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Woodwork update #24

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Feb 9, 2021
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Woodwork update #24

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34 changes: 21 additions & 13 deletions predict-credit-churn/CreditChurn.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -94,18 +94,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"First let's use some built in functions from EvalML to convert the data to a woodwork data structure and then cast its dtypes to something we'd rather work with. Then we're going to take a look at some of the unqiue, non-numeric values in the features. Sure enough, `Education_Level`, `Marital_Status`, and `Income_Category` have `Unknown` as a value. This is something we'll have to remember before we get to the model training, since `Unknown` isn't an acceptable value for any of the features."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from evalml.utils.gen_utils import _convert_to_woodwork_structure, _convert_woodwork_types_wrapper\n",
"data = _convert_to_woodwork_structure(data)\n",
"data = _convert_woodwork_types_wrapper(data.to_dataframe())"
"We're going to take a look at some of the unqiue, non-numeric values in the features. Sure enough, `Education_Level`, `Marital_Status`, and `Income_Category` have `Unknown` as a value. This is something we'll have to remember before we get to the model training, since `Unknown` isn't an acceptable value for any of the features."
]
},
{
Expand Down Expand Up @@ -183,7 +172,7 @@
"outputs": [],
"source": [
"X = data.copy()\n",
"data = data.drop(['Credit_Limit'], axis=1)\n",
"X = X.drop(['Credit_Limit'], axis=1)\n",
"y = X.pop('Attrition_Flag')\n",
"\n",
"X['Income_Category'] = X['Income_Category'].replace({'Less than $40K':0,\n",
Expand Down Expand Up @@ -230,6 +219,25 @@
"X = preprocessing(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using `infer_feature_types`, we can convert our dataset into a [Woodwork](https://github.com/alteryx/woodwork) data structure, and even [specify what types](https://evalml.alteryx.com/en/stable/user_guide/automl.html) certain features should be. For example, we want to cast `Income_Category` as a categorical type, rather than natural language which is what it was inferred as."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from evalml.utils.gen_utils import infer_feature_types\n",
"X = infer_feature_types(X, feature_types={'Income_Category': 'categorical',\n",
" 'Education_Level': 'categorical'})\n",
"X"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down