Fpmax (#553)

* Add fpmax algorithm to frequent patterns module * Refactor unit tests for frequent patterns * Small fix to fpmax * Add unit tests for fpmax * Fix unit tests for apriori and growth plus more refactoring * Change EOL to match rest of repo (LF instead of CRLF) * Remove unittest parent class from frequent pattern tests as it is unneeded * Improve valid val check performance in frequent patterns * Fix some pep8 issues * Fix pytest issues * Refactor fpgrowth * add boolean array to unit tests * add documentation
rasbt · Jun 23, 2019 · cbe17d7 · cbe17d7
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diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
@@ -93,6 +93,7 @@ nav:
     - user_guide/frequent_patterns/apriori.md
     - user_guide/frequent_patterns/association_rules.md
     - user_guide/frequent_patterns/fpgrowth.md
+    - user_guide/frequent_patterns/fpmax.md
   - general concepts:
     - user_guide/general_concepts/activation-functions.md
     - user_guide/general_concepts/gradient-optimization.md

diff --git a/docs/sources/CHANGELOG.md b/docs/sources/CHANGELOG.md
@@ -21,8 +21,9 @@ The CHANGELOG for the current development version is available at
 - Added optional `groups` parameter to `SequentialFeatureSelector` and `ExhaustiveFeatureSelector` `fit()` methods for forwarding to sklearn CV ([#537](https://github.com/rasbt/mlxtend/pull/537) via [arc12](https://github.com/qiaguhttps://github.com/arc12))
 - Added a new `plot_pca_correlation_graph` function to the `mlxtend.plotting` submodule for plotting a PCA correlation graph. ([#544](https://github.com/rasbt/mlxtend/pull/544) via [Gabriel-Azevedo-Ferreira](https://github.com/qiaguhttps://github.com/Gabriel-Azevedo-Ferreira))
 - Added a `zoom_factor` parameter to the `mlxten.plotting.plot_decision_region` function that allows users to zoom in and out of the decision region plots. ([#545](https://github.com/rasbt/mlxtend/pull/545))
-- Added a function `fpgrowth` that implements the FP-Growth algorithm for mining frequent itemsets as a drop-in replacement of the existing `apriori` algorithm. ([#550](https://github.com/rasbt/mlxtend/pull/550) via [Steve Harenberg](https://github.com/harenbergsd))
+- Added a function `fpgrowth` that implements the FP-Growth algorithm for mining frequent itemsets as a drop-in replacement for the existing `apriori` algorithm. ([#550](https://github.com/rasbt/mlxtend/pull/550) via [Steve Harenberg](https://github.com/harenbergsd))
 - New `heatmap` function in `mlxtend.plotting`.  ([#552](https://github.com/rasbt/mlxtend/pull/552))
+- Added a function `fpmax` that implements the FP-Max algorithm for mining maximal itemsets as a drop-in replacement for the `fpgrowth` algorithm. ([#553](https://github.com/rasbt/mlxtend/pull/553) via [Steve Harenberg](https://github.com/harenbergsd))
 - New `figsize` parameter for the `plot_decision_regions` function in `mlxtend.plotting`. ([#555](https://github.com/rasbt/mlxtend/pull/555) via [Mirza Hasanbasic](https://github.com/kazyka))
 
 ##### Changes

diff --git a/docs/sources/USER_GUIDE_INDEX.md b/docs/sources/USER_GUIDE_INDEX.md
@@ -64,6 +64,7 @@
 - [apriori](user_guide/frequent_patterns/apriori.md)
 - [association_rules](user_guide/frequent_patterns/association_rules.md)
 - [fpgrowth](user_guide/frequent_patterns/fpgrowth.md)
+- [fpmax](user_guide/frequent_patterns/fpmax.md)
 
