cuppa/src/main/python/pycuppa/doc/notebooks/predict_example.ipynb

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   "cell_type": "code",
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   "id": "fa84481c-9b9f-4420-a02a-d18e7b8a3f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from cuppa.sample_data.cuppa_features import FeatureLoaderNew\n",
    "from cuppa.classifier.cuppa_classifier import CuppaClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "97784529-3857-4533-bf24-a934c6abcfe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Suppress log messages\n",
    "import logging\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7686a838-4f3d-4acb-b6b6-df996623a68a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set working directory as the path of the pycuppa package\n",
    "os.chdir(\"../..\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0b562a-75be-4456-9cb9-13ce6a1d23ca",
   "metadata": {},
   "source": [
    "## Load features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32da222d-0ea3-40c0-b55e-e298ee4ec3f0",
   "metadata": {},
   "source": [
    "The features tsv file is formatted as a long form dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95d9a150-cfd5-4713-9942-9ca8d9d59e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Category</th>\n",
       "      <th>Key</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DNA</th>\n",
       "      <td>SNV96</td>\n",
       "      <td>C&gt;A_ACA</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA</th>\n",
       "      <td>SNV96</td>\n",
       "      <td>C&gt;A_ACC</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA</th>\n",
       "      <td>SNV96</td>\n",
       "      <td>C&gt;A_ACG</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA</th>\n",
       "      <td>SNV96</td>\n",
       "      <td>C&gt;A_ACT</td>\n",
       "      <td>87.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNA</th>\n",
       "      <td>SNV96</td>\n",
       "      <td>C&gt;A_CCA</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RNA</th>\n",
       "      <td>ALT_SJ</td>\n",
       "      <td>17;80059742;80061789</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RNA</th>\n",
       "      <td>ALT_SJ</td>\n",
       "      <td>17;80059742;80064051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RNA</th>\n",
       "      <td>ALT_SJ</td>\n",
       "      <td>17;80109649;80110400</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RNA</th>\n",
       "      <td>ALT_SJ</td>\n",
       "      <td>17;80841503;80842021</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RNA</th>\n",
       "      <td>ALT_SJ</td>\n",
       "      <td>17;80841716;80842021</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49816 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Source              Category    Key  Value\n",
       "DNA   SNV96               C>A_ACA  131.0    NaN\n",
       "DNA   SNV96               C>A_ACC   85.0    NaN\n",
       "DNA   SNV96               C>A_ACG   19.0    NaN\n",
       "DNA   SNV96               C>A_ACT   87.0    NaN\n",
       "DNA   SNV96               C>A_CCA  115.0    NaN\n",
       "..      ...                   ...    ...    ...\n",
       "RNA  ALT_SJ  17;80059742;80061789    1.0    NaN\n",
       "RNA  ALT_SJ  17;80059742;80064051    1.0    NaN\n",
       "RNA  ALT_SJ  17;80109649;80110400    3.0    NaN\n",
       "RNA  ALT_SJ  17;80841503;80842021    1.0    NaN\n",
       "RNA  ALT_SJ  17;80841716;80842021    2.0    NaN\n",
       "\n",
       "[49816 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_path = \"cuppa/resources/mock_data/input_data/new_format/prostate_sample.cuppa_data.tsv.gz\"\n",
    "pd.read_table(features_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a854f2-0dd9-4e5f-b1c5-7a8f888554f8",
   "metadata": {},
   "source": [
    "However, this tsv file needs to be parsed into a pandas dataframe of shape n_samples x n_features.\n",
    "Note that this example data only has one sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01104a2e-b5bf-41f1-966b-711fffa4bcb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>snv96.C&gt;A_ACA</th>\n",
       "      <th>snv96.C&gt;A_ACC</th>\n",
       "      <th>snv96.C&gt;A_ACG</th>\n",
       "      <th>snv96.C&gt;A_ACT</th>\n",
       "      <th>snv96.C&gt;A_CCA</th>\n",
       "      <th>...</th>\n",
       "      <th>alt_sj.17;80059742;80061789</th>\n",
       "      <th>alt_sj.17;80059742;80064051</th>\n",
       "      <th>alt_sj.17;80109649;80110400</th>\n",
       "      <th>alt_sj.17;80841503;80842021</th>\n",
       "      <th>alt_sj.17;80841716;80842021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sample_1</th>\n",
       "      <td>131.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 49813 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          snv96.C>A_ACA  snv96.C>A_ACC  snv96.C>A_ACG  snv96.C>A_ACT  snv96.C>A_CCA  ...  alt_sj.17;80059742;80061789  alt_sj.17;80059742;80064051  alt_sj.17;80109649;80110400  alt_sj.17;80841503;80842021  alt_sj.17;80841716;80842021\n",
       "sample_1          131.0           85.0           19.0           87.0          115.0  ...                          1.0                          1.0                          3.0                          1.0                          2.0\n",
       "\n",
       "[1 rows x 49813 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader = FeatureLoaderNew(path=features_path, verbose=True)\n",
    "features = loader.load()\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87d183e-7a25-4c67-8830-1d508d24c1a4",
   "metadata": {},
   "source": [
    "<br>\n",
    "The prefix of the feature names denote the feature type. E.g. the feature \"snv96.C>A_ACA\" has the feature type \"snv96\". Below are the names of all feature types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14c8f6f0-c1d1-4098-9803-aba8f161e3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['snv96', 'event', 'gen_pos', 'sig', 'gene_exp', 'alt_sj']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(features.feat_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555b5e52-4608-4ba0-9e7d-111fef7fb2a6",
   "metadata": {},
   "source": [
