Make notebook for ch2 output the same result every time
Aurélien Geron
parent
9910d31ec3
commit
bd6c167e09
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@ -49,11 +49,10 @@
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import numpy.random as rnd\n",
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"import os\n",
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"\n",
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"# to make this notebook's output stable across runs\n",
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"rnd.seed(42)\n",
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"np.random.seed(42)\n",
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"\n",
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"# To plot pretty figures\n",
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"%matplotlib inline\n",
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@ -1154,9 +1153,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 69,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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@ -1178,7 +1179,7 @@
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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": 70,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1196,7 +1197,7 @@
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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": 71,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1210,7 +1211,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 71,
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"execution_count": 72,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1233,7 +1234,7 @@
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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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"execution_count": 73,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1249,7 +1250,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 73,
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"execution_count": 74,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1262,20 +1263,7 @@
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"some_labels = housing_labels.iloc[:5]\n",
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"some_data_prepared = full_pipeline.transform(some_data)\n",
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"\n",
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"print(\"Predictions:\\t\", lin_reg.predict(some_data_prepared))"
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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": 74,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"print(\"Labels:\\t\\t\", list(some_labels))"
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"print(\"Predictions:\", lin_reg.predict(some_data_prepared))"
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]
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},
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{
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@ -1288,7 +1276,7 @@
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},
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"outputs": [],
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"source": [
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"some_data_prepared"
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"print(\"Labels:\", list(some_labels))"
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]
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},
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{
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@ -1300,6 +1288,19 @@
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"editable": true
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},
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"outputs": [],
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"source": [
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"some_data_prepared"
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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": 77,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.metrics import mean_squared_error\n",
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"\n",
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@ -1311,7 +1312,7 @@
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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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"execution_count": 78,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1327,7 +1328,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 78,
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"execution_count": 79,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1337,13 +1338,13 @@
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"source": [
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"from sklearn.tree import DecisionTreeRegressor\n",
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"\n",
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"tree_reg = DecisionTreeRegressor()\n",
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"tree_reg = DecisionTreeRegressor(random_state=42)\n",
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"tree_reg.fit(housing_prepared, housing_labels)"
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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": 79,
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"execution_count": 80,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1369,7 +1370,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 80,
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"execution_count": 81,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1386,7 +1387,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 81,
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"execution_count": 82,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1404,7 +1405,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 82,
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"execution_count": 83,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1420,7 +1421,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 83,
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"execution_count": 84,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1430,13 +1431,13 @@
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"source": [
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"from sklearn.ensemble import RandomForestRegressor\n",
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"\n",
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"forest_reg = RandomForestRegressor()\n",
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"forest_reg = RandomForestRegressor(random_state=42)\n",
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"forest_reg.fit(housing_prepared, housing_labels)"
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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": 84,
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"execution_count": 85,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1452,7 +1453,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 85,
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"execution_count": 86,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1470,7 +1471,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 86,
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"execution_count": 87,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1484,7 +1485,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 87,
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"execution_count": 88,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1504,7 +1505,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 88,
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"execution_count": 89,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1519,25 +1520,12 @@
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" {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
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" ]\n",
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"\n",
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"forest_reg = RandomForestRegressor()\n",
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"forest_reg = RandomForestRegressor(random_state=42)\n",
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"grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
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" scoring='neg_mean_squared_error')\n",
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"grid_search.fit(housing_prepared, housing_labels)"
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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": 89,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"grid_search.best_params_"
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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": 90,
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@ -1548,7 +1536,7 @@
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},
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"outputs": [],
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"source": [
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"grid_search.best_estimator_"
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"grid_search.best_params_"
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]
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},
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{
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@ -1560,6 +1548,19 @@
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"editable": true
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},
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"outputs": [],
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"source": [
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"grid_search.best_estimator_"
