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Use OrdinalEncoder and OneHotEncoder from Scikit-Learn 0.20 instead of CategoricalEncoder

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Aurélien Geron 2018-05-07 11:27:59 +02:00
parent 32efe073a5
commit 71c40c7aec
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02_end_to_end_machine_learning_project.ipynb
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@ -790,7 +790,7 @@
"metadata": {},
"outputs": [],
"source": [
"housing_cat = housing['ocean_proximity']\n",
"housing_cat = housing[['ocean_proximity']]\n",
"housing_cat.head(10)"
]
},
@ -798,7 +798,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use Pandas' `factorize()` method to convert this string categorical feature to an integer categorical feature, which will be easier for Machine Learning algorithms to handle:"
"**Warning**: earlier versions of the book used the `LabelEncoder` class or Pandas' `Series.factorize()` method instead of the `OrdinalEncoder` class (available since Scikit-Learn 0.20). It is preferable to use the `OrdinalEncoder` class, since it is designed for input features (instead of labels) and it plays well with pipelines, as we will see later in this notebook. Similarly, earlier version of the book used the `LabelBinarizer` class or the `CategoricalEncoder` class for one-hot encoding (which we will look at shortly), but since Scikit-Learn 0.20 it is preferable to use the `OneHotEncoder` class. If you are using an older version of Scikit-Learn, please consider upgrading (in case you want to stick to an old version of Scikit-Learn, the new `OrdinalEncoder` and `OneHotEncoder` classes are provided in the `future_encoders.py` file)."
]
},
{
@ -807,8 +807,10 @@
"metadata": {},
"outputs": [],
"source": [
"housing_cat_encoded, housing_categories = housing_cat.factorize()\n",
"housing_cat_encoded[:10]"
"try:\n",
" from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder\n",
"except ImportError:\n",
" from future_encoders import OrdinalEncoder, OneHotEncoder"
]
},
{
@ -817,21 +819,9 @@
"metadata": {},
"outputs": [],
"source": [
"housing_categories"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Warning**: earlier versions of the book used the `LabelEncoder` class instead of Pandas' `factorize()` method. This was incorrect: indeed, as its name suggests, the `LabelEncoder` class was designed for labels, not for input features. The code worked because we were handling a single categorical input feature, but it would break if you passed multiple categorical input features."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can convert each categorical value to a one-hot vector using a `OneHotEncoder`:"
"ordinal_encoder = OrdinalEncoder()\n",
"housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)\n",
"housing_cat_encoded[:10]"
]
},
{
@ -840,18 +830,14 @@
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"encoder = OneHotEncoder()\n",
"housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
"housing_cat_1hot"
"ordinal_encoder.categories_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `OneHotEncoder` returns a sparse array by default, but we can convert it to a dense array if needed:"
"We can convert each categorical value to a one-hot vector using a `OneHotEncoder`. Prior to Scikit-Learn 0.20, this class could only handle integer categorical inputs. Now it can also handle string categorical inputs:"
]
},
{
@ -860,14 +846,16 @@
"metadata": {},
"outputs": [],
"source": [
"housing_cat_1hot.toarray()"
"cat_encoder = OneHotEncoder()\n",
"housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
"housing_cat_1hot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Warning**: earlier versions of the book used the `LabelBinarizer` class at this point. Again, this was incorrect: just like the `LabelEncoder` class, the `LabelBinarizer` class was designed to preprocess labels, not input features. A better solution is to use Scikit-Learn's upcoming `CategoricalEncoder` class: it will soon be added to Scikit-Learn, and in the meantime you can use the code below (copied from [Pull Request #9151](https://github.com/scikit-learn/scikit-learn/pull/9151))."
"By default, the `OneHotEncoder` class returns a sparse array, but we can convert it to a dense array if needed by calling the `toarray()` method:"
]
},
{
@ -876,199 +864,14 @@
"metadata": {},
"outputs": [],
"source": [
"# Definition of the CategoricalEncoder class, copied from PR #9151.\n",
"# Just run this cell, or copy it to your code, do not try to understand it (yet).\n",
"\n",
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from sklearn.utils import check_array\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from scipy import sparse\n",
"\n",
"class CategoricalEncoder(BaseEstimator, TransformerMixin):\n",
" \"\"\"Encode categorical features as a numeric array.\n",
" The input to this transformer should be a matrix of integers or strings,\n",
" denoting the values taken on by categorical (discrete) features.\n",
" The features can be encoded using a one-hot aka one-of-K scheme\n",
" (``encoding='onehot'``, the default) or converted to ordinal integers\n",
" (``encoding='ordinal'``).\n",
" This encoding is needed for feeding categorical data to many scikit-learn\n",
" estimators, notably linear models and SVMs with the standard kernels.\n",
" Read more in the :ref:`User Guide <preprocessing_categorical_features>`.\n",
" Parameters\n",
" ----------\n",
" encoding : str, 'onehot', 'onehot-dense' or 'ordinal'\n",
" The type of encoding to use (default is 'onehot'):\n",
" - 'onehot': encode the features using a one-hot aka one-of-K scheme\n",
" (or also called 'dummy' encoding). This creates a binary column for\n",
