composite_loss_v0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4ae41277-d444-4ab6-8f38-820906045db3",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Composite loss, version 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e51279f-5a9b-4fb8-8c4d-3c00786d4f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Purpose:\n",
    "    # Inject platform labels into keras.Model class to drive model system\n",
    "    # platform-corrected encoding while preserving cancer-type signal\n",
    "\n",
    "# 1/3 code files\n",
    "    # y_mapping_v0: auto download data, map y targets, structure X matrices\n",
    "    # eval_plots_v0: logistic regression, euclidean distance, and UMAP plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eedde839-6f1a-4c90-bb08-392032d91d7d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e5470edf-2e7a-4646-bec5-292875a2626a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/py310tf210/lib/python3.10/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "/Users/karlberb/miniforge3/envs/py310tf210/lib/python3.10/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "/Users/karlberb/miniforge3/envs/py310tf210/lib/python3.10/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "/Users/karlberb/miniforge3/envs/py310tf210/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/Users/karlberb/miniforge3/envs/py310tf210/lib/python3.10/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, f1_score\n",
    "\n",
    "from scipy.spatial.distance import euclidean\n",
    "from matplotlib.colors import ListedColormap\n",
    "from joypy import joyplot\n",
    "\n",
    "import umap\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44de9288-1479-4d96-b1a1-f23d84bf3bb2",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c5462c5-1749-486c-b663-0f39dd0d4a75",
   "metadata": {},
   "source": [
    "#### Original distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8069805a-f5ec-4764-adb9-5f7dfbb681ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance_from_other_types(sample, features, df): # 'cl_ln_gxp', run 2 <-- WHAT DOES THIS MEAN?\n",
    "    other_types = df[df['cancer_type'] != sample['cancer_type']]\n",
    "    mean_features_other_types = other_types[features].mean()\n",
    "    distance = euclidean(sample[features], mean_features_other_types)\n",
    "    return distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "452ffc01-31f8-4285-ae96-0f643ae62359",
   "metadata": {},
   "outputs": [],
   "source": [
    "del(distance_from_other_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80555215-06bb-4459-af14-71d8de28ed83",
   "metadata": {},
   "source": [
    "#### Data mode specific distance functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ef36a77-e725-41e2-a683-daec342a60a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic regression function hold point\n",
    "    # currently in-line later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "752f76c7-970b-4e27-8a1d-2c1d9c29f065",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Euclidean distance, cancer type\n",
    "def cncr_typ_dist(sample, features, df):\n",
    "    other_types = df[df['cancer_type'] != sample['cancer_type']]\n",
    "    mean_features_other_types = other_types[features].mean()\n",
    "    distance = euclidean(sample[features], mean_features_other_types)\n",
    "    return distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a22e0b1-9873-4d58-9ff4-720bd6032b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Euclicean distance, model type\n",
    "def mdl_typ_dist(sample, features, df):\n",
    "    other_types = df[df['model_type'] != sample['model_type']]\n",
    "    mean_features_other_types = other_types[features].mean()\n",
    "    distance = euclidean(sample[features], mean_features_other_types)\n",
    "    return distance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de5d352-2c89-47be-9e4d-0493f2a3365e",
   "metadata": {},
   "source": [
    "### Updated version of VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "23a0f55e-d055-4de9-98e5-4d77d97deb93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Sampling Layer\n",
    "class Sampling(layers.Layer):\n",
    "    \"\"\"Uses (z_mean, z_log_var) to sample z\"\"\"\n",
    "    def call(self, inputs):\n",
    "        z_mean, z_log_var = inputs\n",
    "        batch = tf.shape(z_mean)[0]\n",
    "        dim = tf.shape(z_mean)[1]\n",
    "        epsilon = tf.random.normal(shape=(batch, dim))\n",
    "        return z_mean + tf.exp(0.5 * z_log_var) * epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9725a0e4-4c19-47d1-8a58-b8d427be0285",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the VAE class\n",
    "class VAE(keras.Model):\n",
    "    def __init__(self, encoder, decoder, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.total_loss_tracker = keras.metrics.Mean(name=\"total_loss\")\n",
    "        self.reconstruction_loss_tracker = keras.metrics.Mean(name=\"reconstruction_loss\")\n",
    "        self.kl_loss_tracker = keras.metrics.Mean(name=\"kl_loss\")\n",
    "        \n",
    "        # Define self.distance_quant\n",
    "        # self.model_system_distance_tracker = keras.metrics.Mean(name=\"distance_loss\") # added\n",
    "    \n",
    "        # Define self.logreg_quant (hold)\n",
    "\n",
    "    @property\n",
    "    def metrics(self):\n",
    "        return [\n",
    "            self.total_loss_tracker,\n",
    "            self.reconstruction_loss_tracker,\n",
    "            self.kl_loss_tracker,\n",
    "            # self.model_system_distance_tracker, # added\n",
    "        ]\n",
    "\n",
    "    # Devel the distance function outside class with selected_df <- last col as labels\n",
    "    # REPLACE THE NESTED LAMBA DISTANCE FUNCTION HERE\n",
    "    # def calc_model_dist(latent_tumor, latent_cell_line): # added\n",
    "    #     return distance_vector\n",
    "    \n",
    "    def train_step(self, data):\n",
    "            # Separate out the labels\n",
    "        # labels = data.iloc[:, -1]  # Assuming labels are the last column\n",
    "        # x = data.iloc[:, :-1]  # Rest of the columns are features\n",
    "        \n",
    "        with tf.GradientTape() as tape:\n",
    "            z_mean, z_log_var, z = self.encoder(data) # original, unlabled X matrix\n",
    "            # z_mean, z_log_var, z = self.encoder(x) # added line replacing data with x\n",
    "            reconstruction = self.decoder(z)\n",
