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Merge pull request #6 from vi3itor/ch2-errata 

Fix a few typos and deprecated np.object reference
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Aurélien Geron 2022-05-09 22:47:48 +12:00 committed by GitHub
parent f92a7497f8 338c0907a2
commit ec67af216b
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1 changed files with 10 additions and 10 deletions
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02_end_to_end_machine_learning_project.ipynb
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@ -11,7 +11,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"*This notebook contains all the sample code and solutions to the exercices in chapter 2.*" "*This notebook contains all the sample code and solutions to the exercises in chapter 2.*"
] ]
}, },
{ {
@ -3747,7 +3747,7 @@
"\n", "\n",
"preprocessing = make_column_transformer(\n", "preprocessing = make_column_transformer(\n",
" (num_pipeline, make_column_selector(dtype_include=np.number)),\n", " (num_pipeline, make_column_selector(dtype_include=np.number)),\n",
" (cat_pipeline, make_column_selector(dtype_include=np.object)),\n", " (cat_pipeline, make_column_selector(dtype_include=object)),\n",
")" ")"
] ]
}, },
@ -3918,7 +3918,7 @@
" (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\",\n", " (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\",\n",
" \"population\", \"households\", \"median_income\"]),\n", " \"population\", \"households\", \"median_income\"]),\n",
" (\"geo\", cluster_simil, [\"latitude\", \"longitude\"]),\n", " (\"geo\", cluster_simil, [\"latitude\", \"longitude\"]),\n",
" (\"cat\", cat_pipeline, make_column_selector(dtype_include=np.object)),\n", " (\"cat\", cat_pipeline, make_column_selector(dtype_include=object)),\n",
" ],\n", " ],\n",
" remainder=default_num_pipeline) # one column remaining: housing_median_age" " remainder=default_num_pipeline) # one column remaining: housing_median_age"
] ]
@ -4381,7 +4381,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"**Warning:** the following cell make take a few minutes to run:" "**Warning:** the following cell may take a few minutes to run:"
] ]
}, },
{ {
@ -4660,7 +4660,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"**Warning:** the following cell make take a few minutes to run:" "**Warning:** the following cell may take a few minutes to run:"
] ]
}, },
{ {
@ -5214,7 +5214,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Alternatively, we could use a z-scores rather than t-scores—since the test set is not too small, it won't make a big difference:" "Alternatively, we could use a z-score rather than a t-score. Since the test set is not too small, it won't make a big difference:"
] ]
}, },
{ {
@ -5234,7 +5234,7 @@
} }
], ],
"source": [ "source": [
"# extra code – computes a confidence interval again using z-score\n", "# extra code – computes a confidence interval again using a z-score\n",
"zscore = stats.norm.ppf((1 + confidence) / 2)\n", "zscore = stats.norm.ppf((1 + confidence) / 2)\n",
"zmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)\n", "zmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
"np.sqrt(mean - zmargin), np.sqrt(mean + zmargin)" "np.sqrt(mean - zmargin), np.sqrt(mean + zmargin)"
@ -5746,7 +5746,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Rather than restrict ourselves to k-Nearest Neighbors regressors, let's create a transform that accepts any regressor. For this, we can extend the `MetaEstimatorMixin` and have a required `estimator` argument in the constructor. The `fit()` method must work on a clone of this estimator, and it must also save `feature_names_in_`. The `MetaEstimatorMixin` will ensure that `estimator` is listed as a required parameters, and it will update `get_params()` and `set_params()` to make the estimator's hyperparameters available for tuning. Lastly, we create a `get_feature_names_out()` method: the output column name is the " "Rather than restrict ourselves to k-Nearest Neighbors regressors, let's create a transformer that accepts any regressor. For this, we can extend the `MetaEstimatorMixin` and have a required `estimator` argument in the constructor. The `fit()` method must work on a clone of this estimator, and it must also save `feature_names_in_`. The `MetaEstimatorMixin` will ensure that `estimator` is listed as a required parameters, and it will update `get_params()` and `set_params()` to make the estimator's hyperparameters available for tuning. Lastly, we create a `get_feature_names_out()` method: the output column name is the ..."
] ]
}, },
{ {
@ -6070,7 +6070,7 @@
" self.scale_ = X.std(axis=0)\n", " self.scale_ = X.std(axis=0)\n",
" self.n_features_in_ = X.shape[1] # every estimator stores this in fit()\n", " self.n_features_in_ = X.shape[1] # every estimator stores this in fit()\n",
" if hasattr(X_orig, \"columns\"):\n", " if hasattr(X_orig, \"columns\"):\n",
" self.feature_names_in_ = np.array(X_orig.columns, dtype=np.object)\n", " self.feature_names_in_ = np.array(X_orig.columns, dtype=object)\n",
" return self # always return self!\n", " return self # always return self!\n",
"\n", "\n",
" def transform(self, X):\n", " def transform(self, X):\n",
@ -6133,7 +6133,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Now let's ensure we the transformation works as expected:" "Now let's ensure the transformation works as expected:"
] ]
}, },
{ {