Repository: aehrc/VariantSpark (language: python)

Issue:
Make the covariate calculation part of RF
Implement covariates in vs hail interface function `varspark.hail.random_forest_model` with the interface analogous to hail's   `logistic_regression_rows()` methos (see: https://hail.is/docs/0.2/methods/stats.html#hail.methods.logistic_regression_rows)

Currently `random_forest_model` takes the `covariates` argument but it is ignored. 

The argument should be a list of expression of type float64 (same as logistic_regression_rows) and these expressions should be evaluated and included as continuous variables for random forest model building along the ordered factor genotypic variables.

Because we want to keep the variant importance output table as is (that is indexed by locus and list of alleles) we cannot include the importances of covariates in it. Instead we should a method `covariates_importance()` on `RandomForestModel` that returns a table with the following schema:

-  'covariate': str  -  the name of the covariate (as in the input expression)
-  'importance':float64 -  the importance
-  'splitCount':int64 -  the split count

indexed with the 'covariate'.


So for example we can have a phenotype file in csv format like this (this is the modified hipster_lableles.txt):

```
samples,score,label, age, PC0,PC1, PC2
HG00096,6,0,64,0.332,0.444,0.45
HG00097,15.5,1,32,0.444,0.334,0.128 
HG00099,8,0,17,0.983,0.344,0.4343
...
```

Then the code with the use of covariates `age`, `PC0` and `PC1` should look like this:

```
    data = hl.import_vcf(os.path.join(PROJECT_DIR, 'data/hipsterIndex/hipster.vcf.bgz'))
    labels = hl.import_table(os.path.join(PROJECT_DIR, 'data/hipsterIndex/hipster_labels.txt'),
                             delimiter=',',
                             types=dict(label='float64', score='float64', age='float64', PC0='float64',
                                 PC1='float64',, PC2='float64'
                             )).key_by('samples')

    mt = data.annotate_cols(hipster=labels[data.s])
    print(mt.count())

    with vshl.random_forest_model(y=mt.hipster.label,
                                  x=mt.GT.n_alt_alleles(), 
                                  covariates = [mt.hipster.age, mt.hipster.PC0, mt.hipster.PC1],
                                  seed=13, mtry_fraction=0.05,
                                  min_node_size=5, max_depth=10) as rf_model:
        rf_model.fit_trees(100, 50)

        ...
        rf_model.covriate_importance().show() # display the covariate importance table
```

Output:

```
covariate importance splitCount
age         10.0          200
PC0         1.0             4
...
```


Things to consider:

-  in the first instance all covariates can be treated as continuous variables but in the future we could try to use more fine-grained mapping of the hail schema type to VariantSpark feature types which would for example allow to include some covariates as OrderedFactor or Factor(not implemented yet) variables.


Implementation notes

The examples in 'python` directory and possibly a notebook example should be created to demonstrate this new functionality.

The python example can be based on the `examples/local_run-importance-ch22_with_pheno.sh`. The transposed version of the `data/chr22_1000_pheno-wide.csv` may need to be created to support this (that should also include the classification response variable).

For the notebook datasets a more biologically relevant dataset should be used that possibly includes principal component analysis factors.  Maybe hipster index dataset can be adapted for this (maybe with PC factors or some other random covariates that do not need to associated with the response)




Discussion / hints:
Hi @lm-fsng what do you think about the spec above. In particular how would you see getting the importances of the covariates back (and is this even necessary) 
Hi @lm-fsng I have included the changes we discussed. Please confirm that you are happy with the current spec.
And also please follow up with Rob on whether the covariates need to be included in p-value caluclation.
Hi @piotrszul and @rocreguant , just spoke to Rob about the covariates and the p-value calculation. He said that the continuous covariates would be systematically biased to be more important, which would 'push' the genotypes down on the variable importance scale. This would result in less significant SNPs if we include the covariates in the p-value calculation. The workaround that is to ignore the covariates and just use the variable importance of the SNPs in the p-value method. 

