{
"cells": [
{
"cell_type": "markdown",
"id": "00af697e",
"metadata": {},
"source": [
"# EDA"
]
},
{
"cell_type": "markdown",
"id": "3984890a",
"metadata": {},
"source": [
"This is a review for the dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da6853d1",
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"id": "6d327cf8",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bce1375f",
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"import os\n",
"from fastdownload import download_url\n",
"from IPython.display import display, Image\n",
"from PIL import Image as PILImage\n",
"import shutil\n",
"from ipywidgets.widgets import interact\n",
"from sklearn.model_selection import train_test_split\n",
"from aiking.data.external import * #We need to import this after fastai modules"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d45e332",
"metadata": {},
"outputs": [],
"source": [
"sns.set(color_codes=True)\n",
"sns.set_palette(sns.color_palette(\"muted\"))\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"id": "3a50be69",
"metadata": {},
"source": [
"## Getting Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a872e5b5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#5) [Path('/Landmark2/pdo/aiking/data/ultra-mnist/train'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/ultra-mnist.zip'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/train.csv'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/sample_submission.csv')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# kaggle competitions download -c ultra-mnist\n",
"path = untar_data(\"kaggle_competitions::ultra-mnist\"); path.ls()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a56b6ef2",
"metadata": {},
"outputs": [],
"source": [
"# !unzip /Landmark2/pdo/aiking/data/ultra-mnist/ultra-mnist.zip -d /Landmark2/pdo/aiking/data/ultra-mnist"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "22b9baf2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pd.read_csv(path/\"train.csv\")['digit_sum'].value_counts().sort_index().plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"id": "9a1c23ae-7576-4c45-a619-145598b2e2a2",
"metadata": {},
"source": [
"## Simple Data Visualization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27f611bf",
"metadata": {},
"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>id</th>\n",
" <th>digit_sum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>vyctxmodyu</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>kghlqhpshk</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>rfznywdgjo</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>zllfkikwrw</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>qeiqjoqakl</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27995</th>\n",
" <td>tmsimiaoed</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27996</th>\n",
" <td>zoewngbuwq</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27997</th>\n",
" <td>edxdmumlvt</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27998</th>\n",
" <td>vomwiagnxi</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27999</th>\n",
" <td>uvogzysotf</td>\n",
" <td>21</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>28000 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" id digit_sum\n",
"0 vyctxmodyu 12\n",
"1 kghlqhpshk 14\n",
"2 rfznywdgjo 19\n",
"3 zllfkikwrw 24\n",
"4 qeiqjoqakl 20\n",
"... ... ...\n",
"27995 tmsimiaoed 26\n",
"27996 zoewngbuwq 0\n",
"27997 edxdmumlvt 1\n",
"27998 vomwiagnxi 13\n",
"27999 uvogzysotf 21\n",
"\n",
"[28000 rows x 2 columns]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv(path/\"train.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e65ec421",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#28000) [Path('/Landmark2/pdo/aiking/data/ultra-mnist/test/shrxqvnwni.jpeg'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test/ewandndkhm.jpeg'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test/gldzgpmcox.jpeg'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test/mzahedfegm.jpeg'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test/zfnsiamdih.jpeg'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test/axoyjjoraz.jpeg'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test/xcmrpmdwpm.jpeg'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test/xyuiqnuugg.jpeg'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test/nsybrzrmmb.jpeg'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test/immkpvyqyb.jpeg')...]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(path/\"test\").ls()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79f3d2fc-f194-4b92-813c-a989e1d5342e",
"metadata": {},
"outputs": [],
"source": [
"images = (path/\"train\").ls()\n",
