ml-insights/sample_notebooks/2_Run_ML_Insights_using_config_file.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "5bf7c8b1",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Run ML Insights Using Config File"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d377a077",
   "metadata": {},
   "source": [
    "# Use Case\n",
    "\n",
    "This Notebook shows the ML Insights Low code option to define and customise all of its core features like data scheme , data ingestion, data transformation, metric calculation and post processing of metric output using json based configuration .\n",
    "\n",
    "### About Dataset\n",
    "The Iris flower data set or Fisher's Iris data set is a multivariate data set . The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.\n",
    "\n",
    "Dataset source : https://archive.ics.uci.edu/dataset/53/iris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "679a0258",
   "metadata": {},
   "source": [
    "# Install ML Observability Insights Library SDK\n",
    "\n",
    "- Prerequisites\n",
    "    - Linux/Mac (Intel CPU)\n",
    "    - Python 3.8 and 3.9 only\n",
    "    \n",
    "    \n",
    "- Installation \n",
    "    - ML Insights is made available as a Python package (via Artifactory) which can be installed using pip install as shown below. Depending on the execution engine on which to do the run, one can use scoped package. For eg: if we want to run on dask, use oracle-ml-insights[dask], for spark use oracle-ml-insights[spark], for native use oracle-ml-insights. One can install all the dependencies as use oracle-ml-insights[all]\n",
    "    \n",
    "      !pip install oracle-ml-insights\n",
    "\n",
    "\n",
    "Refer : [Installation and Setup](https://docs.oracle.com/en-us/iaas/tools/ml-insights-docs/latest/ml-insights-documentation/html/user_guide/tutorials/install.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b3fd12",
   "metadata": {},
   "source": [
    "This example notebook showcases how to use Insights config reader to run the evaluation based on monitor config.  Sample monitor config JSON and sample data are available under `monitor_configs/monitor_config.json` and `input_data/iris-dataset` respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "392e6853",
   "metadata": {},
   "outputs": [],
   "source": [
    "!python3 -m pip install oracle-ml-insights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741580e9",
   "metadata": {},
   "source": [
    "# 1 ML Insights Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f8349eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from mlm_insights.builder.insights_builder import InsightsBuilder\n",
    "from mlm_insights.config_reader.insights_config_reader import InsightsConfigReader\n",
    "\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd095db",
   "metadata": {},
   "source": [
    "# 2 What is Json based Configuration \n",
    "\n",
    "ML Insights provides low code option to define and customise all of its core features like data scheme , data ingestion, data transformation, metric calculation and post processing of metric output using json based configuration .\n",
    "\n",
    "Refer : [ML Insights Config File Documentation](https://docs.oracle.com/en-us/iaas/tools/ml-insights-docs/latest/ml-insights-documentation/html/user_guide/tutorials/config_reader.html)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eaab2a9",
   "metadata": {},
   "source": [
    "# 3 Compute the profile \n",
    "\n",
    "Initialize the Insights builder by specifying the location of the monitor config JSON using the Insights config reader.\n",
    "\n",
    "The run() method is responsible to run the internal workflow. It also handles the life cycle of each component passed, which includes creation (if required), invoking interface functions, destroying etc . Additionally, runner also handles some more advanced operations like thread pooling, compute engine abstraction etc.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d953610",
   "metadata": {},
   "outputs": [],
   "source": [
    "insights_builder = InsightsConfigReader(config_location=\"monitor_configs/monitor_config.json\").get_builder()\n",
    "run_result = insights_builder.build().run()\n",
    "profile = run_result.profile\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0fa648",
   "metadata": {},
   "source": [
    "# 4 Result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621cf4c2",
   "metadata": {},
   "source": [
    "## 4.1 Visualize the Profile in tabular format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f2dd5d1b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Skewness</th>\n",
       "      <th>StandardDeviation</th>\n",
       "      <th>Min</th>\n",
       "      <th>IsConstantFeature</th>\n",
       "      <th>IQR</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Range</th>\n",
       "      <th>TypeMetric.string_type_count</th>\n",
       "      <th>TypeMetric.integral_type_count</th>\n",
       "      <th>TypeMetric.fractional_type_count</th>\n",
       "      <th>...</th>\n",
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       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>0.311753</td>\n",
       "      <td>0.825301</td>\n",
       "      <td>4.3</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>5.8</td>\n",
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       "      <td>5.843333</td>\n",
       "      <td>-0.573568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>0.315767</td>\n",
       "      <td>0.434411</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>...</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>3.057333</td>\n",
       "      <td>0.180976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>-0.272128</td>\n",
