mirror of
https://github.com/ArthurDanjou/handson-ml3.git
synced 2026-01-14 12:14:36 +01:00
Update all notebooks assuming we are all in the future now: sklearn 0.20+, python 3.5+, TF 2.0 preview
This commit is contained in:
Aurélien Geron
@@ -64,6 +64,23 @@
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"warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
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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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"This notebook assumes you have installed Scikit-Learn ≥0.20."
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\""
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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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@@ -74,7 +91,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -115,7 +132,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -127,7 +144,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -139,7 +156,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -148,7 +165,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -158,7 +175,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -181,7 +198,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -193,7 +210,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -202,7 +219,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -220,7 +237,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -236,7 +253,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -252,7 +269,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -268,7 +285,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -284,7 +301,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -300,7 +317,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -316,7 +333,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -332,7 +349,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -355,7 +372,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -378,7 +395,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -394,7 +411,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -417,7 +434,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -445,7 +462,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -469,7 +486,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -498,9 +515,9 @@
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"ax.plot(X3D_inv[:, 0], X3D_inv[:, 1], X3D_inv[:, 2], \"k+\")\n",
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"ax.plot(X3D_inv[:, 0], X3D_inv[:, 1], X3D_inv[:, 2], \"k.\")\n",
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"ax.plot(X3D_above[:, 0], X3D_above[:, 1], X3D_above[:, 2], \"bo\")\n",
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"ax.set_xlabel(\"$x_1$\", fontsize=18)\n",
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"ax.set_ylabel(\"$x_2$\", fontsize=18)\n",
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"ax.set_zlabel(\"$x_3$\", fontsize=18)\n",
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"ax.set_xlabel(\"$x_1$\", fontsize=18, labelpad=10)\n",
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"ax.set_ylabel(\"$x_2$\", fontsize=18, labelpad=10)\n",
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"ax.set_zlabel(\"$x_3$\", fontsize=18, labelpad=10)\n",
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"ax.set_xlim(axes[0:2])\n",
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"ax.set_ylim(axes[2:4])\n",
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"ax.set_zlim(axes[4:6])\n",
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@@ -519,7 +536,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -548,7 +565,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -558,7 +575,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -582,7 +599,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -607,7 +624,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -695,7 +712,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -768,23 +785,19 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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"from six.moves import urllib\n",
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"try:\n",
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" from sklearn.datasets import fetch_openml\n",
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" mnist = fetch_openml('mnist_784', version=1)\n",
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" mnist.target = mnist.target.astype(np.int64)\n",
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"except ImportError:\n",
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" from sklearn.datasets import fetch_mldata\n",
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" mnist = fetch_mldata('MNIST original')"
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"from sklearn.datasets import fetch_openml\n",
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"\n",
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"mnist = fetch_openml('mnist_784', version=1)\n",
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"mnist.target = mnist.target.astype(np.uint8)"
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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": 32,
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"execution_count": 33,
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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": 33,
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"execution_count": 34,
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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": 34,
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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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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(6,4))\n",
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"plt.plot(cumsum, linewidth=3)\n",
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"plt.axis([0, 400, 0, 1])\n",
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"plt.xlabel(\"Dimensions\")\n",
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"plt.ylabel(\"Explained Variance\")\n",
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"plt.plot([d, d], [0, 0.95], \"k:\")\n",
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"plt.plot([0, d], [0.95, 0.95], \"k:\")\n",
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"plt.plot(d, 0.95, \"ko\")\n",
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"plt.annotate(\"Elbow\", xy=(65, 0.85), xytext=(70, 0.7),\n",
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" arrowprops=dict(arrowstyle=\"->\"), fontsize=16)\n",
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"plt.grid(True)\n",
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"save_fig(\"explained_variance_plot\")\n",
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"plt.show()"
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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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"outputs": [],
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"source": [
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},
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 38,
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"outputs": [],
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"source": [
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},
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"execution_count": 38,
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"execution_count": 39,
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"outputs": [],
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"source": [
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"execution_count": 39,
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"execution_count": 40,
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"source": [
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},
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"execution_count": 40,
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"execution_count": 41,
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"execution_count": 41,
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"source": [
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"cell_type": "code",
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"execution_count": 53,
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"execution_count": 54,
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"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1146,7 +1180,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"execution_count": 55,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
@@ -1185,7 +1219,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1194,7 +1228,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1206,7 +1240,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1240,7 +1274,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"execution_count": 59,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1264,7 +1298,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1280,7 +1314,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1290,7 +1324,7 @@
|
||||
"\n",
|
||||
"clf = Pipeline([\n",
|
||||
" (\"kpca\", KernelPCA(n_components=2)),\n",
|
||||
" (\"log_reg\", LogisticRegression(solver=\"liblinear\"))\n",
|
||||
" (\"log_reg\", LogisticRegression(solver=\"lbfgs\"))\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"param_grid = [{\n",
|
||||
@@ -1304,7 +1338,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1313,7 +1347,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1325,7 +1359,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1343,7 +1377,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1352,7 +1386,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"execution_count": 66,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1364,7 +1398,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1388,7 +1422,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"execution_count": 68,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1400,7 +1434,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 68,
|
||||
"execution_count": 69,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1412,7 +1446,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1424,7 +1458,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"execution_count": 71,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1439,7 +1473,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 71,
|
||||
"execution_count": 72,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -1535,7 +1569,7 @@
|
||||
"source": [
|
||||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||||
"\n",
|
||||
"rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)"
|
||||
"rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1604,7 +1638,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rnd_clf2 = RandomForestClassifier(n_estimators=10, random_state=42)\n",
|
||||
"rnd_clf2 = RandomForestClassifier(n_estimators=100, random_state=42)\n",
|
||||
"t0 = time.time()\n",
|
||||
"rnd_clf2.fit(X_train_reduced, y_train)\n",
|
||||
"t1 = time.time()"
|
||||
@@ -2232,7 +2266,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 - tf2",
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user