Upgrade Tablesaw example to use Smile 2.0 (#8217)

twosigma · Feb 7, 2020 · 5fef5e9 · 5fef5e9
1 parent 5551792
commit 5fef5e9
Showing 1 changed file with 8 additions and 7 deletions.
diff --git a/doc/groovy/Tablesaw.ipynb b/doc/groovy/Tablesaw.ipynb
@@ -18,9 +18,9 @@
    "outputs": [],
    "source": [
     "%%classpath add mvn\n",
-    "tech.tablesaw tablesaw-beakerx 0.35.0\n",
-    "com.jimmoores quandl-tablesaw 2.0.0\n",
-    "com.github.haifengl smile-core 1.5.2"
+    "tech.tablesaw tablesaw-beakerx 0.37.0\n",
+    "com.jimmoores quandl-tablesaw 2.1.0\n",
+    "com.github.haifengl smile-core 2.0.0"
    ]
   },
   {
@@ -33,6 +33,7 @@
     "%import tech.tablesaw.api.*\n",
     "%import tech.tablesaw.columns.*\n",
     "%import smile.clustering.*\n",
+    "%import smile.data.formula.Formula\n",
     "%import smile.regression.*\n",
     "\n",
     "// display Tablesaw tables with BeakerX table display widget\n",
@@ -188,7 +189,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kMeans = new KMeans(whiskeyData.as().doubleMatrix(\"Body\", \"Sweetness\", \"Smoky\", \"Medicinal\", \"Tobacco\", \"Honey\", \"Spicy\", \"Winey\", \"Nutty\", \"Malty\", \"Fruity\", \"Floral\"), 5)"
+    "kMeans = KMeans.fit(whiskeyData.as().doubleMatrix(\"Body\", \"Sweetness\", \"Smoky\", \"Medicinal\", \"Tobacco\", \"Honey\", \"Spicy\", \"Winey\", \"Nutty\", \"Malty\", \"Fruity\", \"Floral\"), 5)"
    ]
   },
   {
@@ -197,7 +198,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Table whiskeyClusters = Table.create(\"Clusters\", whiskeyData.stringColumn(\"Distillery\"), DoubleColumn.create(\"Cluster\", kMeans.getClusterLabel()));\n",
+    "Table whiskeyClusters = Table.create(\"Clusters\", whiskeyData.stringColumn(\"Distillery\"), DoubleColumn.create(\"Cluster\", kMeans.y));\n",
     "whiskeyClusters = whiskeyClusters.sortAscendingOn(\"Cluster\", \"Distillery\");"
    ]
   },
@@ -236,7 +237,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "winsModel = new OLS(moneyball.select(\"W\", \"RD\").smile().numericDataset(\"RD\"));"
+    "winsModel = OLS.fit(Formula.lhs(\"RD\"), moneyball.select(\"W\", \"RD\").smile().toDataFrame());"
    ]
   },
   {
@@ -245,7 +246,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "runsScored = new OLS(moneyball.select(\"OBP\", \"SLG\", \"RS\").smile().numericDataset(\"RS\"));"
+    "runsScored = OLS.fit(Formula.lhs(\"RS\"), moneyball.select(\"OBP\", \"SLG\", \"RS\").smile().toDataFrame());"
    ]
   },
   {