       "      <td>1.759404</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>6.9</td>\n",
       "      <td>43</td>\n",
       "      <td>2254.8</td>\n",
       "      <td>False</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>-1.395536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>-0.101934</td>\n",
       "      <td>0.759693</td>\n",
       "      <td>0.1</td>\n",
       "      <td>False</td>\n",
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       "                   Skewness  StandardDeviation  Min  IsConstantFeature  IQR  \\\n",
       "sepal length (cm)  0.311753           0.825301  4.3              False  1.3   \n",
       "sepal width (cm)   0.315767           0.434411  2.0              False  0.5   \n",
       "petal length (cm) -0.272128           1.759404  1.0              False  3.5   \n",
       "petal width (cm)  -0.101934           0.759693  0.1              False  1.5   \n",
       "\n",
       "                         Mode  Range  TypeMetric.string_type_count  \\\n",
       "sepal length (cm)       [5.0]    3.6                             0   \n",
       "sepal width (cm)        [3.0]    2.4                             0   \n",
       "petal length (cm)  [1.4, 1.5]    5.9                             0   \n",
       "petal width (cm)        [0.2]    2.4                             0   \n",
       "\n",
       "                   TypeMetric.integral_type_count  \\\n",
       "sepal length (cm)                               0   \n",
       "sepal width (cm)                                0   \n",
       "petal length (cm)                               0   \n",
       "petal width (cm)                                0   \n",
       "\n",
       "                   TypeMetric.fractional_type_count  ...  \\\n",
       "sepal length (cm)                               600  ...   \n",
       "sepal width (cm)                                600  ...   \n",
       "petal length (cm)                               600  ...   \n",
       "petal width (cm)                                600  ...   \n",
       "\n",
       "                   Count.missing_count_percentage  Max  DistinctCount     Sum  \\\n",
       "sepal length (cm)                             0.0  7.9             35  3506.0   \n",
       "sepal width (cm)                              0.0  4.4             23  1834.4   \n",
       "petal length (cm)                             0.0  6.9             43  2254.8   \n",
       "petal width (cm)                              0.0  2.5             22   719.6   \n",
       "\n",
       "                   IsQuasiConstantFeature  Quartiles.q1  Quartiles.q2  \\\n",
       "sepal length (cm)                   False           5.1           5.8   \n",
       "sepal width (cm)                    False           2.8           3.0   \n",
       "petal length (cm)                   False           1.6           4.3   \n",
       "petal width (cm)                    False           0.3           1.3   \n",
       "\n",
       "                   Quartiles.q3      Mean  Kurtosis  \n",
       "sepal length (cm)           6.4  5.843333 -0.573568  \n",
       "sepal width (cm)            3.3  3.057333  0.180976  \n",
       "petal length (cm)           5.1  3.758000 -1.395536  \n",
       "petal width (cm)            1.8  1.199333 -1.336067  \n",
       "\n",
       "[4 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73df6b5",
   "metadata": {},
   "source": [
    "## 4.2 Visualize the Profile in JSON format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5ede511",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"dataset_metrics\": {},\n",
      "    \"feature_metrics\": {\n",
      "        \"petal length (cm)\": {\n",
      "            \"Count\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    600.0,\n",
      "                    0.0,\n",
      "                    0.0\n",
      "                ],\n",
      "                \"metric_description\": \"Feature metric that returns total count, missing count and missing count percentage\",\n",
      "                \"metric_name\": \"Count\",\n",
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      "                    0,\n",
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      "            \"DistinctCount\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    43\n",
      "                ],\n",
      "                \"metric_description\": \"Approximate Distinct Count\",\n",
      "                \"metric_name\": \"DistinctCount\",\n",
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      "                \"variable_dimensions\": [\n",
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      "                    557,\n",
      "                    92.83333333333333\n",
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      "            },\n",
      "            \"IQR\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    3.4999999999999996\n",
      "                ],\n",
      "                \"metric_description\": \"Compute the interquartile range of the data. Interquartile range (IQR) is the difference between the 75th and 25th percentile of the data. It is a measure of the dispersion similar to standard deviation or variance, but is much more robust against outliers\",\n",
      "                \"metric_name\": \"IQR\",\n",
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      "            \"IsConstantFeature\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    false\n",
      "                ],\n",
      "                \"metric_description\": \"Feature Metric to compute if all values are same\",\n",
      "                \"metric_name\": \"IsConstantFeature\",\n",
      "                \"variable_count\": 1,\n",
