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Adding missing figure in chapter 02

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Aurélien Geron 2017-06-08 14:23:33 +02:00
parent 74794da1de
commit 8935c61570
1 changed files with 148 additions and 118 deletions
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02_end_to_end_machine_learning_project.ipynb
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@ -14,6 +14,13 @@
"*This notebook contains all the sample code and solutions to the exercices in chapter 2.*" "*This notebook contains all the sample code and solutions to the exercices in chapter 2.*"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: You may find little differences between the code outputs in the book and in these Jupyter notebooks: these slight differences are mostly due to the random nature of many training algorithms: although I have tried to make these notebooks' outputs as constant as possible, it is impossible to guarantee that they will produce the exact same output on every platform. Also, some data structures (such as dictionaries) do not preserve the item order. Finally, I fixed a few minor bugs (I added notes next to the concerned cells) which lead to slightly different results, without changing the ideas presented in the book."
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
@ -408,6 +415,17 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"housing[\"income_cat\"].hist()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"deletable": true, "deletable": true,
@ -425,7 +443,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 25,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"deletable": true, "deletable": true,
@ -438,7 +456,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 26,
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"deletable": true, "deletable": true,
@ -462,7 +480,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 27,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"deletable": true, "deletable": true,
@ -475,7 +493,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 28,
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@ -499,7 +517,7 @@
}, },
{ {
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"execution_count": 28, "execution_count": 29,
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@ -512,7 +530,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 30,
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"collapsed": false, "collapsed": false,
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@ -526,7 +544,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 31,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"deletable": true, "deletable": true,
@ -538,9 +556,16 @@
"save_fig(\"better_visualization_plot\")" "save_fig(\"better_visualization_plot\")"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The argument `sharex=False` fixes a display bug (the x-axis values and legend were not displayed). This is a temporary fix (see: https://github.com/pandas-dev/pandas/issues/10611). Thanks to Wilmer Arellano for pointing it out."
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 32,
"metadata": { "metadata": {
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"deletable": true, "deletable": true,
@ -551,16 +576,14 @@
"housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n", "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
" s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n", " s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
" c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n", " c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
" sharex=False) # sharex=False fixes a bug (temporary solution)\n", " sharex=False)\n",
" # See: https://github.com/pandas-dev/pandas/issues/10611\n",
" # Thanks to Wilmer Arellano for pointing it out.\n",
"plt.legend()\n", "plt.legend()\n",
"save_fig(\"housing_prices_scatterplot\")" "save_fig(\"housing_prices_scatterplot\")"
] ]
}, },
{ {
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"execution_count": 32, "execution_count": 33,
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"deletable": true, "deletable": true,
@ -592,7 +615,7 @@
}, },
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"metadata": { "metadata": {
"collapsed": true, "collapsed": true,
"deletable": true, "deletable": true,
@ -605,7 +628,7 @@
}, },
{ {
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"execution_count": 34, "execution_count": 35,
"metadata": { "metadata": {
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@ -618,7 +641,7 @@
}, },
{ {
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"execution_count": 35, "execution_count": 36,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"deletable": true, "deletable": true,
@ -634,7 +657,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 36, "execution_count": 37,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"deletable": true, "deletable": true,
@ -652,7 +675,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 37, "execution_count": 38,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true,
"deletable": true, "deletable": true,
@ -665,9 +688,16 @@
"housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"]" "housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"]"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: there was a bug in the previous cell, in the definition of the `rooms_per_household` attribute. This explains why the correlation value below differs slightly from the value in the book (unless you are reading the latest version)."