Fix patch:
diff --git a/data/chr22_1000_full_pheno.csv b/data/chr22_1000_full_pheno.csv
new file mode 100644
index 00000000..90bcdde3
--- /dev/null
+++ b/data/chr22_1000_full_pheno.csv
@@ -0,0 +1,1093 @@
+sample,22_16050408,age,bmi,PC1,PC2,PC3
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diff --git a/data/chr22_1000_pheno.csv b/data/chr22_1000_pheno.csv
new file mode 100644
index 00000000..fb54a128
--- /dev/null
+++ b/data/chr22_1000_pheno.csv
@@ -0,0 +1,1093 @@
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diff --git a/data/hipsterIndex/hipster_labels_covariates.txt b/data/hipsterIndex/hipster_labels_covariates.txt
new file mode 100644
index 00000000..5d31febc
--- /dev/null
+++ b/data/hipsterIndex/hipster_labels_covariates.txt
@@ -0,0 +1,2505 @@
+samples,score,label,age,PC0,PC1,PC2
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diff --git a/dev-notebooks/HipsterHailvsVS.ipynb b/dev-notebooks/HipsterHailvsVS.ipynb
index 33d83d79..332ba9d7 100644
--- a/dev-notebooks/HipsterHailvsVS.ipynb
+++ b/dev-notebooks/HipsterHailvsVS.ipynb
@@ -640,7 +640,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Build random forest and extract gini importanct with VariantSpark (on the same data)"
+    "## Build random forest and extract gini importance with VariantSpark (on the same data)"
    ]
   },
   {
diff --git a/dev-notebooks/HipsterHailvsVS_covariates.ipynb b/dev-notebooks/HipsterHailvsVS_covariates.ipynb
new file mode 100644
index 00000000..746c43d6
--- /dev/null
+++ b/dev-notebooks/HipsterHailvsVS_covariates.ipynb
@@ -0,0 +1,1020 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "VariantSpark integration with Hail 0.2\n",
+    "==============================\n",
+    "\n",
+    "## Bootstrap\n",
+    "\n",
+    "Use `vshl.init()` to include `variant-spark` jar on the classpath. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "using variant-spark jar at '/Users/reg032/workspace/VariantSpark/target/variant-spark_2.11-0.5.0-a0-dev0-all.jar'\n",
+      "22/05/09 14:34:16 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
+      "Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n",
+      "Setting default log level to \"WARN\".\n",
+      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
+      "22/05/09 14:34:16 WARN Hail: This Hail JAR was compiled for Spark 3.1.1, running with Spark 3.1.2.\n",
+      "  Compatibility is not guaranteed.\n",
+      "22/05/09 14:34:17 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n",
+      "22/05/09 14:34:17 WARN Utils: Service 'SparkUI' could not bind on port 4041. Attempting port 4042.\n",
+      "22/05/09 14:34:17 WARN Utils: Service 'SparkUI' could not bind on port 4042. Attempting port 4043.\n",
+      "22/05/09 14:34:17 WARN Utils: Service 'SparkUI' could not bind on port 4043. Attempting port 4044.\n",
+      "22/05/09 14:34:17 WARN Utils: Service 'SparkUI' could not bind on port 4044. Attempting port 4045.\n",
+      "Running on Apache Spark version 3.1.2\n",
+      "SparkUI available at http://192.168.86.23:4045\n",
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.74-0c3a74d12093\n",
+      "LOGGING: writing to /Users/reg032/workspace/hail-20220509-1434-0.2.74-0c3a74d12093.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "import hail as hl\n",
+    "import varspark.hail as vshl\n",
+    "vshl.init()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div class=\"bk-root\">\n",
+       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
+       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
+       "    </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "\n",
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  var force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "  var JS_MIME_TYPE = 'application/javascript';\n",
+       "  var HTML_MIME_TYPE = 'text/html';\n",
+       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    var script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    var cell = handle.cell;\n",
+       "\n",
+       "    var id = cell.output_area._bokeh_element_id;\n",
+       "    var server_id = cell.output_area._bokeh_server_id;\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null && id in Bokeh.index) {\n",
+       "      Bokeh.index[id].model.document.clear();\n",
+       "      delete Bokeh.index[id];\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            var id = msg.content.text.trim();\n",
+       "            if (id in Bokeh.index) {\n",
+       "              Bokeh.index[id].model.document.clear();\n",
+       "              delete Bokeh.index[id];\n",
+       "            }\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    var output_area = handle.output_area;\n",
+       "    var output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      var bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      var script_attrs = bk_div.children[0].attributes;\n",
+       "      for (var i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      var toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    var events = require('base/js/events');\n",
+       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  \n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"<div style='background-color: #fdd'>\\n\"+\n",
+       "     \"<p>\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"</p>\\n\"+\n",
+       "     \"<ul>\\n\"+\n",
+       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
+       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
+       "     \"</ul>\\n\"+\n",
+       "     \"<code>\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"</code>\\n\"+\n",
+       "     \"</div>\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    var el = document.getElementById(\"1001\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error() {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (var i = 0; i < css_urls.length; i++) {\n",
+       "      var url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    for (var i = 0; i < js_urls.length; i++) {\n",
+       "      var url = js_urls[i];\n",
+       "      var element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error;\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };var element = document.getElementById(\"1001\");\n",
+       "  if (element == null) {\n",
+       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
+       "    return false;\n",
+       "  }\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  \n",
+       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
+       "  var css_urls = [];\n",
+       "  \n",
+       "\n",
+       "  var inline_js = [\n",
+       "    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "    function(Bokeh) {\n",
+       "    \n",
+       "    \n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    \n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "      \n",
+       "    for (var i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "    if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from hail.plot import show\n",
+    "from pprint import pprint\n",
+    "hl.plot.output_notebook()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load and explore hipster data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = hl.import_vcf('../data/hipsterIndex/hipster.vcf.bgz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-05-09 14:34:22 Hail: INFO: Reading table without type imputation\n",
+      "  Loading field 'samples' as type str (not specified)\n",
+      "  Loading field 'score' as type float64 (user-supplied)\n",
+      "  Loading field 'label' as type float64 (user-supplied)\n",
+      "  Loading field 'age' as type float64 (user-supplied)\n",
+      "  Loading field 'PC0' as type float64 (user-supplied)\n",
+      "  Loading field 'PC1' as type float64 (user-supplied)\n",