    "            # print(z)\n",
    "            reconstruction_loss = data.shape[1] * keras.losses.binary_crossentropy(data, reconstruction)\n",
    "            # reconstruction_loss = data.shape[1] * keras.losses.binary_crossentropy(x, reconstruction) # added line replacing data with x\n",
    "            \n",
    "            # print(reconstruction_loss)\n",
    "            kl_loss = - 0.5 * tf.reduce_sum(1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var), axis=-1)\n",
    "            # print(kl_loss)\n",
    "            total_loss = reconstruction_loss + kl_loss\n",
    "\n",
    "            # composite_loss = total_loss + distance_loss\n",
    "            # + ctd + mtd <-- cancer and model type distance\n",
    "            # + ctlr + mtlr <-- cancer and model type logistic regression\n",
    "\n",
    "        grads = tape.gradient(total_loss, self.trainable_weights) # original, replaced below\n",
    "        # grads = tape.gradient(composite_loss, self.trainable_weights) # added composite loss to include cancer model system distance calculation\n",
    "        \n",
    "        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n",
    "        self.total_loss_tracker.update_state(total_loss)\n",
    "        self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n",
    "        self.kl_loss_tracker.update_state(kl_loss)\n",
    "        return {\n",
    "            \"loss\": self.total_loss_tracker.result(),\n",
    "            \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n",
    "            \"kl_loss\": self.kl_loss_tracker.result(),\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c363816-6296-4839-9a66-5ca93ec5a79c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build Encoder\n",
    "def build_encoder(feature_dim, latent_dim):\n",
    "    encoder_inputs = keras.Input(shape=(feature_dim,), name=\"input_1\")\n",
    "    x = keras.layers.Dense(latent_dim, kernel_initializer='glorot_uniform', name=\"encoder_dense_1\")(encoder_inputs)\n",
    "    x = keras.layers.BatchNormalization(name=\"batchnorm\")(x)\n",
    "    z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n",
    "    z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n",
    "    z = Sampling()([z_mean, z_log_var])\n",
    "    encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n",
    "    return encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "05e09756-81a2-4764-b381-365b1ee53bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build Decoder\n",
    "def build_decoder(feature_dim, latent_dim):\n",
    "    latent_inputs = keras.Input(shape=(latent_dim,))\n",
    "    x = keras.layers.Dense(feature_dim, kernel_initializer='glorot_uniform', activation='sigmoid')(latent_inputs)\n",
    "    decoder_outputs = x\n",
    "    decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n",
    "    return decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d118b6d5-1444-4980-9984-0302435d5075",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "latent_dim = 50\n",
    "learning_rate = 0.001\n",
    "epochs = 30\n",
    "batch_size = 128"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef647c36-69e8-47e6-bcff-6e4396f7838a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d32675e4-5242-4cfd-b5d6-cf62317b7fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output from y_mapping_v0\n",
    "# toggle and inspect data type object for encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abfd8007-bb17-482f-a039-9f745f0fad68",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_typ_obj = pd.read_csv('../results/pltfrm_cnctns/cl_cp_cnvr_996.tsv',\n",
    "                       sep = '\\t', index_col = 0)\n",
    "d_typ = 'cnvr'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "242c122a-9fc1-41cb-b7c3-ad07769a7593",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_typ_obj = pd.read_csv('../results/pltfrm_cnctns/cl_cp_gexp_998.tsv',\n",
    "                       sep = '\\t', index_col = 0)\n",
    "d_typ = 'gexp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fa0d098-0d0f-4544-bd4e-2aae63ceb78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg_obj = pd.read_csv('../results/pltfrm_cnctns/cl_cp_prot_850.tsv',\n",
    "                       sep = '\\t', index_col = 0)\n",
    "d_typ = 'prot'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d4e1d1d-3c54-48b5-b1a6-64ad0fc33b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gexp'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_typ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11c264f5-ded4-440a-a34a-bd4225ad95b3",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cancer_type</th>\n",
       "      <th>model_type</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>10.0</th>\n",
       "      <th>12.0</th>\n",
       "      <th>13.0</th>\n",
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       "      <th>124906461.0</th>\n",
       "      <th>125316803.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>improve_sample_id</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>108162.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>3.51</td>\n",
       "      <td>12.93</td>\n",
       "      <td>3.13</td>\n",
       "      <td>7.56</td>\n",
       "      <td>2.29</td>\n",
       "      <td>15.58</td>\n",
       "      <td>1.2</td>\n",
       "      <td>11.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.73</td>\n",
       "      <td>10.1</td>\n",
       "      <td>9.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.89</td>\n",
       "    </tr>\n",
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       "<p>1 rows × 28118 columns</p>\n",
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       "                        cancer_type model_type   1.0    2.0   3.0   9.0  10.0  \\\n",
       "improve_sample_id                                                               \n",
       "108162.0           Breast carcinoma      Tumor  3.51  12.93  3.13  7.56  2.29   \n",
       "\n",
       "                    12.0  13.0   14.0  ...  116435287.0  116435289.0  \\\n",
       "improve_sample_id                      ...                             \n",
       "108162.0           15.58   1.2  11.25  ...          0.0          0.0   \n",
       "\n",
       "                   118126072.0  118142757.0  118568804.0  122394733.0  \\\n",
       "improve_sample_id                                                       \n",
       "108162.0                  5.32          0.0          0.0         7.73   \n",