    "## Load classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3795ecd-8769-4561-9361-1e0c3a8490bc",
   "metadata": {},
   "source": [
    "A pre-trained CUPPA classifier is stored in the resources directory. Below is an sklearn diagram of the structure of the classifer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb1076ec-c9a2-416a-90ea-a17142c76d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                  transformers=[(&#x27;gen_pos&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;add_noise&#x27;,\n",
       "                                                  NoiseProfileAdder(agg_func=&#x27;sum&#x27;,\n",
       "                                                                    count_ceiling=10000,\n",
       "                                                                    noise_counts=500)),\n",
       "                                                 (&#x27;cos_sim&#x27;,\n",
       "                                                  ProfileSimilarityTransformer(count_ceiling=10000,\n",
       "                                                                               feature_prefix=&#x27;gen_pos.&#x27;)),\n",
       "                                                 (&#x27;non_best_scale&#x27;,\n",
       "                                                  NonBestSimilarityScaler(exponent=5)),\n",
       "                                                 (&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_we...\n",
       "                                                  ProfileSimilarityTransformer(agg_func=&#x27;mean&#x27;,\n",
       "                                                                               feature_prefix=&#x27;alt_sj.&#x27;)),\n",
       "                                                 (&#x27;non_best_scaler&#x27;,\n",
       "                                                  NonBestSimilarityScaler(exponent=2)),\n",
       "                                                 (&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=4,\n",
       "                                                                     class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     max_iter=10000,\n",
       "                                                                     multi_class=&#x27;multinomial&#x27;,\n",
       "                                                                     penalty=&#x27;l1&#x27;,\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver=&#x27;saga&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x2b35753d0&gt;)],\n",
       "                  verbose=True)), (&#x27;meta_clfs&#x27;, ColumnTransformer(n_jobs=5,\n",
       "                  transformers=[(&#x27;dna_combined&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     max_iter=1000,\n",
       "                                                                     multi_class=&#x27;multinomial&#x27;,\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver=&#x27;saga&#x27;)),\n",
       "                                                 (&#x27;calibrator&#x27;,\n",
       "                                                  RollingAvgCalibration(min_true_samples=10,\n",
       "                                                                        window_size=&#x27;variable&#x27;)),\n",
       "                                                 (&#x27;fusion_overrider&#x27;,\n",
       "                                                  FusionProbOverrider(_is_fitted=True...\n",
       "                                                              use_first_col=True)),\n",
       "                                                 (&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     max_iter=1000,\n",
       "                                                                     multi_class=&#x27;multinomial&#x27;,\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver=&#x27;saga&#x27;)),\n",
       "                                                 (&#x27;calibrator&#x27;,\n",
       "                                                  RollingAvgCalibration(min_true_samples=10,\n",
       "                                                                        window_size=&#x27;variable&#x27;)),\n",
       "                                                 (&#x27;sex_filter&#x27;,\n",
       "                                                  SexProbFilter())]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x2b4b8ab80&gt;)],\n",
       "                  verbose=True)), (&#x27;prob_combiner&#x27;, ProbCombiner())])</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\">CuppaClassifier</label><div class=\"sk-toggleable__content\"><pre>CuppaClassifier(steps=[(&#x27;sub_clfs&#x27;, ColumnTransformer(n_jobs=5,\n",
       "                  transformers=[(&#x27;gen_pos&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;add_noise&#x27;,\n",
       "                                                  NoiseProfileAdder(agg_func=&#x27;sum&#x27;,\n",
       "                                                                    count_ceiling=10000,\n",
       "                                                                    noise_counts=500)),\n",
       "                                                 (&#x27;cos_sim&#x27;,\n",
       "                                                  ProfileSimilarityTransformer(count_ceiling=10000,\n",
       "                                                                               feature_prefix=&#x27;gen_pos.&#x27;)),\n",
       "                                                 (&#x27;non_best_scale&#x27;,\n",
       "                                                  NonBestSimilarityScaler(exponent=5)),\n",
       "                                                 (&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_we...\n",
       "                                                  ProfileSimilarityTransformer(agg_func=&#x27;mean&#x27;,\n",
       "                                                                               feature_prefix=&#x27;alt_sj.&#x27;)),\n",
       "                                                 (&#x27;non_best_scaler&#x27;,\n",
       "                                                  NonBestSimilarityScaler(exponent=2)),\n",
