schuetoliver
/
handson-ml
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix people_per_house feature name, and fix solution to last exercise

main
Aurélien Geron 2021-11-15 20:56:11 +13:00
parent c658c2b07c
commit ef27260251
1 changed files with 8 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
02_end_to_end_machine_learning_project.ipynb
View File

@ -1803,7 +1803,7 @@
" [\"total_bedrooms\", \"total_rooms\"]),\n",
" (\"rooms_per_house\", ratio_pipeline(\"rooms_per_house\"),\n",
" [\"total_rooms\", \"households\"]),\n",
" (\"people_per_house\", ratio_pipeline(\"bedrooms_ratio\"),\n",
" (\"people_per_house\", ratio_pipeline(\"people_per_house\"),\n",
" [\"population\", \"households\"]),\n",
" (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\",\n",
" \"population\", \"households\", \"median_income\"]),\n",
@ -2869,6 +2869,8 @@
"metadata": {},
"outputs": [],
"source": [
"from sklearn.utils.estimator_checks import check_estimator\n",
"\n",
"check_estimator(FeatureFromRegressor(KNeighborsRegressor()))"
]
},
@ -3046,12 +3048,13 @@
" self.with_mean = with_mean\n",
"\n",
" def fit(self, X, y=None): # y is required even though we don't use it\n",
" X_orig = X\n",
" X = check_array(X) # checks that X is an array with finite float values\n",
" self.mean_ = X.mean(axis=0)\n",
" self.scale_ = X.std(axis=0)\n",
" self.n_features_in_ = X.shape[1] # every estimator stores this in fit()\n",
" if hasattr(X, \"columns\"):\n",
" self.feature_names_in_ = np.array(X.columns, np.object)\n",
" if hasattr(X_orig, \"columns\"):\n",
" self.feature_names_in_ = np.array(X_orig.columns, dtype=np.object)\n",
" return self # always return self!\n",
"\n",
" def transform(self, X):\n",
@ -3203,8 +3206,8 @@
"scaler = StandardScalerClone()\n",
"X_scaled = scaler.fit_transform(df)\n",
"\n",
"assert np.all(ss.feature_names_in_ == [\"a\", \"b\"])\n",
"assert np.all(ss.get_feature_names_out() == [\"a\", \"b\"])"
"assert np.all(scaler.feature_names_in_ == [\"a\", \"b\"])\n",
"assert np.all(scaler.get_feature_names_out() == [\"a\", \"b\"])"
]
},
{