" each category and returns a sparse matrix.\n",
" - 'onehot-dense': the same as 'onehot' but returns a dense array\n",
" instead of a sparse matrix.\n",
" - 'ordinal': encode the features as ordinal integers. This results in\n",
" a single column of integers (0 to n_categories - 1) per feature.\n",
" categories : 'auto' or a list of lists/arrays of values.\n",
" Categories (unique values) per feature:\n",
" - 'auto' : Determine categories automatically from the training data.\n",
" - list : ``categories[i]`` holds the categories expected in the ith\n",
" column. The passed categories are sorted before encoding the data\n",
" (used categories can be found in the ``categories_`` attribute).\n",
" dtype : number type, default np.float64\n",
" Desired dtype of output.\n",
" handle_unknown : 'error' (default) or 'ignore'\n",
" Whether to raise an error or ignore if a unknown categorical feature is\n",
" present during transform (default is to raise). When this is parameter\n",
" is set to 'ignore' and an unknown category is encountered during\n",
" transform, the resulting one-hot encoded columns for this feature\n",
" will be all zeros.\n",
" Ignoring unknown categories is not supported for\n",
" ``encoding='ordinal'``.\n",
" Attributes\n",
" ----------\n",
" categories_ : list of arrays\n",
" The categories of each feature determined during fitting. When\n",
" categories were specified manually, this holds the sorted categories\n",
" (in order corresponding with output of `transform`).\n",
" Examples\n",
" --------\n",
" Given a dataset with three features and two samples, we let the encoder\n",
" find the maximum value per feature and transform the data to a binary\n",
" one-hot encoding.\n",
" >>> from sklearn.preprocessing import CategoricalEncoder\n",
" >>> enc = CategoricalEncoder(handle_unknown='ignore')\n",
" >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])\n",
" ... # doctest: +ELLIPSIS\n",
" CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,\n",
" encoding='onehot', handle_unknown='ignore')\n",
" >>> enc.transform([[0, 1, 1], [1, 0, 4]]).toarray()\n",
" array([[ 1., 0., 0., 1., 0., 0., 1., 0., 0.],\n",
" [ 0., 1., 1., 0., 0., 0., 0., 0., 0.]])\n",
" See also\n",
" --------\n",
" sklearn.preprocessing.OneHotEncoder : performs a one-hot encoding of\n",
" integer ordinal features. The ``OneHotEncoder assumes`` that input\n",
" features take on values in the range ``[0, max(feature)]`` instead of\n",
" using the unique values.\n",
" sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of\n",
" dictionary items (also handles string-valued features).\n",
" sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot\n",
" encoding of dictionary items or strings.\n",
" \"\"\"\n",
"\n",
" def __init__(self, encoding='onehot', categories='auto', dtype=np.float64,\n",
" handle_unknown='error'):\n",
" self.encoding = encoding\n",
" self.categories = categories\n",
" self.dtype = dtype\n",
" self.handle_unknown = handle_unknown\n",
"\n",
" def fit(self, X, y=None):\n",
" \"\"\"Fit the CategoricalEncoder to X.\n",
" Parameters\n",
" ----------\n",
" X : array-like, shape [n_samples, n_feature]\n",
" The data to determine the categories of each feature.\n",
" Returns\n",
" -------\n",
" self\n",
" \"\"\"\n",
"\n",
" if self.encoding not in ['onehot', 'onehot-dense', 'ordinal']:\n",
" template = (\"encoding should be either 'onehot', 'onehot-dense' \"\n",
" \"or 'ordinal', got %s\")\n",
" raise ValueError(template % self.handle_unknown)\n",
"\n",
" if self.handle_unknown not in ['error', 'ignore']:\n",
" template = (\"handle_unknown should be either 'error' or \"\n",
" \"'ignore', got %s\")\n",
" raise ValueError(template % self.handle_unknown)\n",
"\n",
" if self.encoding == 'ordinal' and self.handle_unknown == 'ignore':\n",
" raise ValueError(\"handle_unknown='ignore' is not supported for\"\n",
" \" encoding='ordinal'\")\n",
"\n",
" X = check_array(X, dtype=np.object, accept_sparse='csc', copy=True)\n",
" n_samples, n_features = X.shape\n",
"\n",
" self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]\n",
"\n",
" for i in range(n_features):\n",
" le = self._label_encoders_[i]\n",
" Xi = X[:, i]\n",
" if self.categories == 'auto':\n",
" le.fit(Xi)\n",
" else:\n",
" valid_mask = np.in1d(Xi, self.categories[i])\n",
" if not np.all(valid_mask):\n",
" if self.handle_unknown == 'error':\n",
" diff = np.unique(Xi[~valid_mask])\n",
" msg = (\"Found unknown categories {0} in column {1}\"\n",
" \" during fit\".format(diff, i))\n",
" raise ValueError(msg)\n",
" le.classes_ = np.array(np.sort(self.categories[i]))\n",
"\n",
" self.categories_ = [le.classes_ for le in self._label_encoders_]\n",
"\n",
" return self\n",
"\n",
" def transform(self, X):\n",
" \"\"\"Transform X using one-hot encoding.\n",
" Parameters\n",
" ----------\n",
" X : array-like, shape [n_samples, n_features]\n",
" The data to encode.\n",
" Returns\n",
" -------\n",
" X_out : sparse matrix or a 2-d array\n",
" Transformed input.\n",
" \"\"\"\n",
" X = check_array(X, accept_sparse='csc', dtype=np.object, copy=True)\n",
" n_samples, n_features = X.shape\n",
" X_int = np.zeros_like(X, dtype=np.int)\n",
" X_mask = np.ones_like(X, dtype=np.bool)\n",
"\n",
" for i in range(n_features):\n",
" valid_mask = np.in1d(X[:, i], self.categories_[i])\n",
"\n",
" if not np.all(valid_mask):\n",
" if self.handle_unknown == 'error':\n",
" diff = np.unique(X[~valid_mask, i])\n",
" msg = (\"Found unknown categories {0} in column {1}\"\n",
" \" during transform\".format(diff, i))\n",
" raise ValueError(msg)\n",
" else:\n",
" # Set the problematic rows to an acceptable value and\n",