       "                                                 (&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=4,\n",
       "                                                                     class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     max_iter=10000,\n",
       "                                                                     multi_class=&#x27;multinomial&#x27;,\n",
       "                                                                     penalty=&#x27;l1&#x27;,\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver=&#x27;saga&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x2b35753d0&gt;)],\n",
       "                  verbose=True)), (&#x27;meta_clfs&#x27;, ColumnTransformer(n_jobs=5,\n",
       "                  transformers=[(&#x27;dna_combined&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     max_iter=1000,\n",
       "                                                                     multi_class=&#x27;multinomial&#x27;,\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver=&#x27;saga&#x27;)),\n",
       "                                                 (&#x27;calibrator&#x27;,\n",
       "                                                  RollingAvgCalibration(min_true_samples=10,\n",
       "                                                                        window_size=&#x27;variable&#x27;)),\n",
       "                                                 (&#x27;fusion_overrider&#x27;,\n",
       "                                                  FusionProbOverrider(_is_fitted=True...\n",
       "                                                              use_first_col=True)),\n",
       "                                                 (&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     max_iter=1000,\n",
       "                                                                     multi_class=&#x27;multinomial&#x27;,\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver=&#x27;saga&#x27;)),\n",
       "                                                 (&#x27;calibrator&#x27;,\n",
       "                                                  RollingAvgCalibration(min_true_samples=10,\n",
       "                                                                        window_size=&#x27;variable&#x27;)),\n",
       "                                                 (&#x27;sex_filter&#x27;,\n",
       "                                                  SexProbFilter())]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x2b4b8ab80&gt;)],\n",
       "                  verbose=True)), (&#x27;prob_combiner&#x27;, ProbCombiner())])</pre></div></div></div><div class=\"sk-serial\"><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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">sub_clfs: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=5,\n",
       "                  transformers=[(&#x27;gen_pos&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;add_noise&#x27;,\n",
       "                                                  NoiseProfileAdder(agg_func=&#x27;sum&#x27;,\n",
       "                                                                    count_ceiling=10000,\n",
       "                                                                    noise_counts=500)),\n",
       "                                                 (&#x27;cos_sim&#x27;,\n",
       "                                                  ProfileSimilarityTransformer(count_ceiling=10000,\n",
       "                                                                               feature_prefix=&#x27;gen_pos.&#x27;)),\n",
       "                                                 (&#x27;non_best_scale&#x27;,\n",
       "                                                  NonBestSimilarityScaler(exponent=5)),\n",
       "                                                 (&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_we...\n",
       "                                                  ProfileSimilarityTransformer(agg_func=&#x27;mean&#x27;,\n",
       "                                                                               feature_prefix=&#x27;alt_sj.&#x27;)),\n",
       "                                                 (&#x27;non_best_scaler&#x27;,\n",
       "                                                  NonBestSimilarityScaler(exponent=2)),\n",
       "                                                 (&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=4,\n",
       "                                                                     class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     max_iter=10000,\n",
       "                                                                     multi_class=&#x27;multinomial&#x27;,\n",
       "                                                                     penalty=&#x27;l1&#x27;,\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver=&#x27;saga&#x27;))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x2b35753d0&gt;)],\n",
       "                  verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label 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\">gen_pos</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x28fa8f850&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NoiseProfileAdder</label><div class=\"sk-toggleable__content\"><pre>NoiseProfileAdder(agg_func=&#x27;sum&#x27;, count_ceiling=10000, noise_counts=500)</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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ProfileSimilarityTransformer</label><div class=\"sk-toggleable__content\"><pre>ProfileSimilarityTransformer(count_ceiling=10000, feature_prefix=&#x27;gen_pos.&#x27;)</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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NonBestSimilarityScaler</label><div class=\"sk-toggleable__content\"><pre>NonBestSimilarityScaler(exponent=5)</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, class_weight=&#x27;balanced&#x27;, max_iter=1000,\n",
       "                   multi_class=&#x27;multinomial&#x27;, penalty=&#x27;l1&#x27;, random_state=0,\n",