       "\n",
       "                   122405565.0  124905743.0  124906461.0  125316803.0  \n",
       "improve_sample_id                                                      \n",
       "108162.0                  10.1         9.75          0.0         9.89  \n",
       "\n",
       "[1 rows x 28118 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_typ_obj.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "046780f6-a00a-4491-b4f2-944a7bac590e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(998, 28118)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_typ_obj.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "978a0410-8c9d-4cf9-9fc0-7bc947a71429",
   "metadata": {},
   "source": [
    "## Distance template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c094d26b-3dfc-4fe4-a0b4-41541c7d4f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# From front_end_plots_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d85a339-1bb7-4e7d-883b-349016b08121",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'model_type' # grey scale, log reg plot and Euc ridge plot - done, V2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c86a151a-9a74-41d1-a854-92822b92b276",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'cancer_type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f186f2f-3ae7-42af-a6e9-54eac3c0702d",
   "metadata": {},
   "outputs": [],
   "source": [
    "platforms = 'cptac_+_cell_line' # go back later and write this into the logreg plots also"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67263110-722c-408c-8ab1-28478d95bb92",
   "metadata": {},
   "source": [
    "### Euclidean on both quantification types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cae34ce8-1a80-4970-bdd0-3d0306faa747",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoded feature columns\n",
    "feature_columns = d_typ_obj.columns[2:]\n",
    "\n",
    "# Calculate the Euclidean distance for each sample\n",
    "d_typ_obj['mdl_typ_dstncs'] = d_typ_obj.apply(\n",
    "    lambda row: mdl_typ_dist(row, feature_columns, d_typ_obj), axis=1)\n",
    "\n",
    "d_typ_obj['cncr_typ_dstncs'] = d_typ_obj.apply(\n",
    "    lambda row: cncr_typ_dist(row, feature_columns, d_typ_obj), axis=1)\n",
    "\n",
    "# Sort the columns\n",
    "new_cols = ['cancer_type', 'model_type', 'cncr_typ_dstncs', 'mdl_typ_dstncs'] + list(feature_columns)\n",
    "d_typ_obj = d_typ_obj[new_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "eb96421e-1678-4113-94bf-ff1bb36ef101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cancer_type\n",
       "Lung adenocarcinoma                 434.565195\n",
       "Clear cell renal cell carcinoma     394.924917\n",
       "Lung squamous cell carcinoma        389.852333\n",
       "Pancreatic ductal adenocarcinoma    370.950654\n",
       "Colon adenocarcinoma                367.954228\n",
       "Breast carcinoma                    309.188259\n",
       "Ovarian carcinoma                   262.554143\n",
       "Name: cncr_typ_dstncs, dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_distances"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c55e78-78e1-40c3-97e9-fec2d2f2f177",
   "metadata": {},
   "source": [
    "# VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2bd3d4a-f965-4398-8462-9c486b71e6ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(d_typ_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53ed870b-f58b-4a54-97e1-02eb774b0473",
   "metadata": {},
   "outputs": [
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       "      <td>Breast carcinoma</td>\n",
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       "      <td>3.13</td>\n",
       "      <td>7.56</td>\n",
       "      <td>2.29</td>\n",
       "      <td>15.58</td>\n",
       "      <td>1.2</td>\n",
       "      <td>11.25</td>\n",
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       "      <td>5.32</td>\n",
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       "                        cancer_type model_type   1.0    2.0   3.0   9.0  10.0  \\\n",
       "improve_sample_id                                                               \n",
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       "108162.0           15.58   1.2  11.25  ...          0.0          0.0   \n",
       "\n",
       "                   118126072.0  118142757.0  118568804.0  122394733.0  \\\n",
       "improve_sample_id                                                       \n",
       "108162.0                  5.32          0.0          0.0         7.73   \n",
       "\n",
       "                   122405565.0  124905743.0  124906461.0  125316803.0  \n",
       "improve_sample_id                                                      \n",
       "108162.0                  10.1         9.75          0.0         9.89  \n",
       "\n",
       "[1 rows x 28118 columns]"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "d_typ_obj.head(1)"
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  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ecec2be-8471-49b3-9ded-3f57be79a57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# v0 - scale data for baseline ecoding\n",
    "selected_df = d_typ_obj.iloc[:, 2:]\n",
    "feature_count = selected_df.shape[1]\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "selected_df = pd.DataFrame(\n",
    "    scaler.fit_transform(selected_df),\n",
    "    columns=selected_df.columns,\n",
    "    index=selected_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef6e67c3-6be9-4360-a218-a45651004488",
   "metadata": {},
   "outputs": [
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       "      <td>0.710049</td>\n",
       "      <td>0.480799</td>\n",
       "      <td>0.507248</td>\n",
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       "      <td>0.831227</td>\n",
       "      <td>0.472165</td>\n",
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       "<p>1 rows × 28116 columns</p>\n",
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       "                        1.0       2.0       3.0       9.0      10.0     12.0  \\\n",
       "improve_sample_id                                                              \n",
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       "                   116435289.0  118126072.0  118142757.0  118568804.0  \\\n",
       "improve_sample_id                                                       \n",