"df_train = pd.read_csv(path/\"train.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cd19835-3450-470e-8213-c1539f78919a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n"
]
}
],
"source": [
"print(df_train[df_train['id'] == \"shrxqvnwni\"]['digit_sum'].values)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b9683e28",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "006f7f57a5bc45f793694c8c7c8f4d27",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=14000, description='idx', max=28000), Output()), _dom_classes=('widget-i…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"@interact(idx=(0, len(images)))\n",
"def display_img(idx):\n",
" img_loc = images[idx]\n",
" img_name = os.path.splitext(img_loc.name)[0]\n",
" print(df_train[df_train['id'] == img_name]['digit_sum'].values)\n",
" \n",
" # return Image(img_loc, width=400, height=400)\n",
" photo = plt.imread(img_loc)\n",
" photo = photo.astype('int')\n",
" return plt.imshow(photo)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab01c20e",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3434e5a7231641578ca6273863889793",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=14000, description='idx', max=28000), Output()), _dom_classes=('widget-i…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test_images = (path/\"test\").ls()\n",
"range(len(images))\n",
"\n",
"@interact(idx=(0, len(test_images)))\n",
"def display_img(idx):\n",
" return Image(test_images[idx], width=400, height=400)"
]
},
{
"cell_type": "markdown",
"id": "67deea19-0493-4a40-ab93-059539a99cb9",
"metadata": {},
"source": [
"## Train Test Split"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aba48be5-4248-4d05-8be8-da55b0e0ec12",
"metadata": {},
"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>id</th>\n",
" <th>digit_sum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>vyctxmodyu</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>kghlqhpshk</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>rfznywdgjo</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>zllfkikwrw</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>qeiqjoqakl</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27995</th>\n",
" <td>tmsimiaoed</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27996</th>\n",
" <td>zoewngbuwq</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27997</th>\n",
" <td>edxdmumlvt</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27998</th>\n",
" <td>vomwiagnxi</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27999</th>\n",
" <td>uvogzysotf</td>\n",
" <td>21</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>28000 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" id digit_sum\n",
"0 vyctxmodyu 12\n",
"1 kghlqhpshk 14\n",
"2 rfznywdgjo 19\n",
"3 zllfkikwrw 24\n",
"4 qeiqjoqakl 20\n",
"... ... ...\n",
"27995 tmsimiaoed 26\n",
"27996 zoewngbuwq 0\n",
"27997 edxdmumlvt 1\n",
"27998 vomwiagnxi 13\n",
"27999 uvogzysotf 21\n",
"\n",
"[28000 rows x 2 columns]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(path/\"train.csv\")\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fd211da0-7277-46c1-97b8-1059335aed88",
"metadata": {},
"outputs": [],
"source": [
"df_train, df_valid= train_test_split(df, test_size=0.2, stratify=df['digit_sum'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a504230-6d49-4be7-b59e-d6a8d0409271",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((22400, 2), (5600, 2))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.shape, df_valid.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ace2683-e441-4125-991b-feb0fa79fbd7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_train['digit_sum'].value_counts().sort_index().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "996fe528-47c7-42ab-8d51-63e75a1a08cf",
"metadata": {},
"outputs": [],
"source": [
"df_train.to_csv(path/\"train_train.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee132272-f242-407f-ad71-677c9f0ed84f",
"metadata": {},
"outputs": [],
"source": [
"df_valid.to_csv(path/\"valid_train.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e7d63ce0-c915-4d3b-adf4-fde09c30c785",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#7) [Path('/Landmark2/pdo/aiking/data/ultra-mnist/train'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/train_train.csv'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/valid_train.csv'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/ultra-mnist.zip'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/train.csv'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/sample_submission.csv')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path.ls()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af74a6db-de78-4caa-95af-a0e340532a1f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id,digit_sum\n",