       "                   solver=&#x27;saga&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label 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\">snv96</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x28fa8ffd0&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NoiseProfileAdder</label><div class=\"sk-toggleable__content\"><pre>NoiseProfileAdder(noise_counts=500)</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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Log1pTransformer</label><div class=\"sk-toggleable__content\"><pre>Log1pTransformer()</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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, class_weight=&#x27;balanced&#x27;, max_iter=1000,\n",
       "                   multi_class=&#x27;multinomial&#x27;, penalty=&#x27;l1&#x27;, random_state=0,\n",
       "                   solver=&#x27;saga&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">event</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x28fb5c0d0&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><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-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">feature_transform: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;other&#x27;, Chi2FeatureSelector(),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x28fb1d460&gt;),\n",
       "                                (&#x27;sv-tmb&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;log1p&#x27;, Log1pTransformer()),\n",
       "                                                 (&#x27;max_scale&#x27;, MaxScaler())]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x28fb1d970&gt;)],\n",
       "                  verbose_feature_names_out=False)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">other</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x28fb1d460&gt;</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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Chi2FeatureSelector</label><div class=\"sk-toggleable__content\"><pre>Chi2FeatureSelector()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">sv-tmb</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x28fb1d970&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Log1pTransformer</label><div class=\"sk-toggleable__content\"><pre>Log1pTransformer()</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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MaxScaler</label><div class=\"sk-toggleable__content\"><pre>MaxScaler()</pre></div></div></div></div></div></div></div></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-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, class_weight=&#x27;balanced&#x27;, max_iter=1000,\n",
       "                   multi_class=&#x27;multinomial&#x27;, penalty=&#x27;l1&#x27;, random_state=0,\n",
       "                   solver=&#x27;saga&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">gene_exp</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x2b04e4250&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NaRowFilter</label><div class=\"sk-toggleable__content\"><pre>NaRowFilter(pattern=&#x27;^gene_exp&#x27;, use_first_col=True)</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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MaxScaledChi2FeatureSelector</label><div class=\"sk-toggleable__content\"><pre>MaxScaledChi2FeatureSelector()</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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ProfileSimilarityTransformer</label><div class=\"sk-toggleable__content\"><pre>ProfileSimilarityTransformer(agg_func=&#x27;mean&#x27;, feature_prefix=&#x27;gene_exp.&#x27;)</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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NonBestSimilarityScaler</label><div class=\"sk-toggleable__content\"><pre>NonBestSimilarityScaler(exponent=5)</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-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=4, class_weight=&#x27;balanced&#x27;, max_iter=10000,\n",
       "                   multi_class=&#x27;multinomial&#x27;, penalty=&#x27;l1&#x27;, random_state=0,\n",
       "                   solver=&#x27;saga&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">alt_sj</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x2b35753d0&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NaRowFilter</label><div class=\"sk-toggleable__content\"><pre>NaRowFilter(pattern=&#x27;^alt_sj&#x27;, show_warnings=False, use_first_col=True)</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-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MaxScaledChi2FeatureSelector</label><div class=\"sk-toggleable__content\"><pre>MaxScaledChi2FeatureSelector()</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-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ProfileSimilarityTransformer</label><div class=\"sk-toggleable__content\"><pre>ProfileSimilarityTransformer(agg_func=&#x27;mean&#x27;, feature_prefix=&#x27;alt_sj.&#x27;)</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-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NonBestSimilarityScaler</label><div class=\"sk-toggleable__content\"><pre>NonBestSimilarityScaler(exponent=2)</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-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=4, class_weight=&#x27;balanced&#x27;, max_iter=10000,\n",
       "                   multi_class=&#x27;multinomial&#x27;, penalty=&#x27;l1&#x27;, random_state=0,\n",
       "                   solver=&#x27;saga&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><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-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">meta_clfs: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=5,\n",
       "                  transformers=[(&#x27;dna_combined&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     max_iter=1000,\n",
       "                                                                     multi_class=&#x27;multinomial&#x27;,\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver=&#x27;saga&#x27;)),\n",
       "                                                 (&#x27;calibrator&#x27;,\n",
