Add workaround for a bug introduced by Scikit-Learn 0.19.0, in chapter 03
Aurélien Geron
parent
d016b56672
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ca35dddc38
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@ -26,7 +26,9 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# To support both python 2 and python 3\n",
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@ -143,7 +145,9 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# EXTRA\n",
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@ -313,7 +317,9 @@
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import cross_val_predict\n",
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@ -463,9 +469,38 @@
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" method=\"decision_function\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Note: there is an [issue](https://github.com/scikit-learn/scikit-learn/issues/9589) introduced in Scikit-Learn 0.19.0 where the result of `cross_val_predict()` is incorrect in the binary classification case when using `method=\"decision_function\"`, as in the code above. The resulting array has an extra first dimension full of 0s. We need to add this small hack for now to work around this issue:"
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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": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_scores.shape"
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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": 36,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# hack to work around issue #9589 introduced in Scikit-Learn 0.19.0\n",
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"if y_scores.ndim == 2:\n",
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" y_scores = y_scores[:, 1]"
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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": 37,
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"metadata": {
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"collapsed": true
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},
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@ -478,7 +513,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -498,7 +533,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -507,8 +542,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"execution_count": 40,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"y_train_pred_90 = (y_scores > 70000)"
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@ -516,7 +553,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -525,7 +562,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 42,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -534,7 +571,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 43,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -559,8 +596,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {},
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"execution_count": 44,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from sklearn.metrics import roc_curve\n",
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@ -570,7 +609,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -589,7 +628,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"outputs": [],
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"source": [
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@ -600,7 +639,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"execution_count": 47,
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"metadata": {
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"collapsed": true
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},
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@ -614,7 +653,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"execution_count": 48,
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"metadata": {
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"collapsed": true
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},
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@ -626,7 +665,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": 49,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -640,7 +679,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"execution_count": 50,
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"metadata": {},
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"source": [
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@ -649,7 +688,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"execution_count": 51,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -659,7 +698,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"source": [
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@ -675,7 +714,7 @@
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{
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"cell_type": "code",
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{
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"cell_type": "code",
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{
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{
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"cell_type": "code",
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{
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"cell_type": "code",
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"execution_count": 60,
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"metadata": {},
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{
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"cell_type": "code",
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"execution_count": 61,
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"execution_count": 63,
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 62,
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 63,
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"metadata": {},
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"execution_count": 65,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def plot_confusion_matrix(matrix):\n",
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"execution_count": 66,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -819,8 +860,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"metadata": {},
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"execution_count": 67,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"row_sums = conf_mx.sum(axis=1, keepdims=True)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 66,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 67,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"execution_count": 70,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"execution_count": 71,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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"execution_count": 72,
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 71,
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"metadata": {},
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"execution_count": 73,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"noise = np.random.randint(0, 100, (len(X_train), 784))\n",
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},
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{
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"cell_type": "code",
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"execution_count": 72,
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"metadata": {},
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"source": [
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{
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"metadata": {},
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"execution_count": 76,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from sklearn.dummy import DummyClassifier\n",
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"metadata": {
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"scrolled": true
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},
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{
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"y_knn_pred = knn_clf.predict(X_test)"
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},
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{
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{
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"source": [
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},
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{
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"metadata": {},
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},
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{
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"cell_type": "code",
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"metadata": {},
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{
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"execution_count": 84,
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"metadata": {
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"collapsed": true
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},
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{
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{
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"execution_count": 84,
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"execution_count": 86,
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"metadata": {},
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"outputs": [],
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"source": [
|
||||
|
|
@ -1095,7 +1144,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 85,
|
||||
"execution_count": 87,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1144,7 +1193,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.3"
|
||||
"version": "3.5.2"
|
||||
},
|
||||
"nav_menu": {},
|
||||
"toc": {
|
||||
|
|
|
|||
Loading…
Reference in New Issue