ezancestry_library_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using ezancestry as a Python library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import clone\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from snps import SNPs\n",
    "\n",
    "from ezancestry.commands import predict\n",
    "\n",
    "# load config variables\n",
    "from ezancestry.config import aisnps_set as _aisnps_set\n",
    "\n",
    "# load functions\n",
    "from ezancestry.fetch import get_thousand_genomes_aisnps\n",
    "from ezancestry.model import DEFAULT_PIPELINE\n",
    "from ezancestry.process import process_user_input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pull aisnps from 1kG\n",
    "\n",
    "This will query the 1000 Genomes VCF for the Kidd et al. AISNPs and save the results as a .csv when it does not exist on your local disk.  \n",
    "The function also returns a results as a pandas DataFrame.\n",
    "\n",
    "Note that this will take a few minutes to run as it is querying the 1000 Genomes VCF.\n",
    "It will download `.tbi` files for the VCFs.\n",
    "\n",
    "**optional**\n",
    "The Kidd and Seldin files are included when you install `ezancestry` and are located in the `ezancestry` directory. You do not need to run `get_thousand_genomes_aisnps` function if you have already installed or cloned the `ezancestry` repository.\n",
    "\n",
    "If you want to load the 1000 Genomes aisnps without downloading anything, you can use the following code:\n",
    "\n",
    "```python\n",
    "pd.read_csv(f\"{_aisnps_directory}/kidd.1kG.csv\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# kidd1kg = get_thousand_genomes_aisnps(aisnps_directory=None, aisnps_sets=\"kidd\")\n",
    "# seldin1kg = get_thousand_genomes_aisnps(aisnps_directory=None, aisnps_sets=\"seldin\")\n",
    "\n",
    "# If you have a custom AISNPs file, named custom.aisnps.txt, you can load it using the following function to query the 1000 Genomes VCF\n",
    "# custom1kg = get_thousand_genomes_aisnps(aisnps_directory=None, aisnps_sets=\"custom\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-21 21:32:20.259 | INFO     | ezancestry.fetch:get_thousand_genomes_aisnps:34 - Loaded: kidd.1kG.csv\n"
     ]
    }
   ],
   "source": [
    "# If the file exists in the aisnps_directory, you can load it rather than querying the 1000 Genomes VCF\n",
    "kidd1kg = get_thousand_genomes_aisnps(aisnps_directory=\"data/aisnps/\", aisnps_sets=_aisnps_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(dtype=&lt;class &#x27;numpy.int8&#x27;&gt;,\n",
       "                               handle_unknown=&#x27;ignore&#x27;, sparse_output=False)),\n",
       "                (&#x27;pca&#x27;, PCA(n_components=10)),\n",
       "                (&#x27;kneighborsclassifier&#x27;,\n",
       "                 KNeighborsClassifier(n_jobs=4, n_neighbors=11,\n",
       "                                      weights=&#x27;distance&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(dtype=&lt;class &#x27;numpy.int8&#x27;&gt;,\n",
       "                               handle_unknown=&#x27;ignore&#x27;, sparse_output=False)),\n",
       "                (&#x27;pca&#x27;, PCA(n_components=10)),\n",
       "                (&#x27;kneighborsclassifier&#x27;,\n",
       "                 KNeighborsClassifier(n_jobs=4, n_neighbors=11,\n",
       "                                      weights=&#x27;distance&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(dtype=&lt;class &#x27;numpy.int8&#x27;&gt;, handle_unknown=&#x27;ignore&#x27;,\n",
       "              sparse_output=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=10)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_jobs=4, n_neighbors=11, weights=&#x27;distance&#x27;)</pre></div></div></div></div></div></div></div>"
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       "Pipeline(steps=[('onehotencoder',\n",
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       "                ('pca', PCA(n_components=10)),\n",
       "                ('kneighborsclassifier',\n",
       "                 KNeighborsClassifier(n_jobs=4, n_neighbors=11,\n",
       "                                      weights='distance'))])"
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    }
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    "DEFAULT_PIPELINE"
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(dtype=&lt;class &#x27;numpy.int8&#x27;&gt;,\n",
       "                               handle_unknown=&#x27;ignore&#x27;, sparse_output=False)),\n",
       "                (&#x27;pca&#x27;, PCA(n_components=10)),\n",
       "                (&#x27;classifier&#x27;,\n",
       "                 RandomForestClassifier(max_depth=100, max_features=1.0,\n",
