test du notebook sur les refs
This commit is contained in:
François Vieille
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 132,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 133,
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"metadata": {},
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"outputs": [],
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"source": [
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"refs = pd.read_csv('../data/external/refs/references_labels.csv')"
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]
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},
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{
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"cell_type": "code",
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|
||||
"source": [
|
||||
"refs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 155,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"refs2 = refs.pivot_table(index=\"image\", columns=\"label\", aggfunc=len, fill_value=0).reset_index()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 156,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Index(['image', 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='object', name='label')"
|
||||
]
|
||||
},
|
||||
"execution_count": 156,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"refs2.columns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 105,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"refs2.index.name = None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 136,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ValueError",
|
||||
"evalue": "Length mismatch: Expected axis has 10 elements, new values have 9 elements",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-136-d55c82b68b48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrefs2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"label\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrefs2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'image'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m 3625\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3626\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3627\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3628\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3629\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[0;34m()\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_set_axis\u001b[0;34m(self, axis, labels)\u001b[0m\n\u001b[1;32m 557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 558\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_set_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 559\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 560\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mset_axis\u001b[0;34m(self, axis, new_labels)\u001b[0m\n\u001b[1;32m 3072\u001b[0m raise ValueError('Length mismatch: Expected axis has %d elements, '\n\u001b[1;32m 3073\u001b[0m \u001b[0;34m'new values have %d elements'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3074\u001b[0;31m (old_len, new_len))\n\u001b[0m\u001b[1;32m 3075\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3076\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mValueError\u001b[0m: Length mismatch: Expected axis has 10 elements, new values have 9 elements"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"refs2.columns = [\"label\" + str(col) for col in refs2.columns.tolist() if col != 'image']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 157,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"refs2.rename(columns=lambda x: \"label\" + str(x) if x != 'image' else 'image', inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 163,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"RangeIndex(start=0, stop=320, step=1)"
|
||||
]
|
||||
},
|
||||
"execution_count": 163,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"refs2.index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 164,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"label\n",
|
||||
"label1 1\n",
|
||||
"label2 0\n",
|
||||
"Name: 0, dtype: object\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for i, row in refs2.iterrows():\n",
