improve filtering of datasets in benchmarks

Author: paulbkoch

docs/benchmarks/ebm-benchmark.ipynb

@@ -913,7 +913,8 @@
     "\n",
     "# Optionally filter out results we want to replace\n",
     "#results_df = results_df[results_df['method'] != 'ebm']\n",
-    "#results_df = results_df[~((results_df['method'] == 'ebm') & (results_df['meta'] == '{}'))]\n",
+    "#results_df = results_df[(results_df['method'] != 'ebm') | (results_df['meta'] != '{}')]\n",
+    "#results_df = results_df[(results_df['method'] != 'ebm') | (results_df['meta'] != '{\"interactions\": 0}')]\n",
     "print(f'Results (post-filtered) count: {results_df.shape[0]}')"
    ]
   },
@@ -925,21 +926,26 @@
    "outputs": [],
    "source": [
     "# Fill in results from previous runs if desired.\n",
-    "filler_df = pd.DataFrame(columns=results_df.columns)\n",
-    "#filler_df = pd.read_csv(\"prev.csv\")\n",
-    "\n",
-    "# Optionally filter out results from the filter\n",
-    "#filler_df = filler_df[filler_df['meta'] == \"{'interactions': 0\"]\n",
-    "\n",
-    "key_columns = ['task', 'method', 'meta', 'replicate_num', 'name', 'seq_num']\n",
-    "filler_df = filler_df[~filler_df.set_index(key_columns).index.isin(results_df.set_index(key_columns).index)]\n",
-    "if 0 < filler_df.shape[0]:\n",
-    "    results_df = pd.concat([results_df, filler_df], ignore_index=True)\n",
-    "    results_df = results_df.sort_values(by=[\"task\", \"method\", \"meta\", \"replicate_num\", \"name\", \"seq_num\"])\n",
-    "    results_df.to_csv(\"merged.csv\", index=None)\n",
-    "print(f'Filter count: {filler_df.shape[0]}')\n",
-    "print(f'Results count: {results_df.shape[0]}')\n",
-    "#print(filler_df.to_string())"
+    "basefile = 'base.csv'\n",
+    "import os\n",
+    "if os.path.exists(basefile):\n",
+    "    filler_df = pd.DataFrame(columns=results_df.columns)\n",
+    "    filler_df = pd.read_csv(basefile)\n",
+    "    \n",
+    "    # Optionally filter out results from the filter\n",
+    "    filler_df = filler_df[filler_df['method'] != 'ebm']\n",
+    "    #filler_df = filler_df[(filler_df['method'] != 'ebm') | (filler_df['meta'] != '{}')]\n",
+    "    #filler_df = filler_df[(filler_df['method'] != 'ebm') | (filler_df['meta'] != '{\"interactions\": 0}')]\n",
+    "    \n",
+    "    key_columns = ['task', 'method', 'meta', 'replicate_num', 'name', 'seq_num']\n",
+    "    filler_df = filler_df[~filler_df.set_index(key_columns).index.isin(results_df.set_index(key_columns).index)]\n",
+    "    if 0 < filler_df.shape[0]:\n",
+    "        results_df = pd.concat([results_df, filler_df], ignore_index=True)\n",
+    "        results_df = results_df.sort_values(by=[\"task\", \"method\", \"meta\", \"replicate_num\", \"name\", \"seq_num\"])\n",
+    "        results_df.to_csv(\"merged.csv\", index=None)\n",
+    "    print(f'Filter count: {filler_df.shape[0]}')\n",
+    "    print(f'Results count: {results_df.shape[0]}')\n",
+    "    #print(filler_df.to_string())"
    ]
   },
   {
@@ -960,7 +966,13 @@
     "\n",
     "# Optionally filter out any incomplete datasets\n",
     "#results_df = results_df[results_df['task'] != 'Devnagari-Script']\n",
-    "#results_df = results_df[results_df['type'] == 'regression']\n",
+    "#results_df = results_df[results_df['task'] != 'CIFAR_10']\n",
+    "#results_df = results_df[results_df['task'] != 'isolet']\n",
+    "#results_df = results_df[results_df['task'] != 'mnist_784']\n",
+    "#results_df = results_df[results_df['task'] != 'Airlines_DepDelay_10M']\n",
+    "#results_df = results_df[results_df['type'] != 'binary']\n",
+    "#results_df = results_df[results_df['type'] != 'multiclass']\n",
+    "#results_df = results_df[results_df['type'] != 'regression']\n",
     "print(f'Final count: {results_df.shape[0]}')"
    ]
   },