\n <td>73.195312</td>\n <td>58.266667</td>\n <td>4264.846875</td>\n <td>608</td>\n <td>486</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1c32170b4af4ce1a3030eb8167753b06</td>\n <td>Pleural thickening</td>\n <td>11</td>\n <td>R9</td>\n <td>627.0</td>\n <td>357.0</td>\n <td>947.0</td>\n <td>433.0</td>\n <td>2540</td>\n <td>3072</td>\n <td>...</td>\n <td>70.656250</td>\n <td>187.535827</td>\n <td>85.697917</td>\n <td>155.850787</td>\n <td>78.177083</td>\n <td>63.370079</td>\n <td>15.041667</td>\n <td>953.191601</td>\n <td>608</td>\n <td>503</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0c7a38f293d5f5e4846aa4ca6db4daf1</td>\n <td>ILD</td>\n <td>5</td>\n <td>R17</td>\n <td>1347.0</td>\n <td>245.0</td>\n <td>2188.0</td>\n <td>2169.0</td>\n <td>2285</td>\n <td>2555</td>\n <td>...</td>\n <td>58.301370</td>\n <td>520.906783</td>\n <td>516.145597</td>\n <td>420.796499</td>\n <td>287.223483</td>\n <td>200.220569</td>\n <td>457.844227</td>\n <td>91669.831611</td>\n <td>608</td>\n <td>544</td>\n </tr>\n <tr>\n <th>4</th>\n <td>47ed17dcb2cbeec15182ed335a8b5a9e</td>\n <td>Nodule/Mass</td>\n <td>8</td>\n <td>R9</td>\n <td>557.0</td>\n <td>2352.0</td>\n <td>675.0</td>\n <td>2484.0</td>\n <td>2568</td>\n <td>3353</td>\n <td>...</td>\n <td>426.488518</td>\n <td>122.488318</td>\n <td>450.424098</td>\n <td>111.781931</td>\n <td>438.456308</td>\n <td>21.412773</td>\n <td>23.935580</td>\n <td>512.527133</td>\n <td>608</td>\n <td>466</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df['yolo_box'] = df[['x_mid', 'y_mid', 'w', 'h']].values.tolist()\n\nprint(\"We have {} unique images with boxes.\".format(len(df.image_id.unique())))\nunique_img_ids = df.image_id.unique()","metadata":{"execution":{"iopub.status.busy":"2021-08-26T12:52:29.080182Z","iopub.execute_input":"2021-08-26T12:52:29.080547Z","iopub.status.idle":"2021-08-26T12:52:29.136698Z","shell.execute_reply.started":"2021-08-26T12:52:29.080514Z","shell.execute_reply":"2021-08-26T12:52:29.135497Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"We have 4394 unique images with boxes.\n","output_type":"stream"}]},{"cell_type":"code","source":"%cd /kaggle/tmp/","metadata":{"execution":{"iopub.status.busy":"2021-08-26T12:52:32.204526Z","iopub.execute_input":"2021-08-26T12:52:32.204946Z","iopub.status.idle":"2021-08-26T12:52:32.215529Z","shell.execute_reply.started":"2021-08-26T12:52:32.204910Z","shell.execute_reply":"2021-08-26T12:52:32.213639Z"},"trusted":true},"execution_count":82,"outputs":[{"name":"stdout","text":"/kaggle/tmp\n","output_type":"stream"}]},{"cell_type":"code","source":"folder_location = \"/kaggle/tmp/train/\"\n\nfor img_id in tqdm(unique_img_ids): # loop through all unique image ids. Remove the slice to do all images\n filt_df = df.query(\"image_id == @img_id\") # filter the df to a specific id\n #all_boxes = filt_df.yolo_box.values\n file_name = \"{}/{}.txt\".format(folder_location,img_id) # specify the name of the folder and get a file name\n\n with open(file_name, 'w+') as file: # append lines to file\n for i in filt_df.iterrows():\n s = f\"{i[1].class_id} %s %s %s %s \\n\" # The first number is the class name\n new_line = (s % tuple(i[1].yolo_box))\n