" # continue `The rows are marked `X_mask` and will be\n",
" # removed later.\n",
" X_mask[:, i] = valid_mask\n",
" X[:, i][~valid_mask] = self.categories_[i][0]\n",
" X_int[:, i] = self._label_encoders_[i].transform(X[:, i])\n",
"\n",
" if self.encoding == 'ordinal':\n",
" return X_int.astype(self.dtype, copy=False)\n",
"\n",
" mask = X_mask.ravel()\n",
" n_values = [cats.shape[0] for cats in self.categories_]\n",
" n_values = np.array([0] + n_values)\n",
" indices = np.cumsum(n_values)\n",
"\n",
" column_indices = (X_int + indices[:-1]).ravel()[mask]\n",
" row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),\n",
" n_features)[mask]\n",
" data = np.ones(n_samples * n_features)[mask]\n",
"\n",
" out = sparse.csc_matrix((data, (row_indices, column_indices)),\n",
" shape=(n_samples, indices[-1]),\n",
" dtype=self.dtype).tocsr()\n",
" if self.encoding == 'onehot-dense':\n",
" return out.toarray()\n",
" else:\n",
" return out"
"housing_cat_1hot.toarray()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `CategoricalEncoder` expects a 2D array containing one or more categorical input features. We need to reshape `housing_cat` to a 2D array:"
"Alternatively, you can set `sparse=False` when creating the `OneHotEncoder`:"
]
},
{
@ -1077,53 +880,16 @@
"metadata": {},
"outputs": [],
"source": [
"#from sklearn.preprocessing import CategoricalEncoder # in future versions of Scikit-Learn\n",
"\n",
"cat_encoder = CategoricalEncoder()\n",
"housing_cat_reshaped = housing_cat.values.reshape(-1, 1)\n",
"housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)\n",
"cat_encoder = OneHotEncoder(sparse=False)\n",
"housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
"housing_cat_1hot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The default encoding is one-hot, and it returns a sparse array. You can use `toarray()` to get a dense array:"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"housing_cat_1hot.toarray()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can specify the encoding to be `\"onehot-dense\"` to get a dense matrix rather than a sparse matrix:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"cat_encoder = CategoricalEncoder(encoding=\"onehot-dense\")\n",
"housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)\n",
"housing_cat_1hot"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"cat_encoder.categories_"
]
@ -1137,7 +903,7 @@
},
{
"cell_type": "code",
"execution_count": 69,
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
@ -1167,11 +933,13 @@
},
{
"cell_type": "code",
"execution_count": 70,
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"housing_extra_attribs = pd.DataFrame(housing_extra_attribs, columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"])\n",
"housing_extra_attribs = pd.DataFrame(\n",
" housing_extra_attribs,\n",
" columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"])\n",
"housing_extra_attribs.head()"
]
},
@ -1184,7 +952,7 @@
},
{
"cell_type": "code",
"execution_count": 71,
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
@ -1202,7 +970,7 @@
},
{
"cell_type": "code",
"execution_count": 72,
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
@ -1218,7 +986,7 @@
},
{
"cell_type": "code",
"execution_count": 73,
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
@ -1244,7 +1012,7 @@
},
{
"cell_type": "code",
"execution_count": 74,
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
@ -1260,13 +1028,13 @@
"\n",
"cat_pipeline = Pipeline([\n",
" ('selector', DataFrameSelector(cat_attribs)),\n",
" ('cat_encoder', CategoricalEncoder(encoding=\"onehot-dense\")),\n",
" ('cat_encoder', OneHotEncoder(sparse=False)),\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": 75,
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
@ -1280,7 +1048,7 @@
},
{
"cell_type": "code",
"execution_count": 76,
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
@ -1290,7 +1058,7 @@
},
{
"cell_type": "code",
"execution_count": 77,
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
@ -1306,7 +1074,7 @@
},
{
"cell_type": "code",
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"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
@ -1318,7 +1086,7 @@
},
{
"cell_type": "code",
"execution_count": 79,
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
@ -1339,7 +1107,7 @@
},
{
"cell_type": "code",
"execution_count": 80,
"execution_count": 78,
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"source": [
@ -1348,7 +1116,7 @@
},
{
"cell_type": "code",
"execution_count": 81,
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
@ -1357,7 +1125,7 @@
},
{
"cell_type": "code",
"execution_count": 82,
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
@ -1371,7 +1139,7 @@
},
{
"cell_type": "code",
"execution_count": 83,
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
@ -1383,7 +1151,7 @@
},
{
"cell_type": "code",
"execution_count": 84,
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
@ -1395,7 +1163,7 @@
},
{
"cell_type": "code",
"execution_count": 85,
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"metadata": {},
"outputs": [],
"source": [
@ -1414,7 +1182,7 @@
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
@ -1427,7 +1195,7 @@
},
{
"cell_type": "code",
"execution_count": 87,
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
@ -1441,7 +1209,7 @@
},
{
"cell_type": "code",
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"execution_count": 86,
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"source": [
@ -1453,7 +1221,7 @@
},
{
"cell_type": "code",
"execution_count": 89,
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
@ -1465,7 +1233,7 @@
},
{
"cell_type": "code",
"execution_count": 90,