+      "  Loading field 'PC2' as type float64 (user-supplied)\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = hl.import_table('../data/hipsterIndex/hipster_labels_covariates.txt', delimiter=',', \n",
+    "                types=dict(label='float64', score='float64', age='float64', PC0='float64', PC1='float64', PC2='float64')).key_by('samples')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    None\n",
+      "----------------------------------------\n",
+      "Column fields:\n",
+      "    's': str\n",
+      "    'hipster': struct {\n",
+      "        score: float64, \n",
+      "        label: float64, \n",
+      "        age: float64, \n",
+      "        PC0: float64, \n",
+      "        PC1: float64, \n",
+      "        PC2: float64\n",
+      "    }\n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus<GRCh37>\n",
+      "    'alleles': array<str>\n",
+      "    'rsid': str\n",
+      "    'qual': float64\n",
+      "    'filters': set<str>\n",
+      "    'info': struct {\n",
+      "        AA: str, \n",
+      "        AC: array<int32>, \n",
+      "        AF: array<float64>, \n",
+      "        AFR_AF: array<float64>, \n",
+      "        AMR_AF: array<float64>, \n",
+      "        AN: int32, \n",
+      "        CIEND: array<int32>, \n",
+      "        CIPOS: array<int32>, \n",
+      "        CS: str, \n",
+      "        DP: int32, \n",
+      "        EAS_AF: array<float64>, \n",
+      "        END: int32, \n",
+      "        EUR_AF: array<float64>, \n",
+      "        EX_TARGET: bool, \n",
+      "        IMPRECISE: bool, \n",
+      "        MC: array<str>, \n",
+      "        MEINFO: array<str>, \n",
+      "        MEND: int32, \n",
+      "        MLEN: int32, \n",
+      "        MSTART: int32, \n",
+      "        MULTI_ALLELIC: bool, \n",
+      "        NS: int32, \n",
+      "        SAS_AF: array<float64>, \n",
+      "        SVLEN: array<int32>, \n",
+      "        SVTYPE: str, \n",
+      "        TSD: str, \n",
+      "        VT: array<str>\n",
+      "    }\n",
+      "----------------------------------------\n",
+      "Entry fields:\n",
+      "    'GT': call\n",
+      "----------------------------------------\n",
+      "Column key: ['s']\n",
+      "Row key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "mt = data.annotate_cols(hipster = labels[data.s])\n",
+    "mt.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-05-09 14:34:25 Hail: INFO: Coerced almost-sorted dataset       (0 + 1) / 1]\n",
+      "[Stage 1:>                                                          (0 + 1) / 1]\r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(17010, 2504)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mt.count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run log regression using Hail"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-05-09 14:34:29 Hail: INFO: Coerced almost-sorted dataset\n",
+      "2022-05-09 14:34:32 Hail: INFO: logistic_regression_rows: running score on 2504 samples for response variable y,\n",
+      "    with input variable x, and 5 additional covariates...\n"
+     ]
+    }
+   ],
+   "source": [
+    "gwas = hl.logistic_regression_rows(test='score',\n",
+    "                                y=mt.hipster.label,\n",
+    "                                 x=mt.GT.n_alt_alleles(),\n",
+    "                                 covariates=[1.0, mt.hipster.age, mt.hipster.PC0, mt.hipster.PC1, mt.hipster.PC2],\n",
+    "                                 pass_through=[mt.rsid])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "[Stage 4:>                                                          (0 + 1) / 1]\r"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">locus</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">alleles</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">rsid</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">chi_sq_stat</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">p_value</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">locus&lt;GRCh37&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">array&lt;str&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">str</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td></tr>\n",
+       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2:109511398</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;rs150055772&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.45e-01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.21e-01</td></tr>\n",
+       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2:109511454</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;rs558429529&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.58e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.08e-01</td></tr>\n",
+       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2:109511463</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;rs200762071&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.39e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.54e-02</td></tr>\n",
+       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 3 rows</p>\n"
+      ],
+      "text/plain": [
+       "+---------------+------------+---------------+-------------+----------+\n",
+       "| locus         | alleles    | rsid          | chi_sq_stat |  p_value |\n",
+       "+---------------+------------+---------------+-------------+----------+\n",
+       "| locus<GRCh37> | array<str> | str           |     float64 |  float64 |\n",
+       "+---------------+------------+---------------+-------------+----------+\n",
+       "| 2:109511398   | [\"G\",\"A\"]  | \"rs150055772\" |    2.45e-01 | 6.21e-01 |\n",
+       "| 2:109511454   | [\"C\",\"A\"]  | \"rs558429529\" |    1.58e+00 | 2.08e-01 |\n",
+       "| 2:109511463   | [\"G\",\"A\"]  | \"rs200762071\" |    3.39e+00 | 6.54e-02 |\n",
+       "+---------------+------------+---------------+-------------+----------+\n",
+       "showing top 3 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gwas.show(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "  <div class=\"bk-root\" id=\"0fd03b49-4886-4f4e-9d59-5213c8c2bbb3\" data-root-id=\"1003\"></div>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function embed_document(root) {\n",
+       "    \n",
+       "  var docs_json = 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Application\",\"version\":\"1.4.0\"}};\n",
+       "  var render_items = [{\"docid\":\"ac18815b-0eb9-4df0-894c-610631249b9f\",\"roots\":{\"1003\":\"0fd03b49-4886-4f4e-9d59-5213c8c2bbb3\"}}];\n",
+       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "\n",
+       "  }\n",
+       "  if (root.Bokeh !== undefined) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (root.Bokeh !== undefined) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else {\n",
+       "        attempts++;\n",
+       "        if (attempts > 100) {\n",
+       "          clearInterval(timer);\n",
+       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
+       "        }\n",
+       "      }\n",
+       "    }, 10, root)\n",
+       "  }\n",
+       "})(window);"
+      ],
+      "application/vnd.bokehjs_exec.v0+json": ""
+     },
+     "metadata": {
+      "application/vnd.bokehjs_exec.v0+json": {
+       "id": "1003"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = hl.plot.manhattan(gwas.p_value, hover_fields=dict(rs=gwas.rsid))\n",
+    "show(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Fig 1: Manhattan plot for logistic regression p-values._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build random forest and extract gini importance with VariantSpark (on the same data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-05-09 14:35:04 Hail: INFO: Coerced almost-sorted dataset\n",
+      "[Stage 696:>                                                        (0 + 8) / 9]\r"
+     ]
+    }
+   ],
+   "source": [