file.write(new_line)","metadata":{"execution":{"iopub.status.busy":"2021-08-26T12:52:36.120926Z","iopub.execute_input":"2021-08-26T12:52:36.121322Z","iopub.status.idle":"2021-08-26T12:53:05.588538Z","shell.execute_reply.started":"2021-08-26T12:52:36.121287Z","shell.execute_reply":"2021-08-26T12:53:05.587197Z"},"trusted":true},"execution_count":83,"outputs":[{"output_type":"display_data","data":{"text/plain":" 0%| | 0/4394 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ce2a225e3fb34bdf85889a73dacd37bb"}},"metadata":{}}]},{"cell_type":"code","source":"!ls /kaggle/tmp/train/","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create labels for training images that do not have bounding boxes\n# If you wish to train on only images with a finding, remove this code cell\nall_imgs = glob.glob(\"../input/vinbigdata-chest-xray-abnormalities-detection/train/*.dicom\")\nall_imgs = [i.split(\"/\")[-1].replace(\".dicom\", \"\") for i in all_imgs]\npositive_imgs = df.image_id.unique()\n\nnegative_images = set(all_imgs) - set(positive_imgs)\nprint('All images:', len(all_imgs), 'Positive images:', len(positive_imgs))\n\nfor i in tqdm(list(negative_images)):\n file_name = \"{}/{}.txt\".format(folder_location, i)\n #print(file_name)\n with open(file_name, 'w') as fp:\n pass","metadata":{"execution":{"iopub.status.busy":"2021-08-26T12:55:10.124957Z","iopub.execute_input":"2021-08-26T12:55:10.125350Z","iopub.status.idle":"2021-08-26T12:55:10.850690Z","shell.execute_reply.started":"2021-08-26T12:55:10.125315Z","shell.execute_reply":"2021-08-26T12:55:10.849437Z"},"trusted":true},"execution_count":91,"outputs":[{"name":"stdout","text":"All images: 15000 Positive images: 4394\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" 0%| | 0/10606 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6fa58f0740fb481cb18703c90c7b5e49"}},"metadata":{}}]},{"cell_type":"code","source":"%%capture\n\n# zip to make files easier to download\n\n!zip -r yolo_labels.zip /kaggle/tmp/","metadata":{"execution":{"iopub.status.busy":"2021-08-26T12:55:16.898617Z","iopub.execute_input":"2021-08-26T12:55:16.898992Z","iopub.status.idle":"2021-08-26T12:57:09.670506Z","shell.execute_reply.started":"2021-08-26T12:55:16.898961Z","shell.execute_reply":"2021-08-26T12:57:09.668996Z"},"trusted":true},"execution_count":92,"outputs":[]},{"cell_type":"code","source":"!mv /kaggle/tmp/yolo_labels.zip /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2021-08-26T12:57:09.672686Z","iopub.execute_input":"2021-08-26T12:57:09.673021Z","iopub.status.idle":"2021-08-26T12:57:13.255559Z","shell.execute_reply.started":"2021-08-26T12:57:09.672985Z","shell.execute_reply":"2021-08-26T12:57:13.253938Z"},"trusted":true},"execution_count":93,"outputs":[]},{"cell_type":"markdown","source":"---\nclean up","metadata":{}},{"cell_type":"code","source":"# resize\nimage_id = []\norig_height = []\norig_width = []\nre_height = []\nre_width = []\n\n#test\nload_dir = f'../input/vinbigdata-chest-xray-abnormalities-detection/test/'\nsave_dir = f'/kaggle/working/test/'\n# save_dir = f'/kaggle/working/{split}/'\n\n\nos.makedirs(save_dir, exist_ok=True)\n\nfor file in tqdm(os.listdir(load_dir)):\n xray = read_xray(load_dir + file)\n im = resize(xray, (608,608)) # yolov4 default 608\n im = exposure.equalize_hist(im) # histogram normalization\n im = exposure.equalize_adapthist(im/np.max(im)) #clahe\n cv2.imwrite(save_dir + file.replace('dicom', 