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
@ -1477,7 +1245,7 @@
},
{
"cell_type": "code",
"execution_count": 91,
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
@ -1491,7 +1259,7 @@
},
{
"cell_type": "code",
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"execution_count": 90,
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"outputs": [],
"source": [
@ -1501,7 +1269,7 @@
},
{
"cell_type": "code",
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"outputs": [],
"source": [
@ -1517,7 +1285,7 @@
},
{
"cell_type": "code",
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"source": [
@ -1546,7 +1314,7 @@
},
{
"cell_type": "code",
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"metadata": {},
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"source": [
@ -1555,7 +1323,7 @@
},
{
"cell_type": "code",
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"source": [
@ -1571,7 +1339,7 @@
},
{
"cell_type": "code",
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"outputs": [],
"source": [
@ -1582,7 +1350,7 @@
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
@ -1591,7 +1359,7 @@
},
{
"cell_type": "code",
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"source": [
@ -1611,7 +1379,7 @@
},
{
"cell_type": "code",
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"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
@ -1622,7 +1390,7 @@
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
@ -1632,7 +1400,7 @@
},
{
"cell_type": "code",
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"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
@ -1645,7 +1413,7 @@
},
{
"cell_type": "code",
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"source": [
@ -1663,7 +1431,7 @@
},
{
"cell_type": "code",
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@ -1686,7 +1454,7 @@
},
{
"cell_type": "code",
"execution_count": 105,
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"metadata": {},
"outputs": [],
"source": [
@ -1708,7 +1476,7 @@
},
{
"cell_type": "code",
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"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
@ -1717,7 +1485,7 @@
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
@ -1736,7 +1504,7 @@
},
{
"cell_type": "code",
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"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
@ -1774,7 +1542,7 @@
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
@ -1800,7 +1568,7 @@
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
@ -1818,7 +1586,7 @@
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{
"cell_type": "code",
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# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
# Joris Van den Bossche <jorisvandenbossche@gmail.com>
# License: BSD 3 clause
from __future__ import division
import numbers
import warnings
import numpy as np
from scipy import sparse
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.externals import six
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted, FLOAT_DTYPES
from sklearn.preprocessing.label import LabelEncoder
BOUNDS_THRESHOLD = 1e-7
zip = six.moves.zip
map = six.moves.map
range = six.moves.range
__all__ = [
'OneHotEncoder',
'OrdinalEncoder'
]
def _argmax(arr_or_spmatrix, axis=None):
return arr_or_spmatrix.argmax(axis=axis)
def _handle_zeros_in_scale(scale, copy=True):
''' Makes sure that whenever scale is zero, we handle it correctly.
This happens in most scalers when we have constant features.'''
# if we are fitting on 1D arrays, scale might be a scalar
if np.isscalar(scale):
if scale == .0:
scale = 1.
return scale
elif isinstance(scale, np.ndarray):
if copy:
# New array to avoid side-effects
scale = scale.copy()
scale[scale == 0.0] = 1.0
return scale
def _transform_selected(X, transform, selected="all", copy=True):
"""Apply a transform function to portion of selected features
Parameters
----------
X : {array-like, sparse matrix}, shape [n_samples, n_features]
Dense array or sparse matrix.
transform : callable
A callable transform(X) -> X_transformed
copy : boolean, optional
Copy X even if it could be avoided.
selected: "all" or array of indices or mask
Specify which features to apply the transform to.
Returns
-------
X : array or sparse matrix, shape=(n_samples, n_features_new)
"""
X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)
if isinstance(selected, six.string_types) and selected == "all":
return transform(X)
if len(selected) == 0:
return X
n_features = X.shape[1]
ind = np.arange(n_features)
sel = np.zeros(n_features, dtype=bool)
sel[np.asarray(selected)] = True
not_sel = np.logical_not(sel)
n_selected = np.sum(sel)
if n_selected == 0:
# No features selected.
return X
elif n_selected == n_features:
# All features selected.
return transform(X)
else:
X_sel = transform(X[:, ind[sel]])
X_not_sel = X[:, ind[not_sel]]
if sparse.issparse(X_sel) or sparse.issparse(X_not_sel):
return sparse.hstack((X_sel, X_not_sel))
else:
return np.hstack((X_sel, X_not_sel))
class _BaseEncoder(BaseEstimator, TransformerMixin):
"""
Base class for encoders that includes the code to categorize and
transform the input features.
"""
def _fit(self, X, handle_unknown='error'):
X_temp = check_array(X, dtype=None)
if not hasattr(X, 'dtype') and np.issubdtype(X_temp.dtype, np.str_):
X = check_array(X, dtype=np.object)
else:
X = X_temp
n_samples, n_features = X.shape
if self.categories != 'auto':
for cats in self.categories:
if not np.all(np.sort(cats) == np.array(cats)):
raise ValueError("Unsorted categories are not yet "
"supported")
if len(self.categories) != n_features:
raise ValueError("Shape mismatch: if n_values is an array,"
" it has to be of shape (n_features,).")
self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]
for i in range(n_features):
le = self._label_encoders_[i]
Xi = X[:, i]
if self.categories == 'auto':
le.fit(Xi)
else:
if handle_unknown == 'error':
valid_mask = np.in1d(Xi, self.categories[i])
if not np.all(valid_mask):
diff = np.unique(Xi[~valid_mask])
msg = ("Found unknown categories {0} in column {1}"
" during fit".format(diff, i))
raise ValueError(msg)
le.classes_ = np.array(self.categories[i])
self.categories_ = [le.classes_ for le in self._label_encoders_]
def _transform(self, X, handle_unknown='error'):
X_temp = check_array(X, dtype=None)
if not hasattr(X, 'dtype') and np.issubdtype(X_temp.dtype, np.str_):
X = check_array(X, dtype=np.object)
else:
X = X_temp
_, n_features = X.shape
X_int = np.zeros_like(X, dtype=np.int)
X_mask = np.ones_like(X, dtype=np.bool)
for i in range(n_features):
Xi = X[:, i]
valid_mask = np.in1d(Xi, self.categories_[i])
if not np.all(valid_mask):
if handle_unknown == 'error':
diff = np.unique(X[~valid_mask, i])
msg = ("Found unknown categories {0} in column {1}"
" during transform".format(diff, i))
raise ValueError(msg)
else:
# Set the problematic rows to an acceptable value and
# continue `The rows are marked `X_mask` and will be
# removed later.
X_mask[:, i] = valid_mask
Xi = Xi.copy()
Xi[~valid_mask] = self.categories_[i][0]
X_int[:, i] = self._label_encoders_[i].transform(Xi)
return X_int, X_mask
WARNING_MSG = (
"The handling of integer data will change in the future. Currently, the "
"categories are determined based on the range [0, max(values)], while "
"in the future they will be determined based on the unique values.\n"
"If you want the future behaviour, you can specify \"categories='auto'\"."
)
class OneHotEncoder(_BaseEncoder):
"""Encode categorical integer features as a one-hot numeric array.
The input to this transformer should be an array-like of integers or
strings, denoting the values taken on by categorical (discrete) features.
The features are encoded using a one-hot (aka 'one-of-K' or 'dummy')
encoding scheme. This creates a binary column for each category and
returns a sparse matrix or dense array.
By default, the encoder derives the categories based on the unique values
in each feature. Alternatively, you can also specify the `categories`
manually.
The OneHotEncoder previously assumed that the input features take on
values in the range [0, max(values)). This behaviour is deprecated.
This encoding is needed for feeding categorical data to many scikit-learn
estimators, notably linear models and SVMs with the standard kernels.
Note: a one-hot encoding of y labels should use a LabelBinarizer
instead.
Read more in the :ref:`User Guide <preprocessing_categorical_features>`.
Parameters
----------
categories : 'auto' or a list of lists/arrays of values.
Categories (unique values) per feature:
- 'auto' : Determine categories automatically from the training data.
- list : ``categories[i]`` holds the categories expected in the ith
column. The passed categories must be sorted and should not mix
strings and numeric values.
The used categories can be found in the ``categories_`` attribute.
sparse : boolean, default=True
Will return sparse matrix if set True else will return an array.
dtype : number type, default=np.float
Desired dtype of output.
handle_unknown : 'error' (default) or 'ignore'
Whether to raise an error or ignore if a unknown categorical feature is
present during transform (default is to raise). When this parameter
is set to 'ignore' and an unknown category is encountered during
transform, the resulting one-hot encoded columns for this feature
will be all zeros. In the inverse transform, an unknown category
will be denoted as None.
n_values : 'auto', int or array of ints
Number of values per feature.
- 'auto' : determine value range from training data.
- int : number of categorical values per feature.
Each feature value should be in ``range(n_values)``
- array : ``n_values[i]`` is the number of categorical values in
``X[:, i]``. Each feature value should be
in ``range(n_values[i])``
.. deprecated:: 0.20
The `n_values` keyword is deprecated and will be removed in 0.22.
Use `categories` instead.
categorical_features : "all" or array of indices or mask
Specify what features are treated as categorical.
- 'all' (default): All features are treated as categorical.
- array of indices: Array of categorical feature indices.
- mask: Array of length n_features and with dtype=bool.
Non-categorical features are always stacked to the right of the matrix.
.. deprecated:: 0.20
The `categorical_features` keyword is deprecated and will be
removed in 0.22.
Attributes
----------
categories_ : list of arrays
The categories of each feature determined during fitting
(in order corresponding with output of ``transform``).
active_features_ : array
Indices for active features, meaning values that actually occur
in the training set. Only available when n_values is ``'auto'``.
.. deprecated:: 0.20
feature_indices_ : array of shape (n_features,)
Indices to feature ranges.
Feature ``i`` in the original data is mapped to features
from ``feature_indices_[i]`` to ``feature_indices_[i+1]``
(and then potentially masked by `active_features_` afterwards)
.. deprecated:: 0.20
n_values_ : array of shape (n_features,)
Maximum number of values per feature.
.. deprecated:: 0.20
Examples
--------
Given a dataset with two features, we let the encoder find the unique
values per feature and transform the data to a binary one-hot encoding.
>>> from sklearn.preprocessing import OneHotEncoder
>>> enc = OneHotEncoder(handle_unknown='ignore')
>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]
>>> enc.fit(X)
... # doctest: +ELLIPSIS
OneHotEncoder(categories='auto', dtype=<... 'numpy.float64'>,
handle_unknown='ignore', sparse=True)
>>> enc.categories_
[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]
>>> enc.transform([['Female', 1], ['Male', 4]]).toarray()
array([[ 1., 0., 1., 0., 0.],
[ 0., 1., 0., 0., 0.]])