 ## `general concepts`
 - [activation-functions](user_guide/general_concepts/activation-functions.md)

diff --git a/docs/sources/user_guide/frequent_patterns/apriori.ipynb b/docs/sources/user_guide/frequent_patterns/apriori.ipynb
@@ -41,7 +41,12 @@
    "source": [
     "## References\n",
     "\n",
-    "[1] Agrawal, Rakesh, and Ramakrishnan Srikant. \"[Fast algorithms for mining association rules](https://www.it.uu.se/edu/course/homepage/infoutv/ht08/vldb94_rj.pdf).\" Proc. 20th int. conf. very large data bases, VLDB. Vol. 1215. 1994."
+    "[1] Agrawal, Rakesh, and Ramakrishnan Srikant. \"[Fast algorithms for mining association rules](https://www.it.uu.se/edu/course/homepage/infoutv/ht08/vldb94_rj.pdf).\" Proc. 20th int. conf. very large data bases, VLDB. Vol. 1215. 1994.\n",
+    "\n",
+    "## Related\n",
+    "\n",
+    "- [FP-Growth](../fpgrowth.md)\n",
+    "- [FP-Max](../fpmax.md)"
    ]
   },
   {
@@ -53,9 +58,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
     "The `apriori` function expects data in a one-hot encoded pandas DataFrame.\n",
     "Suppose we have the following transaction data:"
@@ -923,23 +926,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\r",
-      "Iteration: 1 | Sampling itemset size 2\r",
-      "Iteration: 2 | Sampling itemset size 2\r",
-      "Iteration: 3 | Sampling itemset size 2\r",
-      "Iteration: 4 | Sampling itemset size 2\r",
-      "Iteration: 5 | Sampling itemset size 2\r",
-      "Iteration: 6 | Sampling itemset size 2\r",
-      "Iteration: 7 | Sampling itemset size 2\r",
-      "Iteration: 8 | Sampling itemset size 2\r",
-      "Iteration: 9 | Sampling itemset size 2\r",
-      "Iteration: 10 | Sampling itemset size 2\r",
-      "Iteration: 11 | Sampling itemset size 3\r",
-      "Iteration: 12 | Sampling itemset size 3\r",
-      "Iteration: 13 | Sampling itemset size 3\r",
-      "Iteration: 14 | Sampling itemset size 3\r",
-      "Iteration: 15 | Sampling itemset size 3\r",
-      "Iteration: 16 | Sampling itemset size 3\r",
       "Iteration: 17 | Sampling itemset size 3\n"
      ]
     },
@@ -1176,5 +1162,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 2
 }
diff --git a/docs/sources/user_guide/frequent_patterns/fpgrowth.ipynb b/docs/sources/user_guide/frequent_patterns/fpgrowth.ipynb
@@ -47,7 +47,12 @@
     "\n",
     "[1] Han, Jiawei, Jian Pei, Yiwen Yin, and Runying Mao. \"Mining frequent patterns without candidate generation. \"[A frequent-pattern tree approach.](https://link.springer.com/content/pdf/10.1023%2FB%3ADAMI.0000005258.31418.83.pdf)\" Data mining and knowledge discovery 8, no. 1 (2004): 53-87.\n",
     "\n",
-    "[2] Agrawal, Rakesh, and Ramakrishnan Srikant. \"[Fast algorithms for mining association rules](https://www.it.uu.se/edu/course/homepage/infoutv/ht08/vldb94_rj.pdf).\" Proc. 20th int. conf. very large data bases, VLDB. Vol. 1215. 1994."
+    "[2] Agrawal, Rakesh, and Ramakrishnan Srikant. \"[Fast algorithms for mining association rules](https://www.it.uu.se/edu/course/homepage/infoutv/ht08/vldb94_rj.pdf).\" Proc. 20th int. conf. very large data bases, VLDB. Vol. 1215. 1994.\n",
+    "\n",
+    "## Related\n",
+    "\n",
+    "- [FP-Max](../fpmax.md)\n",
+    "- [Apriori](../apriori.md)"
    ]
   },
   {