'jpg'), im*255)","metadata":{"execution":{"iopub.status.busy":"2021-08-26T13:48:16.773966Z","iopub.execute_input":"2021-08-26T13:48:16.774372Z","iopub.status.idle":"2021-08-26T14:36:11.716511Z","shell.execute_reply.started":"2021-08-26T13:48:16.774338Z","shell.execute_reply":"2021-08-26T14:36:11.714297Z"},"trusted":true},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":" 0%| | 0/3000 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"25d4644996d441389625bfac3509b52d"}},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/pydicom/pixel_data_handlers/pillow_handler.py:177: UserWarning: The (0028,0101) 'Bits Stored' value (12-bit) doesn't match the JPEG 2000 data (16-bit). It's recommended that you change the 'Bits Stored' value\n f\"The (0028,0101) 'Bits Stored' value ({ds.BitsStored}-bit) \"\n/opt/conda/lib/python3.7/site-packages/pydicom/pixel_data_handlers/pillow_handler.py:177: UserWarning: The (0028,0101) 'Bits Stored' value (14-bit) doesn't match the JPEG 2000 data (16-bit). It's recommended that you change the 'Bits Stored' value\n f\"The (0028,0101) 'Bits Stored' value ({ds.BitsStored}-bit) \"\n","output_type":"stream"}]},{"cell_type":"code","source":"%%capture\n\n# zip to make files easier to download\n\n!zip -r yolo_test.zip /kaggle/working/test","metadata":{"execution":{"iopub.status.busy":"2021-08-26T13:32:53.366208Z","iopub.execute_input":"2021-08-26T13:32:53.366580Z","iopub.status.idle":"2021-08-26T13:32:54.152417Z","shell.execute_reply.started":"2021-08-26T13:32:53.366548Z","shell.execute_reply":"2021-08-26T13:32:54.150757Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb\n","output_type":"stream"}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"!ls","metadata":{"execution":{"iopub.status.busy":"2021-08-26T14:51:42.179491Z","iopub.execute_input":"2021-08-26T14:51:42.180389Z","iopub.status.idle":"2021-08-26T14:51:43.007237Z","shell.execute_reply.started":"2021-08-26T14:51:42.180258Z","shell.execute_reply":"2021-08-26T14:51:43.005811Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"__notebook_source__.ipynb test\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} | 0073/188/73188431.ipynb | s3://data-agents/kaggle-outputs/sharded/003_00073.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"9e15c7(...TRUNCATED) | 0073/188/73188506.ipynb | s3://data-agents/kaggle-outputs/sharded/003_00073.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"cc9a42(...TRUNCATED) | 0073/188/73188931.ipynb | s3://data-agents/kaggle-outputs/sharded/003_00073.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"eaccbc(...TRUNCATED) | 0073/189/73189264.ipynb | s3://data-agents/kaggle-outputs/sharded/003_00073.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e6a458b9\",\n \"metadata\": (...TRUNCATED) | 0073/189/73189361.ipynb | s3://data-agents/kaggle-outputs/sharded/003_00073.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"ca4c5c(...TRUNCATED) | 0073/189/73189836.ipynb | s3://data-agents/kaggle-outputs/sharded/003_00073.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cce75370\",\n \"metadata\": (...TRUNCATED) | 0073/189/73189891.ipynb | s3://data-agents/kaggle-outputs/sharded/003_00073.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"7fce1a(...TRUNCATED) | 0073/190/73190840.ipynb | s3://data-agents/kaggle-outputs/sharded/003_00073.jsonl.gz |
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