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       "                   122394733.0  122405565.0  124905743.0  124906461.0  \\\n",
       "improve_sample_id                                                       \n",
       "108162.0               0.76459      0.75073     0.740881          0.0   \n",
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "selected_df.head(1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5e49b4a9-b838-4eb4-b227-8ff7305bfc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(998, 28116)"
      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "selected_df.shape"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b99649e4-6193-4201-aa23-f0c5d0947e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28116"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e343dc12-c014-4931-b9c0-b96a846279da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4451ce3-f8da-41cf-bbe9-d9c9cbab1320",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 2 # devel toggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "32a3a80e-ec8f-421d-a187-5e65ae2e69cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build VAE\n",
    "encoder = build_encoder(feature_count, latent_dim) # feat count set above, lat dim is a var\n",
    "decoder = build_decoder(feature_count, latent_dim)\n",
    "vae = VAE(encoder, decoder)\n",
    "vae.compile(optimizer=keras.optimizers.Adam(learning_rate=learning_rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae58b32c-7ec9-4e82-9cc5-d8be7585cdd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-12 11:10:34.087333: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 1s 27ms/step - loss: 371766385270223.8125 - reconstruction_loss: 920698944.0000 - kl_loss: 641396591558656.0000\n",
      "Epoch 2/30\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 136750535759189.3281 - reconstruction_loss: 839251328.0000 - kl_loss: 204732333293568.0000\n",
      "Epoch 3/30\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 27800353024682.6680 - reconstruction_loss: 246630096.0000 - kl_loss: 16321281523712.0000\n",
      "Epoch 4/30\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 14374694684.4444 - reconstruction_loss: 2247244.0000 - kl_loss: 7257714688.0000\n",
      "Epoch 5/30\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 74082554.2222 - reconstruction_loss: 237688.1719 - kl_loss: 50079992.0000\n",
      "Epoch 6/30\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 13141247.0000 - reconstruction_loss: 133126.7031 - kl_loss: 12189459.0000\n",
      "Epoch 7/30\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 9855263.2222 - reconstruction_loss: 119197.5547 - kl_loss: 9870015.0000\n",
      "Epoch 8/30\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 10132198.3333 - reconstruction_loss: 111102.2109 - kl_loss: 9628685.0000\n",
      "Epoch 9/30\n",
      "8/8 [==============================] - 0s 31ms/step - loss: 9317774.2222 - reconstruction_loss: 104386.9922 - kl_loss: 9608533.0000\n",
      "Epoch 10/30\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 9843606.1111 - reconstruction_loss: 98697.3281 - kl_loss: 9605152.0000\n",
      "Epoch 11/30\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 9785778.6667 - reconstruction_loss: 93322.7578 - kl_loss: 9592123.0000\n",
      "Epoch 12/30\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 9638108.0000 - reconstruction_loss: 89653.2422 - kl_loss: 9572115.0000\n",
      "Epoch 13/30\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 9451811.1111 - reconstruction_loss: 85735.9141 - kl_loss: 9548014.0000\n",
      "Epoch 14/30\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 9467222.4444 - reconstruction_loss: 85357.8750 - kl_loss: 9521484.0000\n",
      "Epoch 15/30\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 9534634.3333 - reconstruction_loss: 76746.8203 - kl_loss: 9493475.0000\n",
      "Epoch 16/30\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 9798063.8889 - reconstruction_loss: 74285.2812 - kl_loss: 9463830.0000\n",
      "Epoch 17/30\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 9458185.8889 - reconstruction_loss: 67608.7734 - kl_loss: 9433078.0000\n",
      "Epoch 18/30\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 9248240.1111 - reconstruction_loss: 65337.5469 - kl_loss: 9401452.0000\n",
      "Epoch 19/30\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 9722596.7778 - reconstruction_loss: 60502.3281 - kl_loss: 9370055.0000\n",
      "Epoch 20/30\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 9224872.8889 - reconstruction_loss: 61367.8945 - kl_loss: 9336758.0000\n",
      "Epoch 21/30\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 9813498.2222 - reconstruction_loss: 60429.1836 - kl_loss: 9304177.0000\n",
      "Epoch 22/30\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 9424641.0000 - reconstruction_loss: 55998.1836 - kl_loss: 9269786.0000\n",
      "Epoch 23/30\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 9608916.5556 - reconstruction_loss: 53127.6992 - kl_loss: 9235840.0000\n",
      "Epoch 24/30\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 9301888.2222 - reconstruction_loss: 51473.2148 - kl_loss: 9200125.0000\n",
      "Epoch 25/30\n",
      "8/8 [==============================] - 0s 24ms/step - loss: 9194899.5556 - reconstruction_loss: 49339.7109 - kl_loss: 9164906.0000\n",
      "Epoch 26/30\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 9403900.7778 - reconstruction_loss: 47430.6055 - kl_loss: 9129252.0000\n",
      "Epoch 27/30\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 8753751.4444 - reconstruction_loss: 45959.8711 - kl_loss: 9092274.0000\n",
      "Epoch 28/30\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 8898785.7778 - reconstruction_loss: 43528.1953 - kl_loss: 9055483.0000\n",
      "Epoch 29/30\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 9109846.5556 - reconstruction_loss: 42476.3594 - kl_loss: 9018680.0000\n",
      "Epoch 30/30\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 8965974.7778 - reconstruction_loss: 41880.7852 - kl_loss: 8980710.0000\n"
     ]
    }
   ],
   "source": [
    "history = vae.fit(selected_df, epochs=epochs, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e47519-b4f7-4b75-b404-9e19328675a3",
   "metadata": {},
   "source": [
    "## Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4abdecbe-489a-4ffb-98e2-29f9264a8ad3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "z_mean, z_log_var, z = encoder.predict(selected_df)\n",