+    "rf_model = vshl.random_forest_model(y=mt.hipster.label,\n",
+    "                    x=mt.GT.n_alt_alleles(), \n",
+    "                    covariates={'age':mt.hipster.age, 'PC0':mt.hipster.PC0, 'PC1':mt.hipster.PC1, 'PC2':mt.hipster.PC2})\n",
+    "rf_model.fit_trees(500, 100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Capture the variant importances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.18130990415335463\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-05-09 14:38:43 Hail: INFO: Coerced sorted dataset\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">locus</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">alleles</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">importance</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">splitCount</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">locus&lt;GRCh37&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">array&lt;str&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int64</td></tr>\n",
+       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2:109511398</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
+       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2:109511454</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.84e-02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4</td></tr>\n",
+       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2:109511463</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.13e-01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">24</td></tr>\n",
+       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 3 rows</p>\n"
+      ],
+      "text/plain": [
+       "+---------------+------------+------------+------------+\n",
+       "| locus         | alleles    | importance | splitCount |\n",
+       "+---------------+------------+------------+------------+\n",
+       "| locus<GRCh37> | array<str> |    float64 |      int64 |\n",
+       "+---------------+------------+------------+------------+\n",
+       "| 2:109511398   | [\"G\",\"A\"]  |   0.00e+00 |          0 |\n",
+       "| 2:109511454   | [\"C\",\"A\"]  |   1.84e-02 |          4 |\n",
+       "| 2:109511463   | [\"G\",\"A\"]  |   1.13e-01 |         24 |\n",
+       "+---------------+------------+------------+------------+\n",
+       "showing top 3 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(rf_model.oob_error())\n",
+    "impTable = rf_model.variable_importance()\n",
+    "impTable.show(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show the covariates importances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-05-09 14:38:44 Hail: INFO: Coerced sorted dataset\n",
+      "2022-05-09 14:38:44 Hail: INFO: Coerced dataset with out-of-order partitions.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">covariate</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">importance</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">splitCount</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">str</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int64</td></tr>\n",
+       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;PC0&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.53e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">471</td></tr>\n",
+       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;PC1&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.52e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">479</td></tr>\n",
+       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;PC2&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.57e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">464</td></tr>\n",
+       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;age&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.55e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">473</td></tr>\n",
+       "</tbody></table>"
+      ],
+      "text/plain": [
+       "+-----------+------------+------------+\n",
+       "| covariate | importance | splitCount |\n",
+       "+-----------+------------+------------+\n",
+       "| str       |    float64 |      int64 |\n",
+       "+-----------+------------+------------+\n",
+       "| \"PC0\"     |   2.53e+00 |        471 |\n",
+       "| \"PC1\"     |   2.52e+00 |        479 |\n",
+       "| \"PC2\"     |   2.57e+00 |        464 |\n",
+       "| \"age\"     |   2.55e+00 |        473 |\n",
+       "+-----------+------------+------------+"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "covImpTable = rf_model.covariate_importance()\n",
+    "covImpTable.show(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Join hail and VariantSpark results (this is only needed here to get the RSID's)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-05-09 14:38:44 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'alleles' -> 'alleles_1'\n",
+      "    'locus' -> 'locus_1'\n"
+     ]
+    }
+   ],
+   "source": [
+    "gwas_with_imp = gwas.join(impTable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "[Stage 746:>                                                        (0 + 1) / 1]\r"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "  <div class=\"bk-root\" id=\"8b3cb01c-4499-4681-9d01-af20d9f8f6df\" data-root-id=\"1161\"></div>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function embed_document(root) {\n",
+       "    \n",
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":{\"id\":\"1200\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1201\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1198\",\"type\":\"CDSView\"}},\"id\":\"1202\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"filters\":[{\"id\":\"1209\",\"type\":\"GroupFilter\"}],\"source\":{\"id\":\"1196\",\"type\":\"ColumnDataSource\"}},\"id\":\"1210\",\"type\":\"CDSView\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"_global_locus\"},\"y\":{\"field\":\"_pval\"}},\"id\":\"1225\",\"type\":\"Circle\"},{\"attributes\":{\"label\":{\"value\":\"5\"},\"renderers\":[{\"id\":\"1220\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1230\",\"type\":\"LegendItem\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"_global_locus\"},\"y\":{\"field\":\"_pval\"}},\"id\":\"1219\",\"type\":\"Circle\"},{\"attributes\":{\"ticker\":{\"id\":\"1172\",\"type\":\"BasicTicker\"}},\"id\":\"1175\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"1196\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1224\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1225\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1222\",\"type\":\"CDSView\"}},\"id\":\"1226\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_color\":{\"field\":\"_contig\",\"transform\":{\"id\":\"1160\",\"type\":\"CategoricalColorMapper\"}},\"line_color\":{\"field\":\"_contig\",\"transform\":{\"id\":\"1160\",\"type\":\"CategoricalColorMapper\"}},\"x\":{\"field\":\"_global_locus\"},\"y\":{\"field\":\"_pval\"}},\"id\":\"1200\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"1196\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1218\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1219\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1216\",\"type\":\"CDSView\"}},\"id\":\"1220\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1169\",\"type\":\"LinearScale\"}],\"root_ids\":[\"1161\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
+       "  var render_items = [{\"docid\":\"1d065151-5af3-4dc1-8b2f-7168f3b3fba0\",\"roots\":{\"1161\":\"8b3cb01c-4499-4681-9d01-af20d9f8f6df\"}}];\n",
+       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "\n",
+       "  }\n",
+       "  if (root.Bokeh !== undefined) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (root.Bokeh !== undefined) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else {\n",
+       "        attempts++;\n",
+       "        if (attempts > 100) {\n",
+       "          clearInterval(timer);\n",