|
||||
" print(row[{'label1', 'label2'}])\n",
|
||||
" break"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 126,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"320"
|
||||
]
|
||||
},
|
||||
"execution_count": 126,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"refs2.index.size"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 127,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(676, 2)"
|
||||
]
|
||||
},
|
||||
"execution_count": 127,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"refs.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 131,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<bound method NDFrame.describe of label image 1 2 3 4 5 6 7 8 9\n",
|
||||
"0 20170416-012001.jpg 1 0 1 0 0 0 0 0 0\n",
|
||||
"1 20170416-013001.jpg 1 0 0 0 0 0 0 0 0\n",
|
||||
"2 20170416-014001.jpg 0 1 0 0 0 0 0 0 1\n",
|
||||
"3 20170416-015001.jpg 1 0 1 0 0 1 0 0 1\n",
|
||||
"4 20170416-025001.jpg 1 0 0 0 0 0 0 1 0\n",
|
||||
"5 20170416-030001.jpg 1 0 1 0 0 1 0 0 1\n",
|
||||
"6 20170416-032001.jpg 1 0 0 0 0 0 0 0 0\n",
|
||||
"7 20170416-044001.jpg 1 0 0 0 0 0 0 1 0\n",
|
||||
"8 20170416-045001.jpg 0 1 0 1 0 0 0 0 0\n",
|
||||
"9 20170416-050001.jpg 1 0 1 0 0 1 0 0 1\n",
|
||||
"10 20170416-051002.jpg 1 0 1 0 0 0 1 1 0\n",
|
||||
"11 20170416-062001.jpg 1 0 1 0 0 1 0 0 1\n",
|
||||
"12 20170416-063001.jpg 1 0 0 0 0 0 0 1 0\n",
|
||||
"13 20170416-073001.jpg 1 0 1 0 1 0 0 0 1\n",
|
||||
"14 20170416-080001.jpg 0 1 0 1 0 0 0 0 0\n",
|
||||
"15 20170416-081001.jpg 1 0 1 0 0 1 0 0 1\n",
|
||||
"16 20170416-090001.jpg 1 0 0 0 0 0 1 1 0\n",
|
||||
"17 20170416-094002.jpg 1 0 0 0 0 0 0 0 0\n",
|
||||
"18 20170416-104001.jpg 1 0 1 0 0 1 0 0 1\n",
|
||||
"19 20170416-105002.jpg 1 0 1 0 0 0 0 0 0\n",
|
||||
"20 20170416-110001.jpg 1 0 0 1 0 0 0 1 0\n",
|
||||
"21 20170416-111002.jpg 1 0 1 0 0 1 0 0 1\n",
|
||||
"22 20170416-120001.jpg 0 0 1 0 1 0 0 0 1\n",
|
||||
"23 20170416-121001.jpg 1 0 0 0 0 0 1 0 0\n",
|
||||
"24 20170416-123001.jpg 1 0 1 1 0 1 0 0 0\n",
|
||||
"25 20170416-134001.jpg 1 0 1 0 1 0 0 0 0\n",
|
||||
"26 20170416-151001.jpg 0 0 1 0 1 0 0 0 0\n",
|
||||
"27 20170416-152001.jpg 1 0 0 1 0 0 0 1 0\n",
|
||||
"28 20170416-153001.jpg 0 1 0 0 0 0 0 0 0\n",
|
||||
"29 20170416-154002.jpg 1 0 1 0 0 1 0 0 0\n",
|
||||
".. ... .. .. .. .. .. .. .. .. ..\n",
|
||||
"290 20180406-232001.jpg 1 0 1 0 0 1 0 0 0\n",
|
||||
"291 20180408-060001.jpg 1 0 1 0 0 1 0 0 0\n",
|
||||
"292 20180409-222001.jpg 1 0 1 0 0 0 0 0 0\n",
|
||||
"293 20180410-164001.jpg 1 0 0 0 0 0 0 1 0\n",
|
||||
"294 20180411-174001.jpg 1 0 1 1 0 0 0 0 0\n",
|
||||
"295 20180412-170001.jpg 1 0 0 0 0 0 0 0 0\n",
|
||||
"296 20180415-002001.jpg 1 0 1 0 0 0 0 1 0\n",
|
||||
"297 20180415-022001.jpg 1 0 0 0 1 0 0 0 1\n",
|
||||
"298 20180415-034001.jpg 1 0 0 0 0 0 0 0 0\n",
|
||||
"299 20180415-204001.jpg 1 0 0 1 0 0 0 0 0\n",
|
||||
"300 20180415-232001.jpg 0 1 1 0 0 0 0 0 1\n",
|
||||
"301 20180416-024001.jpg 1 0 1 0 0 0 0 0 0\n",
|
||||
"302 20180417-000001.jpg 1 0 1 0 0 0 0 0 0\n",
|
||||
"303 20180418-060001.jpg 1 0 0 0 0 0 0 0 0\n",
|
||||
"304 20180418-222001.jpg 1 0 1 0 0 0 0 0 0\n",
|
||||
"305 20180419-032001.jpg 1 0 1 0 0 0 0 1 0\n",
|
||||
"306 20180419-154001.jpg 0 1 1 0 0 0 0 0 1\n",
|
||||
"307 20180419-200001.jpg 0 1 1 0 0 0 0 0 0\n",
|
||||
"308 20180420-204001.jpg 0 0 1 0 1 0 0 0 0\n",
|
||||
"309 20180424-010001.jpg 1 0 0 0 0 0 0 0 0\n",
|
||||
"310 20180424-052001.jpg 1 0 1 1 0 0 0 0 0\n",
|
||||
"311 20180425-122001.jpg 0 1 1 1 0 0 0 1 0\n",
|
||||
"312 20180429-042001.jpg 0 1 1 0 0 0 0 0 1\n",
|
||||
"313 20180429-070001.jpg 0 0 0 0 1 0 1 0 0\n",
|
||||
"314 20180501-120001.jpg 0 1 1 1 0 0 0 0 0\n",
|
||||
"315 20180506-172001.jpg 1 0 1 0 0 1 0 0 0\n",
|
||||
"316 20180506-232001.jpg 1 0 0 0 0 0 0 0 0\n",
|
||||
"317 20180510-184001.jpg 1 0 1 0 0 0 0 0 0\n",
|
||||
"318 20180511-022001.jpg 0 1 0 0 0 0 0 0 0\n",
|
||||
"319 20180511-062001.jpg 1 0 0 1 0 0 0 0 0\n",
|
||||
"\n",
|
||||
"[320 rows x 10 columns]>"
|
||||
]
|
||||
},
|
||||
"execution_count": 131,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"refs2."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue