Merge pull request #332 from rasbt/transactionarray

TransactionEncoder

Author: rasbt

docs/mkdocs.yml

@@ -105,9 +105,9 @@ pages:
     - user_guide/preprocessing/MeanCenterer.md
     - user_guide/preprocessing/minmax_scaling.md
     - user_guide/preprocessing/one-hot_encoding.md
-    - user_guide/preprocessing/OnehotTransactions.md
     - user_guide/preprocessing/shuffle_arrays_unison.md
     - user_guide/preprocessing/standardize.md
+    - user_guide/preprocessing/TransactionEncoder.md
   - regressor:
     - user_guide/regressor/LinearRegression.md
     - user_guide/regressor/StackingCVRegressor.md

docs/sources/CHANGELOG.md

@@ -33,6 +33,7 @@ The CHANGELOG for the current development version is available at
 - The `plot_decision_regions` function now automatically determines the optimal setting based on the feature dimensions and supports anti-aliasing. The old `res`  parameter has been deprecated. ([#309](https://github.com/rasbt/mlxtend/pull/309) by [Guillaume Poirier-Morency](https://github.com/arteymix))
 - Apriori code is faster due to optimization in `onehot transformation` and the amount of candidates generated by the `apriori` algorithm. ([#327](https://github.com/rasbt/mlxtend/pull/327) by [Jakub Smid](https://github.com/jaksmid))
 - The `OnehotTransactions` class (which is typically often used in combination with the `apriori` function for association rule mining) is now more memory efficient as it uses boolean arrays instead of integer arrays. In addition, the `OnehotTransactions` class can be now be provided with `sparse` argument to generate sparse representations of the `onehot` matrix to further improve memory efficiency. ([#328](https://github.com/rasbt/mlxtend/pull/328) by [Jakub Smid](https://github.com/jaksmid))
+- The `OneHotTransactions` has been deprecated and replaced by the `TransactionEncoder` ([#332](https://github.com/rasbt/mlxtend/pull/332)
 
 ##### Bug Fixes
 

docs/sources/USER_GUIDE_INDEX.md

@@ -85,9 +85,9 @@
 - [MeanCenterer](user_guide/preprocessing/MeanCenterer.md)
 - [minmax_scaling](user_guide/preprocessing/minmax_scaling.md)
 - [one-hot_encoding](user_guide/preprocessing/one-hot_encoding.md)
-- [OnehotTransactions](user_guide/preprocessing/OnehotTransactions.md)
 - [shuffle_arrays_unison](user_guide/preprocessing/shuffle_arrays_unison.md)
 - [standardize](user_guide/preprocessing/standardize.md)
+- [TransactionEncoder](user_guide/preprocessing/TransactionEncoder.md)
 
 ## `regressor`
 - [LinearRegression](user_guide/regressor/LinearRegression.md)

docs/sources/user_guide/frequent_patterns/apriori.ipynb

@@ -78,7 +78,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can transform it into the right format via the `OnehotTransactions` encoder as follows:"
+    "We can transform it into the right format via the `TransactionEncoder` as follows:"
    ]
   },
   {
@@ -218,11 +218,11 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "from mlxtend.preprocessing import OnehotTransactions\n",
+    "from mlxtend.preprocessing import TransactionEncoder\n",
     "\n",
-    "oht = OnehotTransactions()\n",
-    "oht_ary = oht.fit(dataset).transform(dataset)\n",
-    "df = pd.DataFrame(oht_ary, columns=oht.columns_)\n",
+    "te = TransactionEncoder()\n",
+    "te_ary = te.fit(dataset).transform(dataset)\n",
+    "df = pd.DataFrame(te_ary, columns=te.columns_)\n",
     "df"
    ]
   },
@@ -827,8 +827,8 @@
     }
    ],
    "source": [
-    "oht_ary = oht.fit(dataset).transform(dataset, sparse=True)\n",
-    "sparse_df = pd.SparseDataFrame(oht_ary, columns=oht.columns_, default_fill_value=False)\n",
+    "oht_ary = te.fit(dataset).transform(dataset, sparse=True)\n",
+    "sparse_df = pd.SparseDataFrame(te_ary, columns=te.columns_, default_fill_value=False)\n",
     "sparse_df"
    ]
   },
@@ -1034,7 +1034,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,