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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": 92,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"cvres = grid_search.cv_results_\n",
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"for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
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@ -1568,7 +1569,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 92,
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"execution_count": 93,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1581,7 +1582,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 93,
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"execution_count": 94,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1597,15 +1598,15 @@
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" 'max_features': randint(low=1, high=8),\n",
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" }\n",
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"\n",
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"forest_reg = RandomForestRegressor()\n",
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"forest_reg = RandomForestRegressor(random_state=42)\n",
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"rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
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" n_iter=10, cv=5, scoring='neg_mean_squared_error')\n",
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" n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
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"rnd_search.fit(housing_prepared, housing_labels)"
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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": 94,
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"execution_count": 95,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1620,7 +1621,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 95,
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"execution_count": 96,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1634,7 +1635,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 96,
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"execution_count": 97,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1650,7 +1651,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 97,
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"execution_count": 98,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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@ -1672,7 +1673,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 98,
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"execution_count": 99,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1708,7 +1709,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 99,
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"execution_count": 100,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1745,7 +1746,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 100,
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"execution_count": 101,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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@ -1758,7 +1759,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 101,
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"execution_count": 102,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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@ -1784,7 +1785,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 102,
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"execution_count": 103,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1793,8 +1794,8 @@
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"outputs": [],
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"source": [
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"from scipy.stats import geom, expon\n",
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"geom_distrib=geom(0.5).rvs(10000)\n",
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"expon_distrib=expon(scale=1).rvs(10000)\n",
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"geom_distrib=geom(0.5).rvs(10000, random_state=42)\n",
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"expon_distrib=expon(scale=1).rvs(10000, random_state=42)\n",
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"plt.hist(geom_distrib, bins=50)\n",
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"plt.show()\n",
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"plt.hist(expon_distrib, bins=50)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 103,
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"execution_count": 104,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1851,7 +1852,7 @@
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" ]\n",
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"\n",
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"svm_reg = SVR()\n",
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"grid_search = GridSearchCV(svm_reg,param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2, n_jobs=4)\n",
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"grid_search = GridSearchCV(svm_reg, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2, n_jobs=4)\n",
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"grid_search.fit(housing_prepared, housing_labels)"
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]
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},
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@ -1867,7 +1868,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 104,
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"execution_count": 105,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1892,7 +1893,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 105,
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"execution_count": 106,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1935,7 +1936,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 106,
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"execution_count": 107,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1958,7 +1959,8 @@
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"\n",
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"svm_reg = SVR()\n",
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"rnd_search = RandomizedSearchCV(svm_reg, param_distributions=param_distribs,\n",
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" n_iter=50, cv=5, scoring='neg_mean_squared_error', verbose=2, n_jobs=4)\n",
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" n_iter=50, cv=5, scoring='neg_mean_squared_error',\n",
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" verbose=2, n_jobs=4, random_state=42)\n",
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"rnd_search.fit(housing_prepared, housing_labels)"
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]
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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": 107,
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"execution_count": 108,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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@ -1999,7 +2001,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 108,
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"execution_count": 109,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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|
|
@ -2032,7 +2034,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 109,
|
||||
"execution_count": 110,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
|
|
@ -2041,7 +2043,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"expon_distrib = expon(scale=1.)\n",
|
||||
"samples = expon_distrib.rvs(10000)\n",
|
||||
"samples = expon_distrib.rvs(10000, random_state=42)\n",
|
||||
"plt.figure(figsize=(10, 4))\n",
|
||||
"plt.subplot(121)\n",
|
||||
"plt.title(\"Exponential distribution (scale=1.0)\")\n",
|
||||
|
|
@ -2064,7 +2066,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 110,
|
||||
"execution_count": 111,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
|
|
@ -2073,7 +2075,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"reciprocal_distrib = reciprocal(20, 200000)\n",
|
||||
"samples = reciprocal_distrib.rvs(10000)\n",
|
||||
"samples = reciprocal_distrib.rvs(10000, random_state=42)\n",
|
||||
"plt.figure(figsize=(10, 4))\n",
|
||||
"plt.subplot(121)\n",
|
||||
"plt.title(\"Reciprocal distribution (scale=1.0)\")\n",
|
||||
|
|
@ -2116,7 +2118,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 111,
|
||||
"execution_count": 112,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
|
|
@ -2162,7 +2164,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 112,
|
||||
"execution_count": 113,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
|
|
@ -2185,7 +2187,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 113,
|
||||
"execution_count": 114,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
|
|
@ -2199,7 +2201,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 114,
|
||||
"execution_count": 115,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
|
|
@ -2222,7 +2224,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 115,
|
||||
"execution_count": 116,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
|
|
@ -2245,7 +2247,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 116,
|
||||
"execution_count": 117,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
|
|
@ -2261,7 +2263,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 117,
|
||||
"execution_count": 118,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
|
|
@ -2284,7 +2286,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 118,
|
||||
"execution_count": 119,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
|
|
@ -2307,7 +2309,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 119,
|
||||
"execution_count": 120,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
|
|
|
|||
Loading…
Reference in New Issue