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 38, "execution_count": 39,
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"collapsed": false, "collapsed": false,
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@ -681,7 +711,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 39, "execution_count": 40,
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@ -697,7 +727,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 40, "execution_count": 41,
"metadata": { "metadata": {
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"deletable": true, "deletable": true,
@ -720,7 +750,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 41, "execution_count": 42,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true,
"deletable": true, "deletable": true,
@ -732,19 +762,6 @@
"housing_labels = strat_train_set[\"median_house_value\"].copy()" "housing_labels = strat_train_set[\"median_house_value\"].copy()"
] ]
}, },
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"housing.iloc[21:24]"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 43, "execution_count": 43,
@ -755,8 +772,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"housing_copy = housing.copy().iloc[21:24]\n", "housing.iloc[21:24]"
"housing_copy.dropna(subset=[\"total_bedrooms\"]) # option 1"
] ]
}, },
{ {
@ -770,7 +786,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"housing_copy = housing.copy().iloc[21:24]\n", "housing_copy = housing.copy().iloc[21:24]\n",
"housing_copy.drop(\"total_bedrooms\", axis=1) # option 2" "housing_copy.dropna(subset=[\"total_bedrooms\"]) # option 1"
] ]
}, },
{ {
@ -782,6 +798,20 @@
"editable": true "editable": true
}, },
"outputs": [], "outputs": [],
"source": [
"housing_copy = housing.copy().iloc[21:24]\n",
"housing_copy.drop(\"total_bedrooms\", axis=1) # option 2"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [ "source": [
"housing_copy = housing.copy().iloc[21:24]\n", "housing_copy = housing.copy().iloc[21:24]\n",
"median = housing_copy[\"total_bedrooms\"].median()\n", "median = housing_copy[\"total_bedrooms\"].median()\n",
@ -791,7 +821,7 @@
}, },
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@ -804,7 +834,7 @@
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@ -819,7 +849,7 @@
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@ -841,7 +871,7 @@
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@ -856,7 +886,7 @@
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@ -869,7 +899,7 @@
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@ -882,7 +912,7 @@
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@ -895,7 +925,7 @@
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@ -908,7 +938,7 @@
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@ -921,7 +951,7 @@
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@ -934,7 +964,7 @@
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@ -947,7 +977,7 @@
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@ -960,7 +990,7 @@
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@ -974,7 +1004,7 @@
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@ -992,7 +1022,7 @@
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@ -1005,7 +1035,7 @@
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@ -1022,7 +1052,7 @@
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@ -1035,7 +1065,7 @@
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@ -1052,7 +1082,7 @@
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@ -1085,7 +1115,7 @@
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@ -1099,7 +1129,7 @@
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@ -1121,7 +1151,7 @@
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@ -1134,7 +1164,7 @@
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@ -1155,7 +1185,7 @@
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@ -1181,7 +1211,7 @@
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@ -1199,7 +1229,7 @@
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@ -1213,7 +1243,7 @@
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@ -1236,7 +1266,7 @@
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@ -1252,7 +1282,7 @@
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@ -1270,7 +1300,7 @@
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@ -1283,7 +1313,7 @@
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@ -1296,7 +1326,7 @@
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@ -1314,7 +1344,7 @@
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@ -1346,7 +1376,7 @@
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@ -1372,7 +1402,7 @@
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@ -1389,7 +1419,7 @@
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@ -1407,7 +1437,7 @@
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@ -1423,7 +1453,7 @@
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@ -1439,7 +1469,7 @@
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@ -1455,7 +1485,7 @@
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@ -1473,7 +1503,7 @@
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@ -1487,7 +1517,7 @@
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@ -1507,7 +1537,7 @@
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@ -1530,7 +1560,7 @@
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@ -1543,7 +1573,7 @@
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@ -1556,7 +1586,7 @@
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@ -1571,7 +1601,7 @@
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@ -1584,7 +1614,7 @@
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@ -1608,7 +1638,7 @@
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{ {
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@ -1623,7 +1653,7 @@
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{ {
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@ -1637,7 +1667,7 @@
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{ {
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@ -1653,7 +1683,7 @@
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{ {
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@ -1675,7 +1705,7 @@
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{ {
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@ -1711,7 +1741,7 @@
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{ {
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@ -1748,7 +1778,7 @@
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{ {
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@ -1761,7 +1791,7 @@
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{ {
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@ -1787,7 +1817,7 @@
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{ {
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@ -1837,7 +1867,7 @@
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{ {
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@ -1870,7 +1900,7 @@
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{ {
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@ -1895,7 +1925,7 @@
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{ {
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@ -1938,7 +1968,7 @@
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{ {
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@ -1978,7 +2008,7 @@
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{ {
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@ -2003,7 +2033,7 @@
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{ {
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@ -2036,7 +2066,7 @@
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{ {
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@ -2068,7 +2098,7 @@
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{ {
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@ -2120,7 +2150,7 @@
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{ {
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@ -2166,7 +2196,7 @@
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{ {
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@ -2189,7 +2219,7 @@
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{ {
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@ -2203,7 +2233,7 @@
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{ {
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@ -2226,7 +2256,7 @@
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{ {
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@ -2249,7 +2279,7 @@
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{ {
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@ -2265,7 +2295,7 @@
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{ {
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@ -2288,7 +2318,7 @@
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{ {
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@ -2311,7 +2341,7 @@
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{ {
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@ -2354,7 +2384,7 @@
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{ {
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@ -2371,7 +2401,7 @@
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{ {
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@ -2394,7 +2424,7 @@
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{ {
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@ -2441,7 +2471,7 @@
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{ {
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@ -2461,7 +2491,7 @@
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{ {
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@ -2484,7 +2514,7 @@
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{ {
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