    "latent_df = pd.DataFrame(z, index=selected_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "817f31fe-eb02-4d1f-ae22-8b3454d43f7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>4</th>\n",
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       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>improve_sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108162.0</th>\n",
       "      <td>1.326553</td>\n",
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       "      <td>7.309916</td>\n",
       "      <td>-3.748562</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.798430</td>\n",
       "      <td>7.788421</td>\n",
       "      <td>-88.398621</td>\n",
       "      <td>4.322221</td>\n",
       "      <td>6.696524</td>\n",
       "      <td>-0.093051</td>\n",
       "      <td>11.092309</td>\n",
       "      <td>0.053332</td>\n",
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       "    <tr>\n",
       "      <th>108163.0</th>\n",
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       "      <td>8.310746</td>\n",
       "      <td>6.530994</td>\n",
       "      <td>30.143299</td>\n",
       "      <td>91.374123</td>\n",
       "      <td>3.824846</td>\n",
       "      <td>7.248744</td>\n",
       "      <td>-3.091958</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.762000</td>\n",
       "      <td>7.905495</td>\n",
       "      <td>8.334447</td>\n",
       "      <td>4.837840</td>\n",
       "      <td>6.452430</td>\n",
       "      <td>-0.283992</td>\n",
       "      <td>14.849967</td>\n",
       "      <td>0.147493</td>\n",
       "      <td>-6.409623</td>\n",
       "      <td>2.794944</td>\n",
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       "    <tr>\n",
       "      <th>108164.0</th>\n",
       "      <td>1.279919</td>\n",
       "      <td>4.352505</td>\n",
       "      <td>-7.499235</td>\n",
       "      <td>8.625391</td>\n",
       "      <td>6.906949</td>\n",
       "      <td>4.601037</td>\n",
       "      <td>-316.553558</td>\n",
       "      <td>3.714470</td>\n",
       "      <td>7.956074</td>\n",
       "      <td>-3.493851</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.930818</td>\n",
       "      <td>7.702989</td>\n",
       "      <td>58.057083</td>\n",
       "      <td>3.893553</td>\n",
       "      <td>6.481912</td>\n",
       "      <td>-1.094937</td>\n",
       "      <td>-11.830375</td>\n",
       "      <td>0.056609</td>\n",
       "      <td>-6.300360</td>\n",
       "      <td>2.645476</td>\n",
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       "    <tr>\n",
       "      <th>108165.0</th>\n",
       "      <td>0.889113</td>\n",
       "      <td>5.782341</td>\n",
       "      <td>-7.605585</td>\n",
       "      <td>8.455565</td>\n",
       "      <td>6.047612</td>\n",
       "      <td>32.743095</td>\n",
       "      <td>-356.473297</td>\n",
       "      <td>3.966716</td>\n",
       "      <td>7.035977</td>\n",
       "      <td>-3.436546</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.563261</td>\n",
       "      <td>7.854872</td>\n",
       "      <td>-27.038740</td>\n",
       "      <td>4.174204</td>\n",
       "      <td>6.099274</td>\n",
       "      <td>-0.814608</td>\n",
       "      <td>2.700947</td>\n",
       "      <td>0.105228</td>\n",
       "      <td>-6.193448</td>\n",
       "      <td>2.811770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108166.0</th>\n",
       "      <td>1.070891</td>\n",
       "      <td>6.027085</td>\n",
       "      <td>-7.728689</td>\n",
       "      <td>8.030391</td>\n",
       "      <td>7.910209</td>\n",
       "      <td>-13.602486</td>\n",
       "      <td>-964.904236</td>\n",
       "      <td>3.980197</td>\n",
       "      <td>7.204582</td>\n",
       "      <td>-3.482418</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.682076</td>\n",
       "      <td>7.879158</td>\n",
       "      <td>-23.260571</td>\n",
       "      <td>4.663965</td>\n",
       "      <td>6.321442</td>\n",
       "      <td>-0.166740</td>\n",
       "      <td>-3.361066</td>\n",
       "      <td>-0.051339</td>\n",
       "      <td>-6.440380</td>\n",
       "      <td>2.725153</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1367.0</th>\n",
       "      <td>0.845131</td>\n",
       "      <td>2.309035</td>\n",
       "      <td>-1.997595</td>\n",
       "      <td>3.286545</td>\n",
       "      <td>1.004248</td>\n",
       "      <td>7.123163</td>\n",
       "      <td>-6.235314</td>\n",
       "      <td>0.719984</td>\n",
       "      <td>1.323244</td>\n",
       "      <td>-0.899717</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.374199</td>\n",
       "      <td>2.434545</td>\n",
       "      <td>1.759684</td>\n",
       "      <td>1.600800</td>\n",
       "      <td>2.315055</td>\n",
       "      <td>-0.298207</td>\n",
       "      <td>-0.699703</td>\n",
       "      <td>-0.075939</td>\n",
       "      <td>-2.199332</td>\n",
       "      <td>0.957273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1391.0</th>\n",
       "      <td>0.626032</td>\n",
       "      <td>1.654522</td>\n",
       "      <td>-2.640382</td>\n",
       "      <td>2.359541</td>\n",
       "      <td>0.372012</td>\n",
       "      <td>2.020311</td>\n",
       "      <td>-3.035284</td>\n",
       "      <td>0.957688</td>\n",
       "      <td>1.542826</td>\n",
       "      <td>-0.946291</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.067393</td>\n",
       "      <td>2.247756</td>\n",
       "      <td>-4.161005</td>\n",
       "      <td>2.727655</td>\n",
       "      <td>1.199200</td>\n",
       "      <td>1.264145</td>\n",
       "      <td>-1.996476</td>\n",
       "      <td>-0.298196</td>\n",
       "      <td>-2.242419</td>\n",
       "      <td>0.946823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393.0</th>\n",
       "      <td>0.485934</td>\n",
       "      <td>1.304489</td>\n",
       "      <td>-1.756690</td>\n",
       "      <td>1.986058</td>\n",
       "      <td>1.521353</td>\n",
       "      <td>-1.575249</td>\n",
       "      <td>-5.431246</td>\n",
       "      <td>1.192776</td>\n",
       "      <td>2.186990</td>\n",
       "      <td>-1.205116</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051159</td>\n",
       "      <td>1.522836</td>\n",
       "      <td>-0.229122</td>\n",
       "      <td>0.198559</td>\n",
       "      <td>1.390555</td>\n",
       "      <td>1.909260</td>\n",
       "      <td>-2.034043</td>\n",