       "                                                  RollingAvgCalibration(min_true_samples=10,\n",
       "                                                                        window_size=&#x27;variable&#x27;)),\n",
       "                                                 (&#x27;fusion_overrider&#x27;,\n",
       "                                                  FusionProbOverrider(_is_fitted=True...\n",
       "                                                              use_first_col=True)),\n",
       "                                                 (&#x27;logistic_regression&#x27;,\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_weight=&#x27;balanced&#x27;,\n",
       "                                                                     max_iter=1000,\n",
       "                                                                     multi_class=&#x27;multinomial&#x27;,\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver=&#x27;saga&#x27;)),\n",
       "                                                 (&#x27;calibrator&#x27;,\n",
       "                                                  RollingAvgCalibration(min_true_samples=10,\n",
       "                                                                        window_size=&#x27;variable&#x27;)),\n",
       "                                                 (&#x27;sex_filter&#x27;,\n",
       "                                                  SexProbFilter())]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x2b4b8ab80&gt;)],\n",
       "                  verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">dna_combined</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x2b4b5cc40&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, class_weight=&#x27;balanced&#x27;, max_iter=1000,\n",
       "                   multi_class=&#x27;multinomial&#x27;, random_state=0, solver=&#x27;saga&#x27;)</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-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RollingAvgCalibration</label><div class=\"sk-toggleable__content\"><pre>RollingAvgCalibration(min_true_samples=10, window_size=&#x27;variable&#x27;)</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-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FusionProbOverrider</label><div class=\"sk-toggleable__content\"><pre>FusionProbOverrider(_is_fitted=True,\n",
       "                    overrides=      feat_prefix  feat_basename  feat_override_value                     target_class                                            comment\n",
       "0   event.fusion.     CBFB_MYH11                    1  Myeloid: Acute myeloid leukemia                      pycuppa, ref data version 017\n",
       "1   event.fusion.   CTNNB1_PLAG1                    1    Head and neck: Salivary gland  Changed to &quot;Salivary gland&quot; only (no &quot;Adenoid ...\n",
       "2   event.fusion.      FUS_DDIT3                    1    Bone/Soft tissue: Liposarcoma                      pycuppa, ref data vers...\n",
       "59  event.fusion.      YAP1_TFE3                    1          Bone/Soft tissue: Other                   java cuppa, ref data version 016\n",
       "60  event.fusion.     YWHAE_IQCK                    1          Bone/Soft tissue: Other                   java cuppa, ref data version 016\n",
       "61  event.fusion.   YWHAE_NUTM2A                    1          Bone/Soft tissue: Other                   java cuppa, ref data version 016\n",
       "62  event.fusion.   YWHAE_NUTM2B                    1          Bone/Soft tissue: Other                   java cuppa, ref data version 016\n",
       "63  event.fusion.    EWSR1_DDIT3                    1    Bone/Soft tissue: Liposarcoma                   java cuppa, ref data version 016\n",
       "\n",
       "[64 rows x 5 columns])</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-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SexProbFilter</label><div class=\"sk-toggleable__content\"><pre>SexProbFilter(show_warnings=True)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rna_combined</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x2b4b8ab80&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NaRowFilter</label><div class=\"sk-toggleable__content\"><pre>NaRowFilter(pattern=&#x27;gene_exp|alt_sj&#x27;, show_warnings=False, use_first_col=True)</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-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, class_weight=&#x27;balanced&#x27;, max_iter=1000,\n",
       "                   multi_class=&#x27;multinomial&#x27;, random_state=0, solver=&#x27;saga&#x27;)</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-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RollingAvgCalibration</label><div class=\"sk-toggleable__content\"><pre>RollingAvgCalibration(min_true_samples=10, window_size=&#x27;variable&#x27;)</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-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SexProbFilter</label><div class=\"sk-toggleable__content\"><pre>SexProbFilter()</pre></div></div></div></div></div></div></div></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-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ProbCombiner</label><div class=\"sk-toggleable__content\"><pre>ProbCombiner()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "CuppaClassifier(steps=[('sub_clfs', ColumnTransformer(n_jobs=5,\n",
       "                  transformers=[('gen_pos',\n",
       "                                 Pipeline(steps=[('add_noise',\n",
       "                                                  NoiseProfileAdder(agg_func='sum',\n",
       "                                                                    count_ceiling=10000,\n",
       "                                                                    noise_counts=500)),\n",
       "                                                 ('cos_sim',\n",
       "                                                  ProfileSimilarityTransformer(count_ceiling=10000,\n",
       "                                                                               feature_prefix='gen_pos.')),\n",
       "                                                 ('non_best_scale',\n",
