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Use make_column_selector where appropriate. #92

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merged 1 commit into from Nov 18, 2020
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Use make_column_selector where appropriate. #92

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7 changes: 4 additions & 3 deletions notebooks/04_parameter_tuning.ipynb
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Expand Up @@ -90,11 +90,12 @@
"outputs": [],
"source": [
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.compose import make_column_selector as selector\n",
"\n",
"from sklearn.preprocessing import OrdinalEncoder\n",
"\n",
"categorical_columns = [\n",
" 'workclass', 'education', 'marital-status', 'occupation',\n",
" 'relationship', 'race', 'native-country', 'sex']\n",
"categorical_columns_selector = selector(dtype_include=object)\n",
"categorical_columns = categorical_columns_selector(data)\n",
"\n",
"categories = [\n",
" data[column].unique() for column in data[categorical_columns]]\n",
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7 changes: 4 additions & 3 deletions notebooks/04_parameter_tuning_search.ipynb
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Expand Up @@ -86,11 +86,12 @@
"outputs": [],
"source": [
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.compose import make_column_selector as selector\n",
"\n",
"from sklearn.preprocessing import OrdinalEncoder\n",
"\n",
"categorical_columns = [\n",
" 'workclass', 'education', 'marital-status', 'occupation',\n",
" 'relationship', 'race', 'native-country', 'sex']\n",
"categorical_columns_selector = selector(dtype_include=object)\n",
"categorical_columns = categorical_columns_selector(data)\n",
"\n",
"categories = [\n",
" data[column].unique() for column in data[categorical_columns]]\n",
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11 changes: 6 additions & 5 deletions notebooks/04_parameter_tuning_sol_02.ipynb
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Expand Up @@ -61,15 +61,16 @@
"metadata": {},
"outputs": [],
"source": [
"categorical_columns = [\n",
" 'workclass', 'education', 'marital-status', 'occupation',\n",
" 'relationship', 'race', 'native-country', 'sex']\n",
"from sklearn.compose import make_column_selector as selector\n",
"\n",
"categorical_columns_selector = selector(dtype_include=object)\n",
"categorical_columns = categorical_columns_selector(data)\n",
"\n",
"categories = [data[column].unique()\n",
" for column in data[categorical_columns]]\n",
"\n",
"numerical_columns = [\n",
" 'age', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
"numerical_columns_selector = selector(dtype_exclude=object)\n",
"numerical_columns = numerical_columns_selector(data)\n",
"\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.preprocessing import StandardScaler\n",
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7 changes: 4 additions & 3 deletions python_scripts/04_parameter_tuning.py
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Expand Up @@ -57,11 +57,12 @@

# %%
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_selector as selector

from sklearn.preprocessing import OrdinalEncoder

categorical_columns = [
'workclass', 'education', 'marital-status', 'occupation',
'relationship', 'race', 'native-country', 'sex']
categorical_columns_selector = selector(dtype_include=object)
categorical_columns = categorical_columns_selector(data)

categories = [
data[column].unique() for column in data[categorical_columns]]
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7 changes: 4 additions & 3 deletions python_scripts/04_parameter_tuning_search.py
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Expand Up @@ -53,11 +53,12 @@

# %%
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_selector as selector

from sklearn.preprocessing import OrdinalEncoder

categorical_columns = [
'workclass', 'education', 'marital-status', 'occupation',
'relationship', 'race', 'native-country', 'sex']
categorical_columns_selector = selector(dtype_include=object)
categorical_columns = categorical_columns_selector(data)
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Maybe we can split using a new cell here just to show the output of using the selector?

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I guess we do it already in some notebooks previously e.g. here:
https://inria.github.io/scikit-learn-mooc/python_scripts/03_categorical_pipeline.html#working-with-categorical-variables
image

I am wondering whether it is better to do in all notebooks or only in one of the beginning at the beginning.


categories = [
data[column].unique() for column in data[categorical_columns]]
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11 changes: 6 additions & 5 deletions python_scripts/04_parameter_tuning_sol_02.py
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Expand Up @@ -47,15 +47,16 @@
# Start by defining the columns and the preprocessing pipelines to be applied
# on each columns.
# %%
categorical_columns = [
'workclass', 'education', 'marital-status', 'occupation',
'relationship', 'race', 'native-country', 'sex']
from sklearn.compose import make_column_selector as selector

categorical_columns_selector = selector(dtype_include=object)
categorical_columns = categorical_columns_selector(data)

categories = [data[column].unique()
for column in data[categorical_columns]]

numerical_columns = [
'age', 'capital-gain', 'capital-loss', 'hours-per-week']
numerical_columns_selector = selector(dtype_exclude=object)
numerical_columns = numerical_columns_selector(data)

from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
Expand Down