+       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
+       "        }\n",
+       "      }\n",
+       "    }, 10, root)\n",
+       "  }\n",
+       "})(window);"
+      ],
+      "application/vnd.bokehjs_exec.v0+json": ""
+     },
+     "metadata": {
+      "application/vnd.bokehjs_exec.v0+json": {
+       "id": "1161"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import varspark.hail.plot as vshlplt\n",
+    "p = vshlplt.manhattan_imp(gwas_with_imp.importance, \n",
+    "                            hover_fields=dict(ri=gwas_with_imp.rsid),\n",
+    "                            significance_line = None)\n",
+    "show(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Fig 2: Manhattan plot for rf gin importance values._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare logistc regression values vs. rf importance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "  <div class=\"bk-root\" id=\"28a45c93-2f53-4c0d-a6d2-4156b2d77c12\" data-root-id=\"1326\"></div>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function embed_document(root) {\n",
+       "    \n",
+       "  var docs_json = 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\",\"dtype\":\"float64\",\"shape\":[561]}},\"selected\":{\"id\":\"1391\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1390\",\"type\":\"UnionRenderers\"}},\"id\":\"1361\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1334\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null},\"id\":\"1328\",\"type\":\"DataRange1d\"},{\"attributes\":{\"axis_label\":\"gini importance\",\"formatter\":{\"id\":\"1388\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1342\",\"type\":\"BasicTicker\"}},\"id\":\"1341\",\"type\":\"LinearAxis\"},{\"attributes\":{\"callback\":null},\"id\":\"1330\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1332\",\"type\":\"LinearScale\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1346\",\"type\":\"PanTool\"},{\"id\":\"1347\",\"type\":\"WheelZoomTool\"},{\"id\":\"1348\",\"type\":\"BoxZoomTool\"},{\"id\":\"1349\",\"type\":\"SaveTool\"},{\"id\":\"1350\",\"type\":\"ResetTool\"},{\"id\":\"1351\",\"type\":\"HelpTool\"},{\"id\":\"1360\",\"type\":\"HoverTool\"}]},\"id\":\"1352\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1349\",\"type\":\"SaveTool\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1342\",\"type\":\"BasicTicker\"}},\"id\":\"1345\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1342\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1347\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1364\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1350\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1390\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"ticker\":{\"id\":\"1337\",\"type\":\"BasicTicker\"}},\"id\":\"1340\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"1361\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1363\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1364\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1366\",\"type\":\"CDSView\"}},\"id\":\"1365\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1391\",\"type\":\"Selection\"},{\"attributes\":{\"source\":{\"id\":\"1361\",\"type\":\"ColumnDataSource\"}},\"id\":\"1366\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1386\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"overlay\":{\"id\":\"1392\",\"type\":\"BoxAnnotation\"}},\"id\":\"1348\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1363\",\"type\":\"Circle\"}],\"root_ids\":[\"1326\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
+       "  var render_items = [{\"docid\":\"a7e48548-75db-40d9-b01b-b5a7df3b244e\",\"roots\":{\"1326\":\"28a45c93-2f53-4c0d-a6d2-4156b2d77c12\"}}];\n",
+       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "\n",
+       "  }\n",
+       "  if (root.Bokeh !== undefined) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (root.Bokeh !== undefined) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else {\n",
+       "        attempts++;\n",
+       "        if (attempts > 100) {\n",
+       "          clearInterval(timer);\n",
+       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
+       "        }\n",
+       "      }\n",
+       "    }, 10, root)\n",
+       "  }\n",
+       "})(window);"
+      ],
+      "application/vnd.bokehjs_exec.v0+json": ""
+     },
+     "metadata": {
+      "application/vnd.bokehjs_exec.v0+json": {
+       "id": "1326"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = hl.plot.scatter(x=-hl.log10(gwas_with_imp.p_value),\n",
+    "                    y=gwas_with_imp.importance, \n",
+    "                    xlabel = '-log10(p-value)',\n",
+    "                    ylabel = 'gini importance',\n",
+    "                    hover_fields=dict(rs=gwas_with_imp.rsid, loc=gwas_with_imp.locus))\n",
+    "show(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Fig 3: Compare gini importance vs logistic regresion p-values._"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dev/dev-requirements.txt b/dev/dev-requirements.txt
index bd5d33c8..a1b8c599 100644
--- a/dev/dev-requirements.txt
+++ b/dev/dev-requirements.txt
@@ -11,4 +11,5 @@ twine==3.2.0
 # varspark dependencies
 pandas==1.1.4
 typedecorator==0.0.5
+Jinja2==3.0.3
 hail==0.2.74
diff --git a/python/examples/hipster_index_hail_covariates.py b/python/examples/hipster_index_hail_covariates.py
new file mode 100644
index 00000000..ab89d847
--- /dev/null
+++ b/python/examples/hipster_index_hail_covariates.py
@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+'''
+Latest modification on 9 May 2022
+
+@author: szu004
+@coauthor: roc
+'''
+import os
+
+import hail as hl
+
+import varspark.hail as vshl
+from pyspark.sql.functions import desc
+
+PROJECT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir))
+
+
+def main():
+    vshl.init()
+
+    data = hl.import_vcf(os.path.join(PROJECT_DIR, 'data/hipsterIndex/hipster.vcf.bgz'))
+    labels = hl.import_table(os.path.join(PROJECT_DIR, 'data/hipsterIndex/hipster_labels_covariates.txt'),
+                             delimiter=',',
+                             types=dict(label='float64', score='float64', age='float64', PC0='float64', PC1='float64', PC2='float64')).key_by('samples')
+
+    mt = data.annotate_cols(hipster=labels[data.s])
+    print(mt.count())
+
+    with vshl.random_forest_model(y=mt.hipster.label,
+                                  x=mt.GT.n_alt_alleles(), 
+                                  covariates={'age':mt.hipster.age, 'PC0':mt.hipster.PC0, 'PC1':mt.hipster.PC1, 'PC2':mt.hipster.PC2},
+                                  seed=13, mtry_fraction=0.05,
+                                  min_node_size=5, max_depth=10) as rf_model:
+        rf_model.fit_trees(100, 50)
+        print("OOB error: %s" % rf_model.oob_error())
+
+	# Variable importances
+        impTable = rf_model.variable_importance()
+        impTable.to_spark(flatten=True).orderBy(desc('importance')).show(100)
+
+        #Cavariate importances 
+        covImpTable = rf_model.covariate_importance()
+        covImpTable.to_spark(flatten=True).orderBy(desc('importance')).show(4)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/python/requirements.txt b/python/requirements.txt
index 29aa9615..c78be0af 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -4,3 +4,4 @@ pandas==1.1.4
 typedecorator==0.0.5
 hail==0.2.74
 pyspark==3.1.2
+Jinja2==3.0.3
\ No newline at end of file
diff --git a/python/varspark/hail/methods.py b/python/varspark/hail/methods.py
index 822517b4..3980b100 100644
--- a/python/varspark/hail/methods.py
+++ b/python/varspark/hail/methods.py
@@ -1,5 +1,11 @@
+from typing import Dict
+from hail.utils import wrap_to_list
+from hail.utils.java import Env
+
+
 from hail.expr.expressions import *
 from hail.typecheck import *
+from hail.methods.statgen import _get_regression_row_fields
 