       "                                                  NonBestSimilarityScaler(exponent=5)),\n",
       "                                                 ('logistic_regression',\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_we...\n",
       "                                                  ProfileSimilarityTransformer(agg_func='mean',\n",
       "                                                                               feature_prefix='alt_sj.')),\n",
       "                                                 ('non_best_scaler',\n",
       "                                                  NonBestSimilarityScaler(exponent=2)),\n",
       "                                                 ('logistic_regression',\n",
       "                                                  LogisticRegression(C=4,\n",
       "                                                                     class_weight='balanced',\n",
       "                                                                     max_iter=10000,\n",
       "                                                                     multi_class='multinomial',\n",
       "                                                                     penalty='l1',\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver='saga'))]),\n",
       "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x2b35753d0>)],\n",
       "                  verbose=True)), ('meta_clfs', ColumnTransformer(n_jobs=5,\n",
       "                  transformers=[('dna_combined',\n",
       "                                 Pipeline(steps=[('logistic_regression',\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_weight='balanced',\n",
       "                                                                     max_iter=1000,\n",
       "                                                                     multi_class='multinomial',\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver='saga')),\n",
       "                                                 ('calibrator',\n",
       "                                                  RollingAvgCalibration(min_true_samples=10,\n",
       "                                                                        window_size='variable')),\n",
       "                                                 ('fusion_overrider',\n",
       "                                                  FusionProbOverrider(_is_fitted=True...\n",
       "                                                              use_first_col=True)),\n",
       "                                                 ('logistic_regression',\n",
       "                                                  LogisticRegression(C=1,\n",
       "                                                                     class_weight='balanced',\n",
       "                                                                     max_iter=1000,\n",
       "                                                                     multi_class='multinomial',\n",
       "                                                                     random_state=0,\n",
       "                                                                     solver='saga')),\n",
       "                                                 ('calibrator',\n",
       "                                                  RollingAvgCalibration(min_true_samples=10,\n",
       "                                                                        window_size='variable')),\n",
       "                                                 ('sex_filter',\n",
       "                                                  SexProbFilter())]),\n",
       "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x2b4b8ab80>)],\n",
       "                  verbose=True)), ('prob_combiner', ProbCombiner())])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = CuppaClassifier.from_file(path=\"cuppa/resources/cuppa_classifier.pickle.gz\")\n",
    "classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5faaa9e6-0213-4183-9e6f-a8df7c37ecaf",
   "metadata": {},
   "source": [
    "## Raw predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d9d176-d949-4a9a-a2c8-4a7238c0c6d6",
   "metadata": {},
   "source": [
    "To save disk space, features that are absent in a sample are excluded from the features tsv file. \n",
    "\n",
    "We can see that our sample has fewer features than those required by the classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f9da58db-fde3-478f-acf4-68fe8d173ddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. features in sample: 49813\n"
     ]
    }
   ],
   "source": [
    "print(\"No. features in sample: \" + str(features.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c9927bb-e124-4f83-9aec-697d6bce3766",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. features in classifier: 79252\n",
      "\n",
      "No. features per feature type:\n",
      "  gen_pos: 6101\n",
      "  snv96: 96\n",
      "  event: 335\n",
      "  sig: 5\n",
      "  gene_exp: 5893\n",
      "  alt_sj: 66822\n"
     ]
    }
   ],
   "source": [
    "print(\"No. features in classifier: \" + str(len(classifier.required_features)))\n",
    "\n",
    "print(\"\\nNo. features per feature type:\")\n",
    "for feature_type, feature_names in classifier.required_features_by_type.items():\n",
    "    print(\"  \" + feature_type + \": \" + str(len(feature_names)) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab8f8be-fffd-458a-8ce1-5b71079f2fff",
   "metadata": {},
   "source": [
    "<br>We therefore need to fill in the missing features that are stored in the classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1f37eab-14e7-4f00-90f0-9f5a28496e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_filled = classifier.fill_missing_cols(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "671e64c6-908d-4ae4-a37d-247b2a3ec4f8",
   "metadata": {},
   "source": [
    "<br>Then we can make the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54faf02c-81a0-457c-9cb6-2042d58a66dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = classifier.predict(features_filled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb01404-7812-4a56-90a6-32b11a8d2757",
   "metadata": {},
   "source": [