       "      <td>-0.147967</td>\n",
       "      <td>-1.932236</td>\n",
       "      <td>0.773807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477.0</th>\n",
       "      <td>0.445183</td>\n",
       "      <td>2.964250</td>\n",
       "      <td>-2.096198</td>\n",
       "      <td>3.080070</td>\n",
       "      <td>2.647529</td>\n",
       "      <td>0.777457</td>\n",
       "      <td>1.944757</td>\n",
       "      <td>1.147361</td>\n",
       "      <td>0.888714</td>\n",
       "      <td>-0.716609</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.292312</td>\n",
       "      <td>2.139244</td>\n",
       "      <td>-2.227612</td>\n",
       "      <td>2.406773</td>\n",
       "      <td>3.744509</td>\n",
       "      <td>1.036822</td>\n",
       "      <td>-4.109119</td>\n",
       "      <td>-0.224224</td>\n",
       "      <td>-2.233763</td>\n",
       "      <td>1.034476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478.0</th>\n",
       "      <td>0.693807</td>\n",
       "      <td>2.819780</td>\n",
       "      <td>-2.211166</td>\n",
       "      <td>2.993051</td>\n",
       "      <td>1.196503</td>\n",
       "      <td>0.557278</td>\n",
       "      <td>10.553073</td>\n",
       "      <td>0.861107</td>\n",
       "      <td>3.173146</td>\n",
       "      <td>-0.865182</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.207565</td>\n",
       "      <td>2.234147</td>\n",
       "      <td>1.932939</td>\n",
       "      <td>2.755659</td>\n",
       "      <td>2.835176</td>\n",
       "      <td>2.484631</td>\n",
       "      <td>-2.749625</td>\n",
       "      <td>-0.107767</td>\n",
       "      <td>-2.066291</td>\n",
       "      <td>0.989179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>998 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0         1         2         3         4   \\\n",
       "improve_sample_id                                                     \n",
       "108162.0           1.326553  5.393672 -7.761674  7.973125  6.565668   \n",
       "108163.0           0.932773  5.935802 -7.827321  8.310746  6.530994   \n",
       "108164.0           1.279919  4.352505 -7.499235  8.625391  6.906949   \n",
       "108165.0           0.889113  5.782341 -7.605585  8.455565  6.047612   \n",
       "108166.0           1.070891  6.027085 -7.728689  8.030391  7.910209   \n",
       "...                     ...       ...       ...       ...       ...   \n",
       "1367.0             0.845131  2.309035 -1.997595  3.286545  1.004248   \n",
       "1391.0             0.626032  1.654522 -2.640382  2.359541  0.372012   \n",
       "1393.0             0.485934  1.304489 -1.756690  1.986058  1.521353   \n",
       "1477.0             0.445183  2.964250 -2.096198  3.080070  2.647529   \n",
       "1478.0             0.693807  2.819780 -2.211166  2.993051  1.196503   \n",
       "\n",
       "                          5           6         7         8         9   ...  \\\n",
       "improve_sample_id                                                       ...   \n",
       "108162.0            0.205500 -320.040924  4.123513  7.309916 -3.748562  ...   \n",
       "108163.0           30.143299   91.374123  3.824846  7.248744 -3.091958  ...   \n",
       "108164.0            4.601037 -316.553558  3.714470  7.956074 -3.493851  ...   \n",
       "108165.0           32.743095 -356.473297  3.966716  7.035977 -3.436546  ...   \n",
       "108166.0          -13.602486 -964.904236  3.980197  7.204582 -3.482418  ...   \n",
       "...                      ...         ...       ...       ...       ...  ...   \n",
       "1367.0              7.123163   -6.235314  0.719984  1.323244 -0.899717  ...   \n",
       "1391.0              2.020311   -3.035284  0.957688  1.542826 -0.946291  ...   \n",
       "1393.0             -1.575249   -5.431246  1.192776  2.186990 -1.205116  ...   \n",
       "1477.0              0.777457    1.944757  1.147361  0.888714 -0.716609  ...   \n",
       "1478.0              0.557278   10.553073  0.861107  3.173146 -0.865182  ...   \n",
       "\n",
       "                         40        41         42        43        44  \\\n",
       "improve_sample_id                                                      \n",
       "108162.0          -0.798430  7.788421 -88.398621  4.322221  6.696524   \n",
       "108163.0          -0.762000  7.905495   8.334447  4.837840  6.452430   \n",
       "108164.0          -0.930818  7.702989  58.057083  3.893553  6.481912   \n",
       "108165.0          -0.563261  7.854872 -27.038740  4.174204  6.099274   \n",
       "108166.0          -0.682076  7.879158 -23.260571  4.663965  6.321442   \n",
       "...                     ...       ...        ...       ...       ...   \n",
       "1367.0            -0.374199  2.434545   1.759684  1.600800  2.315055   \n",
       "1391.0            -0.067393  2.247756  -4.161005  2.727655  1.199200   \n",
       "1393.0            -0.051159  1.522836  -0.229122  0.198559  1.390555   \n",
       "1477.0            -0.292312  2.139244  -2.227612  2.406773  3.744509   \n",
       "1478.0            -0.207565  2.234147   1.932939  2.755659  2.835176   \n",
       "\n",
       "                         45         46        47        48        49  \n",
       "improve_sample_id                                                     \n",
       "108162.0          -0.093051  11.092309  0.053332 -6.605824  2.623200  \n",
       "108163.0          -0.283992  14.849967  0.147493 -6.409623  2.794944  \n",
       "108164.0          -1.094937 -11.830375  0.056609 -6.300360  2.645476  \n",
       "108165.0          -0.814608   2.700947  0.105228 -6.193448  2.811770  \n",
       "108166.0          -0.166740  -3.361066 -0.051339 -6.440380  2.725153  \n",
       "...                     ...        ...       ...       ...       ...  \n",
       "1367.0            -0.298207  -0.699703 -0.075939 -2.199332  0.957273  \n",
       "1391.0             1.264145  -1.996476 -0.298196 -2.242419  0.946823  \n",
       "1393.0             1.909260  -2.034043 -0.147967 -1.932236  0.773807  \n",
       "1477.0             1.036822  -4.109119 -0.224224 -2.233763  1.034476  \n",
       "1478.0             2.484631  -2.749625 -0.107767 -2.066291  0.989179  \n",
       "\n",
       "[998 rows x 50 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "18660b37-c454-4ab8-a3fd-71656f1b2d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Post class label injection template\n",
    "latent_df.insert(\n",