 from . import rf
 
@@ -7,7 +13,7 @@
 @typecheck(
     y=expr_float64,
     x=expr_int32,
-    covariates=sequenceof(expr_float64),
+    covariates=dictof(str, expr_float64),
     oob=bool,
     mtry_fraction=nullable(float),
     min_node_size=nullable(int),
@@ -15,13 +21,13 @@
     seed=nullable(int),
     imputation_type=nullable(str)
 )
-def random_forest_model(y, x, covariates=(), oob=True, mtry_fraction=None,
+def random_forest_model(y, x, covariates={}, oob=True, mtry_fraction=None,
                         min_node_size=None, max_depth=None, seed=None, imputation_type=None):
     """Creates an empty random forest classifier with specified parameters.
 
     :param Float64Expression y:  Column-indexed response expressions.
     :param Float64Expression x:  Entry-indexed expression for input variable.
-    :param covariates: Reserved for future use.
+    :param covariates: Dictionary of covariate names and their respective expression.
     :param bool oob: Should OOB error be calculated.
     :param float mtry_fraction: The fraction of variables to try at each split.
     :param int min_node_size: The minimum number of samples in a node to be consider for splitting.
@@ -34,12 +40,26 @@ def random_forest_model(y, x, covariates=(), oob=True, mtry_fraction=None,
     :return: An empty random forest classifier.
     :rtype: :py:class:`RandomForestModel`
     """
+
     mt = matrix_table_source('random_forest_model/x', x)
     check_entry_indexed('random_forest_model/x', x)
-    mts = mt._select_all(col_exprs=dict(y=y),
-                         row_exprs=dict(),
+
+    for key, e in covariates.items():
+        analyze('random_forest_model/covariates', e, mt._col_indices)
+
+    '''potential multi-predictions
+    y = wrap_to_list(y)
+    y_field = ['y']
+    y_dict = dict(zip(y_field, y))'''
+    y_dict = {'y':y}
+
+    mts = mt._select_all(col_exprs=dict(**y_dict,
+                                        **{'cov__'+k:v for k,v in covariates.items()}),
+                         row_exprs={},
                          col_key=[],
                          entry_exprs=dict(e=x))
+
+
     return rf.RandomForestModel(mts._mir,
                                 oob=oob,
                                 mtry_fraction=mtry_fraction,
diff --git a/python/varspark/hail/rf.py b/python/varspark/hail/rf.py
index 3d6e5e98..6447b5c1 100644
--- a/python/varspark/hail/rf.py
+++ b/python/varspark/hail/rf.py
@@ -69,6 +69,22 @@ def variable_importance(self):
         """
         return Table._from_java(self._jrf_model.variableImportance())
 
+    def covariate_importance(self):
+        """ Returns the covariate importance for this model in a hail `Table` with the following
+        row fields:
+
+                'covariate': str
+                'importance': float64
+                'splitCount': float64
+
+            and indexed with: ['covariate'].
+
+            The `importance` column contains gini importance for each of the covariates.
+
+            :rtype: :py:class:`hail.is.Table`
+        """
+        return Table._from_java(self._jrf_model.covariatesImportance())
+
     @typecheck_method(
         filename=str,
         resolve_names=bool
diff --git a/src/main/scala/au/csiro/variantspark/data/Feature.scala b/src/main/scala/au/csiro/variantspark/data/Feature.scala
index 80c96624..984c6ad8 100644
--- a/src/main/scala/au/csiro/variantspark/data/Feature.scala
+++ b/src/main/scala/au/csiro/variantspark/data/Feature.scala
@@ -4,7 +4,7 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.rdd.RDD
 
 /**
-  * TODO: Rethingk this inheritance
+  * TODO: Rethink this inheritance
   */
 trait Feature extends Data {
   def label: String
diff --git a/src/main/scala/au/csiro/variantspark/hail/methods/RFModel.scala b/src/main/scala/au/csiro/variantspark/hail/methods/RFModel.scala
index 2a4ddc5a..8fc2f0c9 100644
--- a/src/main/scala/au/csiro/variantspark/hail/methods/RFModel.scala
+++ b/src/main/scala/au/csiro/variantspark/hail/methods/RFModel.scala
@@ -1,7 +1,6 @@
 package au.csiro.variantspark.hail.methods
 
 import java.io.OutputStreamWriter
-
 import au.csiro.pbdava.ssparkle.common.utils.{LoanUtils, Logging}
 import au.csiro.variantspark.algo.{
   DefTreeRepresentationFactory,
@@ -26,9 +25,11 @@ import is.hail.stats.RegressionUtils
 import is.hail.types.virtual._
 import is.hail.utils.{ExecutionTimer, fatal}
 import is.hail.variant._
+
 import javax.annotation.Nullable
 import org.apache.hadoop.conf.Configuration
 import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.Row
 import org.apache.spark.storage.StorageLevel
@@ -51,6 +52,7 @@ case class RFModel(backend: SparkBackend, inputIR: MatrixIR, rfParams: RandomFor
 
   val responseVarName: String = "y"
   val entryVarname: String = "e"
+  val covariateName: String = "cov__"
 
   // the a stateful object
   // TODO: Maybe refactor to a helper object
@@ -93,15 +95,12 @@ case class RFModel(backend: SparkBackend, inputIR: MatrixIR, rfParams: RandomFor
 
         lazy val rf: RandomForest = new RandomForest(rfParams)
 
-        val featuresRDD: RDD[Feature] =
-          RFModel.mvToFeatureRDD(mv, imputationStrategy.getOrElse(DisabledImputationStrategy))
-        inputData = DefTreeRepresentationFactory.createRepresentation(featuresRDD.zipWithIndex())
-
         // These are currently obrained as doubles and converted to Int's needed by RandomForest
         // This is because getPhenosCovCompleteSamples only works on Double64 columns
         // This may be optimized in the future
         val (yMat, cov, completeColIdx) =
-          RegressionUtils.getPhenosCovCompleteSamples(mv, Array(responseVarName), Array[String]())
+          RegressionUtils.getPhenosCovCompleteSamples(mv, Array(mv.typ.colType.fieldNames.head),
+            mv.typ.colType.fieldNames.tail)
         // completeColIdx are indexes of the complete samples.
         // These can be used to subsample the entry data
         // but for now let's just assume that there are no NAs in the labels (and or covariates).
@@ -117,6 +116,20 @@ case class RFModel(backend: SparkBackend, inputIR: MatrixIR, rfParams: RandomFor
         }
         val labels = labelVector.map(_.toInt).toArray
 