>>> enc.inverse_transform([[0, 1, 1, 0, 0], [0, 0, 0, 1, 0]])
array([['Male', 1],
[None, 2]], dtype=object)
See also
--------
sklearn.preprocessing.OrdinalEncoder : performs an ordinal (integer)
encoding of the categorical features.
sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of
dictionary items (also handles string-valued features).
sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot
encoding of dictionary items or strings.
sklearn.preprocessing.LabelBinarizer : binarizes labels in a one-vs-all
fashion.
sklearn.preprocessing.MultiLabelBinarizer : transforms between iterable of
iterables and a multilabel format, e.g. a (samples x classes) binary
matrix indicating the presence of a class label.
"""
def __init__(self, n_values=None, categorical_features=None,
categories=None, sparse=True, dtype=np.float64,
handle_unknown='error'):
self._categories = categories
if categories is None:
self.categories = 'auto'
else:
self.categories = categories
self.sparse = sparse
self.dtype = dtype
self.handle_unknown = handle_unknown
if n_values is not None:
pass
# warnings.warn("Deprecated", DeprecationWarning)
else:
n_values = "auto"
self._deprecated_n_values = n_values
if categorical_features is not None:
pass
# warnings.warn("Deprecated", DeprecationWarning)
else:
categorical_features = "all"
self._deprecated_categorical_features = categorical_features
# Deprecated keywords
@property
def n_values(self):
warnings.warn("The 'n_values' parameter is deprecated.",
DeprecationWarning)
return self._deprecated_n_values
@n_values.setter
def n_values(self, value):
warnings.warn("The 'n_values' parameter is deprecated.",
DeprecationWarning)
self._deprecated_n_values = value
@property
def categorical_features(self):
warnings.warn("The 'categorical_features' parameter is deprecated.",
DeprecationWarning)
return self._deprecated_categorical_features
@categorical_features.setter
def categorical_features(self, value):
warnings.warn("The 'categorical_features' parameter is deprecated.",
DeprecationWarning)
self._deprecated_categorical_features = value
# Deprecated attributes
@property
def active_features_(self):
check_is_fitted(self, 'categories_')
warnings.warn("The 'active_features_' attribute is deprecated.",
DeprecationWarning)
return self._active_features_
@property
def feature_indices_(self):
check_is_fitted(self, 'categories_')
warnings.warn("The 'feature_indices_' attribute is deprecated.",
DeprecationWarning)
return self._feature_indices_
@property
def n_values_(self):
check_is_fitted(self, 'categories_')
warnings.warn("The 'n_values_' attribute is deprecated.",
DeprecationWarning)
return self._n_values_
def _handle_deprecations(self, X):
user_set_categories = False
if self._categories is not None:
self._legacy_mode = False
user_set_categories = True
elif self._deprecated_n_values != 'auto':
msg = (
"Passing 'n_values' is deprecated and will be removed in a "
"future release. You can use the 'categories' keyword instead."
" 'n_values=n' corresponds to 'n_values=[range(n)]'.")
warnings.warn(msg, DeprecationWarning)
# we internally translate this to the correct categories
# and don't use legacy mode
X = check_array(X, dtype=np.int)
if isinstance(self._deprecated_n_values, numbers.Integral):
n_features = X.shape[1]
self.categories = [
list(range(self._deprecated_n_values))
for _ in range(n_features)]
n_values = np.empty(n_features, dtype=np.int)
n_values.fill(self._deprecated_n_values)
else:
try:
n_values = np.asarray(self._deprecated_n_values, dtype=int)
self.categories = [list(range(i))
for i in self._deprecated_n_values]
except (ValueError, TypeError):
raise TypeError(
"Wrong type for parameter `n_values`. Expected 'auto',"
" int or array of ints, got %r".format(type(X)))
self._n_values_ = n_values
n_values = np.hstack([[0], n_values])
indices = np.cumsum(n_values)
self._feature_indices_ = indices
self._legacy_mode = False
else: # n_values = 'auto'
if self.handle_unknown == 'ignore':
# no change in behaviour, no need to raise deprecation warning
self._legacy_mode = False
else:
# check if we have integer or categorical input
try:
X = check_array(X, dtype=np.int)
except ValueError:
self._legacy_mode = False
else:
warnings.warn(WARNING_MSG, DeprecationWarning)
self._legacy_mode = True
if (not isinstance(self._deprecated_categorical_features,
six.string_types)
or (isinstance(self._deprecated_categorical_features,
six.string_types)
and self._deprecated_categorical_features != 'all')):
if user_set_categories:
raise ValueError(
"The 'categorical_features' keyword is deprecated, and "
"cannot be used together with specifying 'categories'.")
warnings.warn("The 'categorical_features' keyword is deprecated.",
DeprecationWarning)
self._legacy_mode = True
def fit(self, X, y=None):
"""Fit OneHotEncoder to X.
Parameters
----------
X : array-like, shape [n_samples, n_feature]
The data to determine the categories of each feature.