    "    loc=0, column='cancer_type',\n",
    "    value=cl_cp_cnvr_996['cancer_type'])\n",
    "latent_df.insert(\n",
    "    loc=1, column='model_type',\n",
    "    value=cl_cp_cnvr_996['model_type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b297e739-3931-43cd-a6c7-2a897a266879",
   "metadata": {},
   "source": [
    "### Loss Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "782bd203-8f80-4ce8-a140-7343c26964ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_loss(loss_dict, latent_dim, selected_df,\n",
    "              epochs, platforms, data_type, plot_title):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(loss_dict['cycle_n'], marker='o',\n",
    "             linestyle='-', color='b')\n",
    "    plt.grid(True, linestyle='--', alpha=0.6)\n",
    "    plt.title(plot_title+' '+data_type+' VAE loss',\n",
    "              fontsize=16, fontweight='bold')\n",
    "    plt.xlabel('Epoch', fontsize=14)\n",
    "    plt.ylabel('Loss', fontsize=14)\n",
    "    plt.annotate(\n",
    "        'Samples: {}'.format(\n",
    "            len(selected_df))+'\\nLatent dim: '+str(\n",
    "            latent_dim)+'\\nRaw features: {}'.format(\n",
    "            len(selected_df.columns)), \n",
    "                 xy=(0.7, 0.3), xycoords='axes fraction', \n",
    "                 bbox=dict(boxstyle='round, pad=0.5',\n",
    "                           fc='white', ec='black'),\n",
    "                 fontsize=12)\n",
    "    plt.savefig(\n",
    "        '../results_composite_loss_v0/'+platforms+'_'+data_name+'_dim.'+str(\n",
    "            latent_dim)+'_epochs.'+str(\n",
    "            epochs)+'_vae_loss.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e652dc43-baa8-43c8-a29d-c4f8fc690f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_dict = {}\n",
    "loss_dict['cycle_n'] = history.history['loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2d3f165a-664e-4791-8e03-5ea9d9fca38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gexp'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_typ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3feea0b5-6520-460c-8a4e-e7ebe6dfac8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_type = 'gene expression'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4e71ccb7-6fcf-45cd-bff3-24314555c54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_name = 'gene_expression'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1da970dc-a250-4dc8-9b66-7e19914319d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_title = 'Cell line + CPTAC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eae8ae9b-3e79-4dfd-90fc-34d90ecf648a",
   "metadata": {},
   "outputs": [],
   "source": [
    "platforms = 'cell_line+cptac'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c06e4f86-35f8-4012-9b59-aeaf8fe47ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(loss_dict, latent_dim, selected_df,\n",
    "          epochs, platforms, data_type, plot_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "f0354f20-28af-448a-a3b8-5fbf75348ac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "encdng_sts = 'enc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "9106a29c-972f-4c63-b877-968580ba7db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'model_type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "65123779-354d-41a5-abcc-c0b3f4c74108",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'cancer_type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "37ad30f3-cf2c-4443-9438-938359d67144",
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_reg(log_reg_obj, mode):\n",
    "    col_X_strt = 2\n",
    "    f1_stor_frm = pd.DataFrame()\n",
    "    \n",
    "    for i in list(range(0, 5)): # Doing a random split five times\n",
    "        print(i)\n",
    "    \n",
    "        trn = log_reg_obj.sample(round(len(log_reg_obj) * .8))\n",
    "        tst = log_reg_obj.loc[~log_reg_obj.index.isin(trn.index)]\n",
    "    \n",
    "        X_trn = trn.iloc[:, col_X_strt:]\n",
    "        X_tst = tst.iloc[:, col_X_strt:]\n",
    "        \n",
    "        y_trn = trn[mode] # get the y targets\n",
    "        y_tst = tst[mode]\n",
    "        \n",
    "        clf = LogisticRegression().fit(X_trn, y_trn)\n",
    "        y_pred = clf.predict(X_tst)\n",
    "        \n",
    "        f1_by_class = f1_score(y_tst, y_pred, average=None)\n",
    "        f1_df = pd.DataFrame({'Label': list(y_tst.unique()),\n",
    "                              'F1_Score': f1_by_class})\n",
    "        f1_stor_frm = pd.concat([f1_stor_frm, f1_df], axis = 0)\n",
    "    return f1_stor_frm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "6edacb66-a434-4bfb-af87-c8c2ad47e602",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/py310tf210/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/karlberb/miniforge3/envs/py310tf210/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/py310tf210/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/karlberb/miniforge3/envs/py310tf210/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/py310tf210/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "f1_stor_frm = log_reg(latent_df, mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "e14967c1-d862-43fb-9c00-5717d140ebd8",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>F1_Score</th>\n",
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       "      <td>cell line</td>\n",
       "      <td>0.963855</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Label  F1_Score\n",
       "0      Tumor  1.000000\n",
       "1  cell line  1.000000\n",
       "0      Tumor  1.000000\n",
       "1  cell line  1.000000\n",
       "0      Tumor  1.000000\n",
       "1  cell line  1.000000\n",
       "0      Tumor  0.990476\n",
       "1  cell line  0.963855\n",
       "0      Tumor  1.000000\n",
       "1  cell line  1.000000"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_stor_frm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "1bb3a0b0-ceea-491f-99d6-9b9fe359f87d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tcga_colors_df = pd.read_csv('/Users/karlberb/Documents/work/archive/tcga_colors.tsv', sep='\\t',\n",
    "                            index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "117e95dd-e08d-4ef7-9077-3ede3391fa79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write / read logreg f1 score frame here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14754b26-cc8d-4072-afbe-5258705bc979",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Log Reg model mode function\n",
