refactor:Using "DSL::Examples" for making the parsing function.

Author: antononcube

Presentations/DSSG-SouthFL-2025/Translations-by-LLM-examples-Raku.ipynb

@@ -2,65 +2,22 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 13,
    "metadata": {
     "jupyter": {
      "is_executing": true
     }
    },
    "outputs": [],
    "source": [
-    "use LLM::Configurations;"
+    "use LLM::Configurations;\n",
+    "use DSL::Examples;\n",
+    "use DSL::English::QuantileRegressionWorkflows;"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-> **@args, *%args { #`(Block|5495820722832) ... }"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my &fLLMForQRMon = llm-example-function([\n",
-    "    'use tsTemp' => 'Regressionizer(temp_data)',\n",
-    "    'with the data tsTemp' => 'Regressionizer(temp_data)',\n",
-    "    'do regression with 12 knots' => 'quantile_regression(knots=12)',\n",
-    "    'quantile regression with 30 knots and interpolation order 3' => 'quantile_regression(knots=12,order=3)',\n",
-    "    'compute the regression quantiles for probabilities 0.1, 0.5, and 0.9' => 'quantile_regression(knots=12, probs=[0.1, 0.5, 0.9])',\n",
-    "    'compute linear regression' => 'least_squares_fit(funcs=6)',\n",
-    "    'compute linear regression with 14 basis functions' => 'least_squares_fit(funcs=14)',\n",
-    "    'do a least squares fit with the basis functions MyFuncs' => 'least_squares_fit(funcs=MyFuncs)',\n",
-    "    'show date list plot' => 'date_list_plot()',\n",
-    "    'plot the data and fits' => 'plot()',\n",
-    "    'plot with width 800 and aspect ratio 1/3' => 'plot(width=800, height=800*1/3)',\n",
-    "    'plot with image size 1000 and plot theme plotly_dark' => 'plot(width=1000, template=\\\"plotly_dark\\\")',\n",
-    "    'show error plots' => 'error_plots()',\n",
-    "    'show date list error plots' => 'error_plots(date_list_plot=True)',\n",
-    "    'show relative error plots' => 'error_plots(relative_errors=True)',\n",
-    "    'echo data summary' => 'echo_data_summary()',\n",
-    "    'rescale the data' => 'rescale(regressor=False, value=True)',\n",
-    "    'rescale the data in both dimensions' => 'rescale(regressor=True, value=True)',\n",
-    "    'find outliers' => 'outliers()',\n",
-    "    'plot outliers' => 'outliers_plot()',\n",
-    "    'take the value' => 'take_value()',\n",
-    "    'show the value' => 'take_value()'\n",
-    "],\n",
-    "e => $conf4o\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -69,31 +26,36 @@
        "&LLMQRMon"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "use LLM::Functions;\n",
+    "my &llm-pipeline-segment = llm-example-function(dsl-examples()<Python><QRMon>, e => $conf4o);\n",
+    "\n",
     "sub LLMQRMon(Str $s) {\n",
-    "    my $res = &fLLMForQRMon($s);\n",
+    "    my $res = &llm-pipeline-segment($s);\n",
     "    $res .= trim;\n",
     "    return $res;\n",
     "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Regressionizer(temp_data) echo_data_summary() rescale(regressor=True, value=False) quantile_regression(knots=20, probs=[0.025, 0.5, 0.976]) least_squares_fit(funcs=34) date_list_plot(width=900, height=900*1/3) outliers_plot(date_list_plot=True, width=900, height=900*1/3)]"
+       "[Regressionizer(temp_data) echo_data_summary() rescale(regressor=True, value=False) quantile_regression(knots=20, probs=[0.025, 0.5, 0.976]) least_squares_fit(funcs=34) date_list_plot(width=900, height=900*1/3) ```python\n",
+       "date_list_plot(outliers=True, width=900, height=900*1/3)\n",
+       "```]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -111,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -123,10 +85,12 @@
        ".quantile_regression(knots=20, probs=[0.025, 0.5, 0.976])\n",
        ".least_squares_fit(funcs=34)\n",
        ".date_list_plot(width=900, height=900*1/3)\n",
-       ".outliers_plot(date_list_plot=True, width=900, height=900*1/3)"
+       ".```python\n",
+       "date_list_plot(outliers=True, width=900, height=900*1/3)\n",
+       "```"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -135,12 +99,48 @@
     "@qrPythonCode.join(\"\\n.\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare with the \"Small Language Model\" (SML) results:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Possible misspelling of 'summary' as 'summari'.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "obj = Regressionizer(temp_data).echo_data_summary().rescale( regressor = True).quantile_regression( knots = 20, probs = [0.025, 0.5, 0.976] ).plot( date_plot = True).plot( datePlotQ = false)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my $spec = q:to/END/;\n",
+    "use dataset temp_data\n",
+    "echo data summari;\n",
+    "rescale the x axis;\n",
+    "do quantile regression with 20 knots, with probabilities 0.025, 0.5, and 0.976;\n",
+    "show date list plot;\n",
+    "show outliers plot;\n",
+    "END\n",
+    "\n",
+    "ToQuantileRegressionWorkflowCode($spec, target => 'Python::Regressionizer')"
+   ]
   }
  ],
  "metadata": {