+        val covFeatures = mv.typ.colType.fieldNames.tail.toSeq.zipWithIndex
+          .map {
+            case (name, i) =>
+              StdFeature.from(name, Vectors.dense(cov(::, i).toArray))
+          }
+
+        val covariateRDD = backend.sc.makeRDD(covFeatures)
+        val genotypeRDD: RDD[Feature] =
+          RFModel.mvToFeatureRDD(mv, imputationStrategy.getOrElse(DisabledImputationStrategy))
+
+        val featuresRDD = genotypeRDD.union(covariateRDD)
+
+        inputData = DefTreeRepresentationFactory.createRepresentation(featuresRDD.zipWithIndex())
+
         // now we somehow need to get to row data
         if (inputData.getStorageLevel == StorageLevel.NONE) {
           inputData.cache()
@@ -138,19 +151,47 @@ case class RFModel(backend: SparkBackend, inputIR: MatrixIR, rfParams: RandomFor
           keySignature.insertFields(Array(("importance", TFloat64), ("splitCount", TInt64)))
         val brVarImp = importanceMapBroadcast
         val brSplitCount = splitCountMapBroadcast
+        val covName = covariateName
         val mapRDD = inputData.mapPartitions { it =>
           val varImp = brVarImp.value
           val splitCount = brSplitCount.value
-          it.map { tf =>
-            RFModel.tfFeatureToImpRow(tf.label, varImp.getOrElse(tf.index, 0.0),
-              splitCount.getOrElse(tf.index, 0L))
-          }
+
+          it.filter(tf => !tf.label.startsWith(covName))
+            .map { tf =>
+              RFModel.tfFeatureToImpRow(tf.label, varImp.getOrElse(tf.index, 0.0),
+                splitCount.getOrElse(tf.index, 0L))
+            }
         }
         TableLiteral(TableValue(ctx, sig, key, mapRDD))
       }
     }
   }
 
+  def covariatesImportance: TableIR = {
+    ExecutionTimer.logTime("RFModel.fitTrees") { timer =>
+      backend.withExecuteContext(timer) { ctx =>
+        // the result should keep the key + add importance related field
+        val sig: TStruct = TStruct("covariate" -> TString, "importance" -> TFloat64,
+          "splitCount" ->
+            TInt64)
+        val brVarImp = importanceMapBroadcast
+        val brSplitCount = splitCountMapBroadcast
+        val covName = covariateName
+        val mapRDD = inputData.mapPartitions { it =>
+          val varImp = brVarImp.value
+          val splitCount = brSplitCount.value
+
+          it.filter { tf => tf.label.startsWith(covName) }
+            .map { tf =>
+              Row(tf.label.split(covName)(1), varImp.getOrElse(tf.index, 0.0),
+                splitCount.getOrElse(tf.index, 0L))
+            }
+        }
+        TableLiteral(TableValue(ctx, sig, Array("covariate"), mapRDD))
+      }
+    }
+  }
+
   def toJson(jsonFilename: String, resolveVarNames: Boolean) {
     println(s"Saving model to: ${jsonFilename}")
     implicit val hadoopConf: Configuration = inputData.sparkContext.hadoopConfiguration


Test patch:
diff --git a/dev/test-gen-hail-cases.sh b/dev/test-gen-hail-cases.sh
index afb220e1..bd383802 100755
--- a/dev/test-gen-hail-cases.sh
+++ b/dev/test-gen-hail-cases.sh
@@ -23,6 +23,7 @@ mkdir -p "${WORK_DIR}"
 
 # also generate hail vds
 python "${FWDIR}/src/test/python/extract_rf_vds.py"
+python "${FWDIR}/src/test/python/extract_rf_pheno_vds.py"
 
 # chr22-100
 RESP="22_16050408"
diff --git a/python/varspark/test/test_hail.py b/python/varspark/test/test_hail.py
index 1ecc21aa..0bddc0c3 100644
--- a/python/varspark/test/test_hail.py
+++ b/python/varspark/test/test_hail.py
@@ -1,6 +1,7 @@
 import filecmp
 import os
 import unittest
+from io import StringIO
 
 import hail as hl
 import numpy.testing as npt
@@ -71,5 +72,44 @@ def test_importance_analysis_from_vcf(self):
             pdt.assert_frame_equal(expected_top100_imp, actual_top100_imp)
 