Returns
-------
self
"""
if self.handle_unknown not in ['error', 'ignore']:
template = ("handle_unknown should be either 'error' or "
"'ignore', got %s")
raise ValueError(template % self.handle_unknown)
self._handle_deprecations(X)
if self._legacy_mode:
# TODO not with _transform_selected ??
self._legacy_fit_transform(X)
return self
else:
self._fit(X, handle_unknown=self.handle_unknown)
return self
def _legacy_fit_transform(self, X):
"""Assumes X contains only categorical features."""
self_n_values = self._deprecated_n_values
dtype = getattr(X, 'dtype', None)
X = check_array(X, dtype=np.int)
if np.any(X < 0):
raise ValueError("X needs to contain only non-negative integers.")
n_samples, n_features = X.shape
if (isinstance(self_n_values, six.string_types) and
self_n_values == 'auto'):
n_values = np.max(X, axis=0) + 1
elif isinstance(self_n_values, numbers.Integral):
if (np.max(X, axis=0) >= self_n_values).any():
raise ValueError("Feature out of bounds for n_values=%d"
% self_n_values)
n_values = np.empty(n_features, dtype=np.int)
n_values.fill(self_n_values)
else:
try:
n_values = np.asarray(self_n_values, dtype=int)
except (ValueError, TypeError):
raise TypeError("Wrong type for parameter `n_values`. Expected"
" 'auto', int or array of ints, got %r"
% type(X))
if n_values.ndim < 1 or n_values.shape[0] != X.shape[1]:
raise ValueError("Shape mismatch: if n_values is an array,"
" it has to be of shape (n_features,).")
self._n_values_ = n_values
self.categories_ = [np.arange(n_val - 1, dtype=dtype)
for n_val in n_values]
n_values = np.hstack([[0], n_values])
indices = np.cumsum(n_values)
self._feature_indices_ = indices
column_indices = (X + indices[:-1]).ravel()
row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),
n_features)
data = np.ones(n_samples * n_features)
out = sparse.coo_matrix((data, (row_indices, column_indices)),
shape=(n_samples, indices[-1]),
dtype=self.dtype).tocsr()
if (isinstance(self_n_values, six.string_types) and
self_n_values == 'auto'):
mask = np.array(out.sum(axis=0)).ravel() != 0
active_features = np.where(mask)[0]
out = out[:, active_features]
self._active_features_ = active_features
self.categories_ = [
np.unique(X[:, i]).astype(dtype) if dtype else np.unique(X[:, i])
for i in range(n_features)]
#import pdb; pdb.set_trace()
return out if self.sparse else out.toarray()
def fit_transform(self, X, y=None):
"""Fit OneHotEncoder to X, then transform X.
Equivalent to self.fit(X).transform(X), but more convenient and more
efficient. See fit for the parameters, transform for the return value.
Parameters
----------
X : array-like, shape [n_samples, n_feature]
Input array of type int.
"""
if self.handle_unknown not in ['error', 'ignore']:
template = ("handle_unknown should be either 'error' or "
"'ignore', got %s")
raise ValueError(template % self.handle_unknown)
self._handle_deprecations(X)
if self._legacy_mode:
return _transform_selected(X, self._legacy_fit_transform,
self._deprecated_categorical_features,
copy=True)
else:
return self.fit(X).transform(X)
def _legacy_transform(self, X):
"""Assumes X contains only categorical features."""
self_n_values = self._deprecated_n_values
X = check_array(X, dtype=np.int)
if np.any(X < 0):
raise ValueError("X needs to contain only non-negative integers.")
n_samples, n_features = X.shape
indices = self._feature_indices_
if n_features != indices.shape[0] - 1:
raise ValueError("X has different shape than during fitting."
" Expected %d, got %d."
% (indices.shape[0] - 1, n_features))
# We use only those categorical features of X that are known using fit.
# i.e lesser than n_values_ using mask.
# This means, if self.handle_unknown is "ignore", the row_indices and
# col_indices corresponding to the unknown categorical feature are
# ignored.
mask = (X < self._n_values_).ravel()
if np.any(~mask):
if self.handle_unknown not in ['error', 'ignore']:
raise ValueError("handle_unknown should be either error or "
"unknown got %s" % self.handle_unknown)
if self.handle_unknown == 'error':
raise ValueError("unknown categorical feature present %s "
"during transform." % X.ravel()[~mask])
column_indices = (X + indices[:-1]).ravel()[mask]
row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),
n_features)[mask]
data = np.ones(np.sum(mask))
out = sparse.coo_matrix((data, (row_indices, column_indices)),
shape=(n_samples, indices[-1]),
dtype=self.dtype).tocsr()
if (isinstance(self_n_values, six.string_types) and
self_n_values == 'auto'):
out = out[:, self._active_features_]
return out if self.sparse else out.toarray()
def _transform_new(self, X):
"""New implementation assuming categorical input"""
X_temp = check_array(X, dtype=None)
if not hasattr(X, 'dtype') and np.issubdtype(X_temp.dtype, np.str_):
X = check_array(X, dtype=np.object)
else:
X = X_temp
n_samples, n_features = X.shape
X_int, X_mask = self._transform(X, handle_unknown=self.handle_unknown)
mask = X_mask.ravel()
n_values = [cats.shape[0] for cats in self.categories_]
n_values = np.array([0] + n_values)
feature_indices = np.cumsum(n_values)
indices = (X_int + feature_indices[:-1]).ravel()[mask]
indptr = X_mask.sum(axis=1).cumsum()
indptr = np.insert(indptr, 0, 0)
data = np.ones(n_samples * n_features)[mask]
out = sparse.csr_matrix((data, indices, indptr),
shape=(n_samples, feature_indices[-1]),
dtype=self.dtype)
if not self.sparse:
return out.toarray()
else:
return out
def transform(self, X):
"""Transform X using one-hot encoding.
Parameters
----------
X : array-like, shape [n_samples, n_features]
The data to encode.
Returns
-------
X_out : sparse matrix if sparse=True else a 2-d array
Transformed input.
"""
if not self._legacy_mode:
return self._transform_new(X)
else:
return _transform_selected(X, self._legacy_transform,
self._deprecated_categorical_features,
copy=True)
def inverse_transform(self, X):
"""Convert back the data to the original representation.