    "<br>The prediction output is a dataframe with multi-indexed rows containing various types of information. The columns are the cancer types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "878de92e-d9a2-4d27-9f66-0f115666dd46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Anogenital</th>\n",
       "      <th>Bone/Soft tissue: Cartilaginous neoplasm</th>\n",
       "      <th>Bone/Soft tissue: GIST</th>\n",
       "      <th>Bone/Soft tissue: Leiomyosarcoma</th>\n",
       "      <th>Bone/Soft tissue: Liposarcoma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_id</th>\n",
       "      <th>data_type</th>\n",
       "      <th>clf_group</th>\n",
       "      <th>clf_name</th>\n",
       "      <th>feat_name</th>\n",
       "      <th>feat_value</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">sample_1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">prob</th>\n",
       "      <th>combined</th>\n",
       "      <th>combined</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dna</th>\n",
       "      <th>dna_combined</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.481489e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rna</th>\n",
       "      <th>rna_combined</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">dna</th>\n",
       "      <th>gen_pos</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.000007</td>\n",
       "      <td>7.943869e-08</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snv96</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.026057</td>\n",
       "      <td>2.691297e-06</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.002645</td>\n",
       "      <td>0.001093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">NaN</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">cv_performance</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">rna</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">alt_sj</th>\n",
       "      <th>n_correct</th>\n",
       "      <th>NaN</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_predicted</th>\n",
       "      <th>NaN</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_total</th>\n",
       "      <th>NaN</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.630435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <th>NaN</th>\n",
       "      <td>0.630435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>389 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                        Anogenital  Bone/Soft tissue: Cartilaginous neoplasm  Bone/Soft tissue: GIST  Bone/Soft tissue: Leiomyosarcoma  Bone/Soft tissue: Liposarcoma\n",
       "sample_id data_type      clf_group clf_name     feat_name   feat_value                                                                                                                                               \n",
       "sample_1  prob           combined  combined     NaN         NaN           0.000100                                       NaN                0.000100                          0.000100                       0.000100\n",
       "                         dna       dna_combined NaN         NaN           0.000000                              1.481489e-05                0.000000                          0.000000                       0.000000\n",
       "                         rna       rna_combined NaN         NaN           0.000000                                       NaN                0.000000                          0.000000                       0.000972\n",
       "                         dna       gen_pos      NaN         NaN           0.000007                              7.943869e-08                0.000031                          0.000006                       0.000003\n",
       "                                   snv96        NaN         NaN           0.026057                              2.691297e-06                0.000300                          0.002645                       0.001093\n",
       "...                                                                            ...                                       ...                     ...                               ...                            ...\n",
       "NaN       cv_performance rna       alt_sj       n_correct   NaN          29.000000                              0.000000e+00               27.000000                         17.000000                       3.000000\n",
       "                                                n_predicted NaN          46.000000                              0.000000e+00               27.000000                         20.000000                       3.000000\n",
       "                                                n_total     NaN          46.000000                              0.000000e+00               27.000000                         19.000000                       5.000000\n",
       "                                                precision   NaN           0.630435                                       NaN                1.000000                          0.850000                       1.000000\n",
       "                                                recall      NaN           0.630435                                       NaN                1.000000                          0.894737                       0.600000\n",
       "\n",
       "[389 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.iloc[:,0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61627e88-dc0b-4946-9fe2-c9f68bdeb771",
   "metadata": {},
   "source": [
    "The prediction output contains the following data types:\n",
    "* _prob_: cancer type probabilities\n",
    "* _feat_contrib_: contributions of each feature to each cancer type\n",
    "* _sig_quantile_: mutational signature quantile relative to the cancer type training cohort\n",
    "* _cv_performance_: cross-validation performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb8e9006-d31b-45de-941a-6eced115fd35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['prob', 'feat_contrib', 'sig_quantile', 'cv_performance']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.index.get_level_values(\"data_type\").unique().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4b8837-7a2c-421a-9d64-195605694b72",
