R(Cluster)

5 files changed, 38 insertions, 10 deletions

diff --git a/Fundamentals_of_Accelerated_Data_Science/1-08_cudf-polars.ipynb b/Fundamentals_of_Accelerated_Data_Science/1-08_cudf-polars.ipynb
index c0f1115..6f810c8 100644
--- a/Fundamentals_of_Accelerated_Data_Science/1-08_cudf-polars.ipynb
+++ b/Fundamentals_of_Accelerated_Data_Science/1-08_cudf-polars.ipynb
@@ -803,7 +803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "6f5883f3-6238-4f98-970b-f06adabfb50e",
    "metadata": {},
    "outputs": [
@@ -846,7 +846,10 @@
    ],
    "source": [
     "# Show optimized Graph\n",
-    "lazy_result.show_graph(optimized=True)"
+    "lazy_result.show_graph(optimized=True)\n",
+    "\n",
+    "# pi - projection compute and keep these columns\n",
+    "# sigme - selection "
    ]
   },
   {
diff --git a/Fundamentals_of_Accelerated_Data_Science/2-04_networkx_cugraph.ipynb b/Fundamentals_of_Accelerated_Data_Science/2-04_networkx_cugraph.ipynb
index 48764b4..e6f3382 100644
--- a/Fundamentals_of_Accelerated_Data_Science/2-04_networkx_cugraph.ipynb
+++ b/Fundamentals_of_Accelerated_Data_Science/2-04_networkx_cugraph.ipynb
@@ -62,6 +62,19 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7375dbf9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://networkx.org/documentation/stable/reference/configs.html\n",
+    "\n",
+    "# nx.config.backend_priority = [\"cugraph\", \"..\"]\n",
+    "# env NETWORKX_BACKEND_PRIORITY=\"cugraph,..\""
+   ]
+  },
+  {
    "cell_type": "markdown",
    "id": "697ea4c9-b416-43d5-9d2c-28aa41ef2561",
    "metadata": {},
@@ -291,12 +304,14 @@
     "### Pagerank Centrality ###\n",
     "Determines a node's importance based on the quantity and quality of links to it, similar to Google's original PageRank algorithm\n",
     "\n",
-    "PageRank’s main difference from EigenCentrality is that it accounts for link direction. Each node in a network is assigned a score based on its number of incoming links (its ‘indegree’). These links are also weighted depending on the relative score of its originating node."
+    "PageRank’s main difference from EigenCentrality is that it accounts for link direction. Each node in a network is assigned a score based on its number of incoming links (its ‘indegree’). These links are also weighted depending on the relative score of its originating node.\n",
+    "\n",
+    "1/n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "a17ee15b-8758-484b-82b9-a158187231c5",
    "metadata": {},
    "outputs": [],
diff --git a/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb
index 3890ea7..4e4b2ab 100644
--- a/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb
+++ b/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb
@@ -205,7 +205,9 @@
     "## Logistic Regression ##\n",
     "Logistic regression can be used to estimate the probability of an outcome as a function of some (assumed independent) inputs. In our case, we would like to estimate infection risk based on population members' age and sex.\n",
     "\n",
-    "Below we train a logistic regresion model. We first create a cuML logistic regression instance `logreg`. The `logreg.fit` method takes 2 arguments: the model's independent variables *X*, and the dependent variable *y*. Fit the `logreg` model using the `gdf` columns `age` and `sex` as *X* and the `infected` column as *y*."
+    "Below we train a logistic regresion model. We first create a cuML logistic regression instance `logreg`. The `logreg.fit` method takes 2 arguments: the model's independent variables *X*, and the dependent variable *y*. Fit the `logreg` model using the `gdf` columns `age` and `sex` as *X* and the `infected` column as *y*.\n",
+    "\n",
+    "1/(1+e^{-z}) sigmoid"
    ]
   },
   {
@@ -637,6 +639,13 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
diff --git a/Fundamentals_of_Accelerated_Data_Science/3-06_xgboost.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-06_xgboost.ipynb
index 654a782..d3548f2 100644
--- a/Fundamentals_of_Accelerated_Data_Science/3-06_xgboost.ipynb
+++ b/Fundamentals_of_Accelerated_Data_Science/3-06_xgboost.ipynb
@@ -443,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,8 +453,6 @@
     "    'device': 'cuda',\n",
     "    'tree_method':  'hist',\n",
     "    'objective':    'binary:logistic',\n",
-    "    'grow_policy':  'lossguide',\n",
-    "    'eval_metric':  'logloss',\n",
     "    'subsample':    '0.8'\n",
     "}"
    ]
diff --git a/Fundamentals_of_Accelerated_Data_Science/3-07_triton.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-07_triton.ipynb
index 757b3fa..47d586c 100644
--- a/Fundamentals_of_Accelerated_Data_Science/3-07_triton.ipynb
+++ b/Fundamentals_of_Accelerated_Data_Science/3-07_triton.ipynb
@@ -120,11 +120,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "61d898fb-a8d2-4d1c-a13f-2c4be6c18969",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# RAPIDS Forest Inference Library\n",
+    "# FIL returns class labels or probabilities\n",
+    "# mem / disk\n",
     "config_text = f\"\"\"backend: \"fil\"\n",
     "max_batch_size: 32768\n",
     "input [                                 \n",
@@ -148,7 +151,7 @@
     "    value: {{ string_value: \"xgboost_json\" }}\n",
     "  }},\n",
     "  {{\n",
-    "    key: \"output_class\"\n",
+    "    key: \"output_class\" \n",
     "    value: {{ string_value: \"false\" }}\n",
     "  }},\n",
     "  {{\n",