In case unknown categories are encountered (all zero's in the
one-hot encoding), ``None`` is used to represent this category.
Parameters
----------
X : array-like or sparse matrix, shape [n_samples, n_encoded_features]
The transformed data.
Returns
-------
X_tr : array-like, shape [n_samples, n_features]
Inverse transformed array.
"""
# if self._legacy_mode:
# raise ValueError("only supported for categorical features")
check_is_fitted(self, 'categories_')
X = check_array(X, accept_sparse='csr')
n_samples, _ = X.shape
n_features = len(self.categories_)
n_transformed_features = sum([len(cats) for cats in self.categories_])
# validate shape of passed X
msg = ("Shape of the passed X data is not correct. Expected {0} "
"columns, got {1}.")
if X.shape[1] != n_transformed_features:
raise ValueError(msg.format(n_transformed_features, X.shape[1]))
# create resulting array of appropriate dtype
dt = np.find_common_type([cat.dtype for cat in self.categories_], [])
X_tr = np.empty((n_samples, n_features), dtype=dt)
j = 0
found_unknown = {}
for i in range(n_features):
n_categories = len(self.categories_[i])
sub = X[:, j:j + n_categories]
# for sparse X argmax returns 2D matrix, ensure 1D array
labels = np.asarray(_argmax(sub, axis=1)).flatten()
X_tr[:, i] = self.categories_[i][labels]
if self.handle_unknown == 'ignore':
# ignored unknown categories: we have a row of all zero's
unknown = np.asarray(sub.sum(axis=1) == 0).flatten()
if unknown.any():
found_unknown[i] = unknown
j += n_categories
# if ignored are found: potentially need to upcast result to
# insert None values
if found_unknown:
if X_tr.dtype != object:
X_tr = X_tr.astype(object)
for idx, mask in found_unknown.items():
X_tr[mask, idx] = None
return X_tr
class OrdinalEncoder(_BaseEncoder):
"""Encode categorical features as an integer array.
The input to this transformer should be an array-like of integers or
strings, denoting the values taken on by categorical (discrete) features.
The features are converted to ordinal integers. This results in
a single column of integers (0 to n_categories - 1) per feature.
Read more in the :ref:`User Guide <preprocessing_categorical_features>`.
Parameters
----------
categories : 'auto' or a list of lists/arrays of values.
Categories (unique values) per feature:
- 'auto' : Determine categories automatically from the training data.
- list : ``categories[i]`` holds the categories expected in the ith
column. The passed categories must be sorted and should not mix
strings and numeric values.
The used categories can be found in the ``categories_`` attribute.
dtype : number type, default np.float64
Desired dtype of output.
Attributes
----------
categories_ : list of arrays
The categories of each feature determined during fitting
(in order corresponding with output of ``transform``).
Examples
--------
Given a dataset with two features, we let the encoder find the unique
values per feature and transform the data to a binary one-hot encoding.
>>> from sklearn.preprocessing import OrdinalEncoder
>>> enc = OrdinalEncoder()
>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]
>>> enc.fit(X)
... # doctest: +ELLIPSIS
OrdinalEncoder(categories='auto', dtype=<... 'numpy.float64'>)
>>> enc.categories_
[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]
>>> enc.transform([['Female', 3], ['Male', 1]])
array([[ 0., 2.],
[ 1., 0.]])
>>> enc.inverse_transform([[1, 0], [0, 1]])
array([['Male', 1],
['Female', 2]], dtype=object)
See also
--------
sklearn.preprocessing.OneHotEncoder : performs a one-hot encoding of
categorical features.
sklearn.preprocessing.LabelEncoder : encodes target labels with values
between 0 and n_classes-1.
sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of
dictionary items (also handles string-valued features).
sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot
encoding of dictionary items or strings.
"""
def __init__(self, categories='auto', dtype=np.float64):
self.categories = categories
self.dtype = dtype
def fit(self, X, y=None):
"""Fit the OrdinalEncoder to X.
Parameters
----------
X : array-like, shape [n_samples, n_features]
The data to determine the categories of each feature.
Returns
-------
self
"""
self._fit(X)
return self
def transform(self, X):
"""Transform X to ordinal codes.
Parameters
----------
X : array-like, shape [n_samples, n_features]
The data to encode.
Returns
-------
X_out : sparse matrix or a 2-d array
Transformed input.
"""
X_int, _ = self._transform(X)
return X_int.astype(self.dtype, copy=False)
def inverse_transform(self, X):
"""Convert back the data to the original representation.
Parameters
----------
X : array-like or sparse matrix, shape [n_samples, n_encoded_features]
The transformed data.
Returns
-------
X_tr : array-like, shape [n_samples, n_features]
Inverse transformed array.
"""
check_is_fitted(self, 'categories_')
X = check_array(X, accept_sparse='csr')
n_samples, _ = X.shape
n_features = len(self.categories_)
# validate shape of passed X
msg = ("Shape of the passed X data is not correct. Expected {0} "
"columns, got {1}.")
if X.shape[1] != n_features:
raise ValueError(msg.format(n_features, X.shape[1]))
# create resulting array of appropriate dtype
dt = np.find_common_type([cat.dtype for cat in self.categories_], [])
X_tr = np.empty((n_samples, n_features), dtype=dt)
for i in range(n_features):
labels = X[:, i].astype('int64')
X_tr[:, i] = self.categories_[i][labels]
return X_tr