   "metadata": {},
   "source": [
    "## Prediction interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6667acee-2572-4368-9da3-a38b07a9839f",
   "metadata": {},
   "source": [
    "The above prediction output does not easily allow us to conclude the cancer type of the sample. The summarize() method returns (in this case) the top 2 cancer type predictions per classifier. \n",
    "\n",
    "We can see that this sample is confidently predicted as a prostate cancer. We can also see that this sample being male and having a TMPRSS2-ERG fusion contributed to the EVENT classifier prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b51607f0-be98-452b-87e0-e92086133904",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>sample_id</th>\n",
       "      <th>clf_group</th>\n",
       "      <th>clf_name</th>\n",
       "      <th>pred_class_1</th>\n",
       "      <th>pred_class_2</th>\n",
       "      <th>pred_prob_1</th>\n",
       "      <th>pred_prob_2</th>\n",
       "      <th>extra_info</th>\n",
       "      <th>extra_info_format</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sample_1</td>\n",
       "      <td>combined</td>\n",
       "      <td>combined</td>\n",
       "      <td>Prostate</td>\n",
       "      <td>Urothelial tract</td>\n",
       "      <td>0.996795</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>dna</td>\n",
       "      <td>dna_combined</td>\n",
       "      <td>Prostate</td>\n",
       "      <td>Bone/Soft tissue: Cartilaginous neoplasm</td>\n",
       "      <td>0.999985</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sample_1</td>\n",
       "      <td>rna</td>\n",
       "      <td>rna_combined</td>\n",
       "      <td>Prostate</td>\n",
       "      <td>NET: Lung</td>\n",
       "      <td>0.995301</td>\n",
       "      <td>0.001875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sample_1</td>\n",
       "      <td>dna</td>\n",
       "      <td>gen_pos</td>\n",
       "      <td>Prostate</td>\n",
       "      <td>Bone/Soft tissue: Other</td>\n",
       "      <td>0.999176</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sample_1</td>\n",
       "      <td>dna</td>\n",
       "      <td>snv96</td>\n",
       "      <td>Prostate</td>\n",
       "      <td>HPB: Pancreas</td>\n",
       "      <td>0.643410</td>\n",
       "      <td>0.083093</td>\n",
       "      <td>AID/APOBEC (SBS2/13)=295.9,4.4%; UV (SBS7)=62....</td>\n",
       "      <td>{sig_name}={count},{percent}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sample_1</td>\n",
       "      <td>dna</td>\n",
       "      <td>event</td>\n",
       "      <td>Prostate</td>\n",
       "      <td>Bone/Soft tissue: Other</td>\n",
       "      <td>0.997120</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>1: fusion.TMPRSS2_ERG=1.0,6.8; trait.is_male=1...</td>\n",
       "      <td>{pred_class_n}: {feat_name}={value},{log_odds}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sample_1</td>\n",
       "      <td>rna</td>\n",
       "      <td>gene_exp</td>\n",
       "      <td>Prostate</td>\n",
       "      <td>Skin: Other</td>\n",
       "      <td>0.998799</td>\n",
       "      <td>0.000891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sample_1</td>\n",
       "      <td>rna</td>\n",
       "      <td>alt_sj</td>\n",
       "      <td>Prostate</td>\n",
       "      <td>Head and neck: Other</td>\n",
       "      <td>0.997567</td>\n",
       "      <td>0.001188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sample_id clf_group      clf_name pred_class_1                              pred_class_2  pred_prob_1  pred_prob_2                                         extra_info                               extra_info_format\n",
       "0  sample_1  combined      combined     Prostate                          Urothelial tract     0.996795     0.000100                                                NaN                                             NaN\n",
       "1  sample_1       dna  dna_combined     Prostate  Bone/Soft tissue: Cartilaginous neoplasm     0.999985     0.000015                                                NaN                                             NaN\n",
       "2  sample_1       rna  rna_combined     Prostate                                 NET: Lung     0.995301     0.001875                                                NaN                                             NaN\n",
       "3  sample_1       dna       gen_pos     Prostate                   Bone/Soft tissue: Other     0.999176     0.000119                                                NaN                                             NaN\n",
       "4  sample_1       dna         snv96     Prostate                             HPB: Pancreas     0.643410     0.083093  AID/APOBEC (SBS2/13)=295.9,4.4%; UV (SBS7)=62....                    {sig_name}={count},{percent}\n",
       "5  sample_1       dna         event     Prostate                   Bone/Soft tissue: Other     0.997120     0.000473  1: fusion.TMPRSS2_ERG=1.0,6.8; trait.is_male=1...  {pred_class_n}: {feat_name}={value},{log_odds}\n",
       "6  sample_1       rna      gene_exp     Prostate                               Skin: Other     0.998799     0.000891                                                NaN                                             NaN\n",
       "7  sample_1       rna        alt_sj     Prostate                      Head and neck: Other     0.997567     0.001188                                                NaN                                             NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_summ = prediction.summarize(top_n_classes=2, show_extra_info=True)\n",
    "pred_summ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e722e100-2535-4a1d-9263-59848b02ffc8",
   "metadata": {},
   "source": [
    "<br>We can also generate a visualization by calling the plot() method. This will create an image as exampled in the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b42e0f8-61e3-4fa5-b647-fa8f2063c14a",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction.plot(plot_path=\"cuppa_vis.png\")"
   ]
  }
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