       "                                        n_estimators=1000, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(dtype=&lt;class &#x27;numpy.int8&#x27;&gt;,\n",
       "                               handle_unknown=&#x27;ignore&#x27;, sparse_output=False)),\n",
       "                (&#x27;pca&#x27;, PCA(n_components=10)),\n",
       "                (&#x27;classifier&#x27;,\n",
       "                 RandomForestClassifier(max_depth=100, max_features=1.0,\n",
       "                                        n_estimators=1000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(dtype=&lt;class &#x27;numpy.int8&#x27;&gt;, handle_unknown=&#x27;ignore&#x27;,\n",
       "              sparse_output=False)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=10)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=100, max_features=1.0, n_estimators=1000,\n",
       "                       random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
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       "Pipeline(steps=[('onehotencoder',\n",
       "                 OneHotEncoder(dtype=<class 'numpy.int8'>,\n",
       "                               handle_unknown='ignore', sparse_output=False)),\n",
       "                ('pca', PCA(n_components=10)),\n",
       "                ('classifier',\n",
       "                 RandomForestClassifier(max_depth=100, max_features=1.0,\n",
       "                                        n_estimators=1000, random_state=42))])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_pipeline = clone(DEFAULT_PIPELINE)\n",
    "better_pipeline.steps[-1] = (\"classifier\", RandomForestClassifier(n_estimators=1000, max_depth=100, max_features=1.0, random_state=42))\n",
    "\n",
    "better_pipeline.fit(kidd1kg.drop(columns=[\"sample\", \"population\", \"superpopulation\", \"gender\"]), kidd1kg[\"superpopulation\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['EUR', 'EUR', 'EUR', ..., 'SAS', 'SAS', 'SAS'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_pipeline.predict(kidd1kg.drop(columns=[\"sample\", \"population\", \"superpopulation\", \"gender\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load from DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mygenomefile = \"/Users/kevin/mygenome/genome2.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-21 21:32:33.181 | INFO     | ezancestry.process:_input_to_dataframe:156 - genome2.txt has a valid genotype for 44 out of a possible 55 (80.0%)\n",
      "2024-02-21 21:32:33.199 | INFO     | ezancestry.model:predict_ancestry:121 - Using user-provided model\n",
      "2024-02-21 21:32:34.583 | INFO     | ezancestry.process:_input_to_dataframe:156 - genome2.txt has a valid genotype for 44 out of a possible 55 (80.0%)\n",
      "2024-02-21 21:32:34.601 | INFO     | ezancestry.model:predict_ancestry:121 - Using user-provided model\n"
     ]
    }
   ],
   "source": [
    "# predict on the\n",
    "predictions = predict(\n",
    "    mygenomefile,\n",
    "    aisnps_set=\"kidd\",\n",
    "    write_predictions=False,\n",
    "    models_directory=None,\n",
    "    output_directory=None,\n",
    "    aisnps_directory=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted_ancestry_superpopulation</th>\n",
       "      <th>EUR</th>\n",
       "      <th>AFR</th>\n",
       "      <th>AMR</th>\n",
       "      <th>EAS</th>\n",
       "      <th>SAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>genome2.txt</th>\n",
       "      <td>EUR</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            predicted_ancestry_superpopulation       EUR  AFR       AMR  EAS  \\\n",
       "genome2.txt                                EUR  0.964286  0.0  0.035714  0.0   \n",
       "\n",
       "             SAS  \n",
       "genome2.txt  0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[[\"predicted_ancestry_superpopulation\", \"EUR\", \"AFR\", \"AMR\", \"EAS\", \"SAS\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-21 21:32:35.997 | INFO     | ezancestry.process:_input_to_dataframe:156 - genome2.txt has a valid genotype for 44 out of a possible 55 (80.0%)\n"
     ]
    }
   ],
   "source": [
    "# This will filter the genome file to only include the SNPs that are in the AISNPs file\n",
    "mygenomedf = process_user_input(mygenomefile, aisnps_directory=\"data/aisnps/\", aisnps_set=\"kidd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.   , 0.129, 0.   , 0.871, 0.   ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_pipeline.predict_proba(mygenomedf[better_pipeline.feature_names_in_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AFR', 'AMR', 'EAS', 'EUR', 'SAS'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_pipeline.classes_"
   ]
  }
 ],
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