      "                \"variable_dimensions\": [\n",
      "                    0\n",
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      "                \"variable_dtypes\": [\n",
      "                    \"BOOLEAN\"\n",
      "                ],\n",
      "                \"variable_names\": [\n",
      "                    \"is_constant\"\n",
      "                ],\n",
      "                \"variable_types\": [\n",
      "                    \"BINARY\"\n",
      "                ]\n",
      "            },\n",
      "            \"IsQuasiConstantFeature\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    false\n",
      "                ],\n",
      "                \"metric_description\": \"Feature Metric to compute if all values are almost same\",\n",
      "                \"metric_name\": \"IsQuasiConstantFeature\",\n",
      "                \"variable_count\": 1,\n",
      "                \"variable_dimensions\": [\n",
      "                    0\n",
      "                ],\n",
      "                \"variable_dtypes\": [\n",
      "                    \"BOOLEAN\"\n",
      "                ],\n",
      "                \"variable_names\": [\n",
      "                    \"is_quasi_constant\"\n",
      "                ],\n",
      "                \"variable_types\": [\n",
      "                    \"BINARY\"\n",
      "                ]\n",
      "            },\n",
      "            \"Kurtosis\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    -1.3955358863990055\n",
      "                ],\n",
      "                \"metric_description\": \"Feature Metric to compute Kurtosis\",\n",
      "                \"metric_name\": \"Kurtosis\",\n",
      "                \"variable_count\": 1,\n",
      "                \"variable_dimensions\": [\n",
      "                    0\n",
      "                ],\n",
      "                \"variable_dtypes\": [\n",
      "                    \"FLOAT\"\n",
      "                ],\n",
      "                \"variable_names\": [\n",
      "                    \"kurtosis\"\n",
      "                ],\n",
      "                \"variable_types\": [\n",
      "                    \"CONTINUOUS\"\n",
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      "            },\n",
      "            \"Max\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    6.9\n",
      "                ],\n",
      "                \"metric_description\": \"Feature Metric to compute maximum value\",\n",
      "                \"metric_name\": \"Max\",\n",
      "                \"variable_count\": 1,\n",
      "                \"variable_dimensions\": [\n",
      "                    0\n",
      "                ],\n",
      "                \"variable_dtypes\": [\n",
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      "                ],\n",
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      "            },\n",
      "            \"Mean\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    3.7580000000000005\n",
      "                ],\n",
      "                \"metric_description\": \"Feature Metric to compute mean\",\n",
      "                \"metric_name\": \"Mean\",\n",
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      "            \"Min\": {\n",
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      "                \"metric_data\": [\n",
      "                    1.0\n",
      "                ],\n",
      "                \"metric_description\": \"Feature Metric to compute minimum value\",\n",
      "                \"metric_name\": \"Min\",\n",
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      "                \"variable_dimensions\": [\n",
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      "            },\n",
      "            \"Mode\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    [\n",
      "                        \"1.4\",\n",
      "                        \"1.5\"\n",
      "                    ]\n",
      "                ],\n",
      "                \"metric_description\": \"Mode\",\n",
      "                \"metric_name\": \"Mode\",\n",
      "                \"variable_count\": 1,\n",
      "                \"variable_dimensions\": [\n",
      "                    1\n",
      "                ],\n",
      "                \"variable_dtypes\": [\n",
      "                    \"STRING\"\n",
      "                ],\n",
      "                \"variable_names\": [\n",
      "                    \"mode\"\n",
      "                ],\n",
      "                \"variable_types\": [\n",
      "                    \"NOMINAL\"\n",
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      "                \"variable_dimensions\": [\n",
      "                    0\n",
      "                ],\n",
      "                \"variable_dtypes\": [\n",
      "                    \"FLOAT\"\n",
      "                ],\n",
      "                \"variable_names\": [\n",
      "                    \"standard_deviation\"\n",
      "                ],\n",
      "                \"variable_types\": [\n",
      "                    \"CONTINUOUS\"\n",
      "                ]\n",
      "            },\n",
      "            \"Sum\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    1834.4\n",
      "                ],\n",
      "                \"metric_description\": \"\",\n",
      "                \"metric_name\": \"Sum\",\n",
      "                \"variable_count\": 1,\n",
      "                \"variable_dimensions\": [\n",
      "                    0\n",
      "                ],\n",
      "                \"variable_dtypes\": [\n",
      "                    \"FLOAT\"\n",