 
+
+    def test_covariates(self):
+
+
+        data = hl.import_vcf(os.path.join(PROJECT_DIR, 'data/chr22_1000.vcf'))
+        labels = hl.import_table(os.path.join(PROJECT_DIR, 'data/chr22-labels.csv'),
+                                 delimiter=',',
+                                 types={'22_16050408': 'float64'}).key_by('sample')
+
+        # loading the covariates and transposing them
+        covariates = pd.read_csv(
+            os.path.join(PROJECT_DIR, 'data/chr22_1000_pheno-wide.csv')).T.reset_index()
+        covariates.at[0, 'index'] = 'samples'
+        covariates.columns = covariates.iloc[0]
+        covariates = covariates.iloc[1:, :]
+        covariates = hl.Table.from_pandas(covariates).key_by('samples')
+        mt = data.annotate_cols(covariates=covariates[data.s],
+                                labels=labels.select('22_16050408')[data.s])
+
+        rf_model = vshl.random_forest_model(y=mt.labels['22_16050408'],
+                                                  x=mt.GT.n_alt_alleles(),
+                                                  covariates={'age': mt.covariates.age,
+                                                              'bmi': mt.covariates.bmi},
+                                                  seed=13, mtry_fraction=0.05,
+                                                  min_node_size=5, max_depth=10)
+
+        rf_model.fit_trees(100, 50)
+
+
+        impTable = rf_model.variable_importance()
+        self.assertEqual(len(impTable.to_pandas()), 1988)
+
+        covImpTable = rf_model.covariate_importance()
+        self.assertEqual(len(covImpTable.to_pandas()), 2)
+
+
+
+
+
 if __name__ == '__main__':
     unittest.main(verbosity=2)
diff --git a/src/test/data/hail/chr22_1000-22_16050408-pheno.vds/README.txt b/src/test/data/hail/chr22_1000-22_16050408-pheno.vds/README.txt
new file mode 100644
index 00000000..65ee15cb
--- /dev/null
+++ b/src/test/data/hail/chr22_1000-22_16050408-pheno.vds/README.txt
@@ -0,0 +1,3 @@
+This folder comprises a Hail (www.hail.is) native Table or MatrixTable.
+  Written with version 0.2.74-0c3a74d12093
+  Created at 2022/05/06 11:31:18
\ No newline at end of file
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new file mode 100644
index 00000000..e69de29b
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new file mode 100644
index 00000000..e69de29b
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diff --git a/src/test/python/extract_rf_pheno_vds.py b/src/test/python/extract_rf_pheno_vds.py
new file mode 100755
index 00000000..37b5c49a
--- /dev/null
+++ b/src/test/python/extract_rf_pheno_vds.py
@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+
+import os
+import sys
+
+import hail as hl
+from hail.expr.expressions import *
+
+PROJECT_DIR = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), '../../..'))
+
+
+def main():
+    hl.init()
+    data = hl.import_vcf(os.path.join(PROJECT_DIR, 'data/chr22_1000.vcf'))
+    labels = hl.import_table(os.path.join(PROJECT_DIR, 'data/chr22_1000_full_pheno.csv'),
+                             delimiter=',',
+                             types={
+                                 '22_16050408': 'float64',
+                                 'age': 'float64',
+                                 'bmi': 'float64',
+                                 'PC1': 'float64',
+                                 'PC2': 'float64',
+                                 'PC3': 'float64'
+                             }).key_by('sample')
+
+    mt = data.annotate_cols(pheno=labels[data.s])
+    col_expressions = dict(
+        y=mt.pheno['22_16050408'],
+        cov__age=mt.pheno['age'],
+        cov__bmi=mt.pheno['bmi'],
+        cov__PC1=mt.pheno['PC1'],
+        cov__PC2=mt.pheno['PC2'],
+        cov__PC3=mt.pheno['PC3']
+    )
+
+    x = mt.GT.n_alt_alleles()
+    mt = matrix_table_source('random_forest_model/x', x)
+    check_entry_indexed('random_forest_model/x', x)
+    mts = mt._select_all(col_exprs=col_expressions,
+                         row_exprs=dict(),
+                         col_key=[],
+                         entry_exprs=dict(e=x))
+
+    mts.describe()
+
+    mts.write(
+        os.path.join(PROJECT_DIR, 'src/test/data/hail/chr22_1000-22_16050408-pheno.vds'),
+        overwrite=True)
+
+
+if __name__ == "__main__":
+    # execute only if run as a script
+    main()
diff --git a/src/test/scala/au/csiro/variantspark/hail/methods/RFModelHailTest.scala b/src/test/scala/au/csiro/variantspark/hail/methods/RFModelHailTest.scala
index de5dae8e..67a5fb67 100644
--- a/src/test/scala/au/csiro/variantspark/hail/methods/RFModelHailTest.scala
+++ b/src/test/scala/au/csiro/variantspark/hail/methods/RFModelHailTest.scala
@@ -50,4 +50,56 @@ class RFModelHailTest {
     }
   }
 
+  /**
+    * The purpose of this test is basic valdation of the hail interface
+    * More substantial testing of the results is done from python
+    * The input vds for this test is created by:
+    *  `src/test/python/extract_rf_pheno_vds.py` called from `dev/test-gen-hail-cases.sh`
+    */
+  @Test
+  def testRunImportanceAnalysisWithCovriates() {
+
+    val matrixIR =
+      MatrixIR.read(sparkBackend.fs, "src/test/data/hail/chr22_1000-22_16050408-pheno.vds")
+
+    withResource(RFModel.pyApply(sparkBackend, matrixIR, null, true, null, null, 13, "mode")) {
+      rfModel =>
+        rfModel.fitTrees(100, 50)
+        assertTrue("OOB Error is defined", !rfModel.oobError.isNaN)
+        // Assessing gene features
+        val importanceTableIR: TableIR = rfModel.variableImportance
+
+        assertEquals(List("locus", "alleles", "importance", "splitCount"),
+          importanceTableIR.typ.rowType.fieldNames.toList)
+
+        // collect the valued form the TableIR
+        // they cannot be directly converted to DataFrame as the contain
+        // non SparkSQL types such as Locus, which needs to be converted to structs
+        // before conversion to spark DataFame is possible
+        val collectedRows = ExecutionTimer.logTime("HailApiApp.collectValues") { timer =>
+          sparkBackend.withExecuteContext(timer) { ctx =>
+            val tv = Interpret(importanceTableIR, ctx, true)
+            tv.rvd.collect(ctx)
+          }
+        }
+        // Each row is [Locus, GenericArray, Double]
+        assertEquals("All variables have reported importance", 1988, collectedRows.size)
+
+        // Assessing covariates
+        val covImportanceTableIR: TableIR = rfModel.covariatesImportance
+
+        assertEquals(List("covariate", "importance", "splitCount"),
+          covImportanceTableIR.typ.rowType.fieldNames.toList)
+
+        val collectedCovRows = ExecutionTimer.logTime("HailApiApp.collectValues") { timer =>
+          sparkBackend.withExecuteContext(timer) { ctx =>
+            val tv = Interpret(covImportanceTableIR, ctx, true)
+            tv.rvd.collect(ctx)
+          }
+        }
+        assertEquals("All covariates have reported importance", 5, collectedCovRows.size)
+
+    }
+  }
+
 }