"vyctxmodyu,12\n",
"kghlqhpshk,14\n",
"rfznywdgjo,19\n",
"zllfkikwrw,24\n",
"qeiqjoqakl,20\n",
"kgybaqqiuf,20\n",
"decsgvbjlk,5\n",
"vzinebdzrx,8\n",
"irjmhkxvxw,1\n"
]
}
],
"source": [
"!head {path}/\"train.csv\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1a72de5a-02a6-4efa-86f2-9b1fbe53cf62",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id,digit_sum\n",
"fneyofrewl,5\n",
"qabcaosvod,21\n",
"ifgkabsbtd,26\n",
"amtrieynfn,19\n",
"ypdcmwqliz,21\n",
"gkxsysyloo,13\n",
"hlzhqfuoxw,27\n",
"pytumbeqxd,4\n",
"rolquggkcx,17\n"
]
}
],
"source": [
"!head {path}/\"train_train.csv\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "284ff962-868b-4d21-b512-40fd2883a5c4",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image((path/\"train\").ls()[0])"
]
},
{
"cell_type": "markdown",
"id": "b243527e-39ac-45dc-bd49-73e7f068afde",
"metadata": {},
"source": [
"## Create a sample"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88968e88-a105-45ae-804f-6ab2d07ee6e2",
"metadata": {},
"outputs": [],
"source": [
"df_train, df_sample= train_test_split(df, test_size=0.02, stratify=df['digit_sum'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1dcd19e7-34e6-47c2-899f-b9d2d1de5ff9",
"metadata": {},
"outputs": [],
"source": [
"df_sample.to_csv(path/\"sample.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af0850aa-cfa4-45cc-b188-3fe4c82eaa35",
"metadata": {},
"outputs": [],
"source": [
"df_train, df_valid= train_test_split(df_sample, test_size=0.2, stratify=df_sample['digit_sum'])\n",
"df_train.to_csv(path/\"train_sample.csv\", index=False)\n",
"df_valid.to_csv(path/\"valid_sample.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c111d8b-3c97-43a1-b063-05ae0e314502",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#10) [Path('/Landmark2/pdo/aiking/data/ultra-mnist/train_sample.csv'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/train'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/sample.csv'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/train_train.csv'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/valid_train.csv'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/ultra-mnist.zip'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/valid_sample.csv'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/train.csv'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/test'),Path('/Landmark2/pdo/aiking/data/ultra-mnist/sample_submission.csv')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path.ls()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c75fa496-9d75-4478-899c-c07d0cde1d9d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(448, 2)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "878b3f02-47e0-4c9b-9971-f23af2989ba8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(112, 2)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_valid.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff7af9b6-ccd1-4945-a420-17cf9e6ac6b1",
"metadata": {},
"outputs": [],
"source": [
"def copyfile(fname):\n",
" src = path/\"train\"/fname\n",
" dest = path/\"sample\"/fname\n",
" shutil.copyfile(src, dest)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b20b83b-547a-4de7-9637-0d917cb347a6",
"metadata": {},
"outputs": [],
"source": [
"# !rm -rf \n",
"!rm -rf {path/\"sample\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0bb6e8c5-c25f-4dbc-8c2b-15333bf014d0",
"metadata": {},
"outputs": [],
"source": [
"!mkdir {path/\"sample\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6b316e9-f5b6-4d5f-8859-795b0f03882d",
"metadata": {},
"outputs": [],
"source": [
"df_sample['id'] = df_sample['id']+\".jpeg\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "478be225-832b-4fc1-a599-0e66fe22e87d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"21387 None\n",
"15672 None\n",
"13161 None\n",
"7152 None\n",
"26522 None\n",
" ... \n",
"11771 None\n",
"15287 None\n",
"27202 None\n",
"19250 None\n",
"248 None\n",
"Name: id, Length: 560, dtype: object"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_sample['id'].map(copyfile)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "42c39d64-8631-4f9b-956f-4554118a0aa1",
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": 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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image((path/\"sample\").ls()[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cff4daa0-2cd6-435d-baad-5b0fc26e9354",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:aiking] *",
"language": "python",
"name": "conda-env-aiking-py"
}
},
"nbformat": 4,
"nbformat_minor": 5
}