Light IH support focusing on Conversion monitoring

examples/ih/Conversion_Reporting.ipynb

@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pdstools import IH\n",
+    "\n",
+    "import plotly.io as pio\n",
+    "import plotly as plotly\n",
+    "\n",
+    "plotly.offline.init_notebook_mode()\n",
+    "pio.renderers.default = \"vscode\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conversion Results\n",
+    "\n",
+    "Visualization of conversion modeling results from IH data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import polars as pl\n",
+    "\n",
+    "ih_export_file = Path(\n",
+    "    \"./Data-pxStrategyResult_InteractionFiles_20241213T091932_GMT.zip\"\n",
+    ")\n",
+    "\n",
+    "if not ih_export_file.exists():\n",
+    "    ih = IH.from_mock_data()\n",
+    "else:\n",
+    "    ih = IH.from_ds_export(\n",
+    "        ih_export_file,\n",
+    "        query=pl.col.ExperimentGroup.is_not_null() & (pl.col.ExperimentGroup != \"\"),\n",
+    "    )\n",
+    "\n",
+    "ih.aggregates.summary_success_rates(by=[\"ExperimentGroup\", \"Channel\"]).drop(\n",
+    "    \"Outcomes\"\n",
+    ").collect().to_pandas().style.hide()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ih.plots.conversion_overall_gauges(\n",
+    "    experiment_field=\"ExperimentGroup\",\n",
+    "    by=\"Channel\",\n",
+    "    reference_values={\"Web\": 0.055, \"Email\": 0.09},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Detailed View \n",
+    "\n",
+    "Showing conversion rates for all actions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ih.plots.conversion_success_rates_tree_map()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conversion Rate Trends\n",
+    "\n",
+    "side-by-side bars and lines (separate methods) with error bars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ih.plots.conversion_success_rates_trend_bar(\n",
+    "    experiment_field=\"ExperimentGroup\",\n",
+    "    every=\"1w\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Engagement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ih.plots.egagement_overall_gauges(\n",
+    "    experiment_field=\"ExperimentGroup\",\n",
+    "    by=\"Channel\",\n",
+    "    reference_values={\"Web\": 0.20, \"Email\": 0.20},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ih.plots.conversion_success_rates_trend_line(\n",
+    "    by=\"Channel\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/ih/Example_IH_Analysis.ipynb

@@ -4,7 +4,19 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'cdhtools'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcdhtools\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mIHanalysis\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcdhtools\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcdh_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m readDSExport\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cdhtools'"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import sys\n",
@@ -18,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -35,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -274,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -548,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -578,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -607,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -638,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -668,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -699,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -728,7 +740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -780,7 +792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -809,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -826,7 +838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -856,7 +868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -886,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -895,7 +907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -932,7 +944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -968,11 +980,9 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "0c5c31b7614ab5f7bbff6555bdc6f3ec4cea8754d51936ee45052251e94c1071"
-  },
   "kernelspec": {
-   "display_name": "Python 3.9.4 64-bit ('newvfenv': conda)",
+   "display_name": ".venv",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
@@ -985,7 +995,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

python/pdstools/__init__.py

@@ -7,6 +7,7 @@
 from polars import enable_string_cache
 
 from .adm.ADMDatamart import ADMDatamart
+from .ih.IH import IH
 from .infinity import Infinity
 from .pega_io import Anonymization, read_ds_export
 from .prediction.Prediction import Prediction
@@ -23,6 +24,7 @@
 
 __all__ = [
     "ADMDatamart",
+    "IH",
     "Anonymization",
     "read_ds_export",
     "Prediction",