      "                ],\n",
      "                \"variable_names\": [\n",
      "                    \"sum\"\n",
      "                ],\n",
      "                \"variable_types\": [\n",
      "                    \"CONTINUOUS\"\n",
      "                ]\n",
      "            },\n",
      "            \"TypeMetric\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    0,\n",
      "                    0,\n",
      "                    600,\n",
      "                    0\n",
      "                ],\n",
      "                \"metric_description\": \"Feature Metric to compute count of data types for feature values\",\n",
      "                \"metric_name\": \"TypeMetric\",\n",
      "                \"variable_count\": 4,\n",
      "                \"variable_dimensions\": [\n",
      "                    0,\n",
      "                    0,\n",
      "                    0,\n",
      "                    0\n",
      "                ],\n",
      "                \"variable_dtypes\": [\n",
      "                    \"INTEGER\",\n",
      "                    \"INTEGER\",\n",
      "                    \"INTEGER\",\n",
      "                    \"INTEGER\"\n",
      "                ],\n",
      "                \"variable_names\": [\n",
      "                    \"string_type_count\",\n",
      "                    \"integral_type_count\",\n",
      "                    \"fractional_type_count\",\n",
      "                    \"boolean_type_count\"\n",
      "                ],\n",
      "                \"variable_types\": [\n",
      "                    \"DISCRETE\",\n",
      "                    \"DISCRETE\",\n",
      "                    \"DISCRETE\",\n",
      "                    \"DISCRETE\"\n",
      "                ]\n",
      "            },\n",
      "            \"Variance\": {\n",
      "                \"metadata\": {},\n",
      "                \"metric_data\": [\n",
      "                    0.1887128888888889\n",
      "                ],\n",
      "                \"metric_description\": \"Feature Metric to compute Variance\",\n",
      "                \"metric_name\": \"Variance\",\n",
      "                \"variable_count\": 1,\n",
      "                \"variable_dimensions\": [\n",
      "                    0\n",
      "                ],\n",
      "                \"variable_dtypes\": [\n",
      "                    \"FLOAT\"\n",
      "                ],\n",
      "                \"variable_names\": [\n",
      "                    \"variance\"\n",
      "                ],\n",
      "                \"variable_types\": [\n",
      "                    \"CONTINUOUS\"\n",
      "                ]\n",
      "            }\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "profile_json = profile.to_json()\n",
    "dataset_metrics = profile_json\n",
    "print(json.dumps(dataset_metrics,sort_keys=True, indent=4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "21ed6bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>feature_metrics.sepal length (cm).Skewness.metric_name</th>\n",
       "      <td>Skewness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_metrics.sepal length (cm).Skewness.metric_description</th>\n",
       "      <td>Feature Metric to compute Skewness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_metrics.sepal length (cm).Skewness.variable_count</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_metrics.sepal length (cm).Skewness.variable_names</th>\n",
       "      <td>[skewness]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_metrics.sepal length (cm).Skewness.variable_types</th>\n",
       "      <td>[CONTINUOUS]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_metrics.petal width (cm).Kurtosis.variable_names</th>\n",
       "      <td>[kurtosis]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_metrics.petal width (cm).Kurtosis.variable_types</th>\n",
       "      <td>[CONTINUOUS]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_metrics.petal width (cm).Kurtosis.variable_dtypes</th>\n",
       "      <td>[FLOAT]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_metrics.petal width (cm).Kurtosis.variable_dimensions</th>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_metrics.petal width (cm).Kurtosis.metric_data</th>\n",
       "      <td>[-1.3360674052315529]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>576 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                     0\n",
       "feature_metrics.sepal length (cm).Skewness.metr...                            Skewness\n",
       "feature_metrics.sepal length (cm).Skewness.metr...  Feature Metric to compute Skewness\n",
       "feature_metrics.sepal length (cm).Skewness.vari...                                   1\n",
       "feature_metrics.sepal length (cm).Skewness.vari...                          [skewness]\n",
       "feature_metrics.sepal length (cm).Skewness.vari...                        [CONTINUOUS]\n",
       "...                                                                                ...\n",
       "feature_metrics.petal width (cm).Kurtosis.varia...                          [kurtosis]\n",
       "feature_metrics.petal width (cm).Kurtosis.varia...                        [CONTINUOUS]\n",
       "feature_metrics.petal width (cm).Kurtosis.varia...                             [FLOAT]\n",
       "feature_metrics.petal width (cm).Kurtosis.varia...                                 [0]\n",
       "feature_metrics.petal width (cm).Kurtosis.metri...               [-1.3360674052315529]\n",
       "\n",
       "[576 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.json_normalize(dataset_metrics).T.dropna()"
   ]
  }
 ],
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