    "def lgrg_plt(f1_stor_frm)\n",
    "    plt.figure(figsize=(15, 8))\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    sns.set(font_scale=1.5)\n",
    "    \n",
    "    sns.barplot(x='Label', y='F1_Score', data=f1_stor_frm, palette=['#666666', '#999999'],\n",
    "               errorbar=None)\n",
    "    sns.swarmplot(x='Label', y='F1_Score', data=f1_stor_frm, color='#333333', size=12)\n",
    "    \n",
    "    plt.title('Model type logistic regression', fontsize=24)\n",
    "    plt.xlabel('Model Type', fontsize=20)\n",
    "    plt.ylabel('F1 Score', fontsize=20)\n",
    "    \n",
    "    plt.grid(color='grey', linestyle='-', linewidth=0.25, alpha=0.5)\n",
    "    plt.savefig('../results_encoded/plat.'+plat+'_'+data_name+'_dim.'+str(\n",
    "                latent_dim)+'_epochs.'+str(\n",
    "                epochs)+'_mode.'+mode+'.png', bbox_inches='tight')\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18346da3-6481-4aba-b58e-8dd4a09bc318",
   "metadata": {},
   "source": [
    "#### LogReg plot model type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "54b1e593-781e-46ff-9b93-004c11661145",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set(font_scale=1.5)\n",
    "\n",
    "sns.barplot(x='Label', y='F1_Score', data=f1_stor_frm, palette=['#666666', '#999999'],\n",
    "           errorbar=None)\n",
    "sns.swarmplot(x='Label', y='F1_Score', data=f1_stor_frm, color='#333333', size=12)\n",
    "\n",
    "plt.title('Model type logistic regression', fontsize=24)\n",
    "plt.xlabel('Model Type', fontsize=20)\n",
    "plt.ylabel('F1 Score', fontsize=20)\n",
    "\n",
    "plt.grid(color='grey', linestyle='-', linewidth=0.25, alpha=0.5)\n",
    "plt.savefig('../results_encoded/plat.'+plat+'_'+data_name+'_dim.'+str(\n",
    "            latent_dim)+'_epochs.'+str(\n",
    "            epochs)+'_mode.'+mode+'.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9debe2d0-4387-41f4-96cf-3b204aaa14c7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### LogReg plot cancer type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "b146e041-0ddd-4260-a1cd-9d1dbb49c3c1",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "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>cohort_color</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tcga_cohorts</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACC</th>\n",
       "      <td>#C1A72F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLCA</th>\n",
       "      <td>#FAD2D9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRCA</th>\n",
       "      <td>#ED2891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CESC</th>\n",
       "      <td>#F6B667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COADREAD</th>\n",
       "      <td>#9EDDF9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESCC</th>\n",
       "      <td>#007EB5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEA</th>\n",
       "      <td>#909485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HNSC</th>\n",
       "      <td>#97D1A9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIRCKICH</th>\n",
       "      <td>#F8AFB3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIRP</th>\n",
       "      <td>#EA7075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGGGBM</th>\n",
       "      <td>#D49DC7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIHCCHOL</th>\n",
       "      <td>#CACCDB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUAD</th>\n",
       "      <td>#D3C3E0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUSC</th>\n",
       "      <td>#A084BD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESO</th>\n",
       "      <td>#542C88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OV</th>\n",
       "      <td>#D97D25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAAD</th>\n",
       "      <td>#6E7BA2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCPG</th>\n",
       "      <td>#E8C51D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRAD</th>\n",
       "      <td>#7E1918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARC</th>\n",
       "      <td>#00A99D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKCM</th>\n",
       "      <td>#BBD642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGCT</th>\n",
       "      <td>#BE1E2D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THCA</th>\n",
       "      <td>#F9ED32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THYM</th>\n",
       "      <td>#CEAC8F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UCEC</th>\n",
       "      <td>#FBE3C7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UVM</th>\n",
       "      <td>#009444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cohort_color\n",
       "tcga_cohorts             \n",
       "ACC               #C1A72F\n",
       "BLCA              #FAD2D9\n",
       "BRCA              #ED2891\n",
       "CESC              #F6B667\n",
       "COADREAD          #9EDDF9\n",
       "ESCC              #007EB5\n",
       "GEA               #909485\n",
       "HNSC              #97D1A9\n",
       "KIRCKICH          #F8AFB3\n",
       "KIRP              #EA7075\n",
       "LGGGBM            #D49DC7\n",
       "LIHCCHOL          #CACCDB\n",
       "LUAD              #D3C3E0\n",
       "LUSC              #A084BD\n",
       "MESO              #542C88\n",
       "OV                #D97D25\n",
       "PAAD              #6E7BA2\n",
       "PCPG              #E8C51D\n",
       "PRAD              #7E1918\n",
       "SARC              #00A99D\n",
       "SKCM              #BBD642\n",
       "TGCT              #BE1E2D\n",
       "THCA              #F9ED32\n",
       "THYM              #CEAC8F\n",
       "UCEC              #FBE3C7\n",
       "UVM               #009444"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tcga_colors_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "4000e9c4-4a48-43e5-b1e8-a059c3cf638b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cell line': 'cell line', 'Tumor': 'Tumor'}"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abbreviation_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "ed3141aa-134d-40d9-97a3-0fe07e584aeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "abbreviation_mapping = {\n",
    "    'Clear cell renal cell carcinoma': 'KIRCKICH', # combined chromophobe\n",
    "    'Colon adenocarcinoma': 'COADREAD', # combined rectal adeno\n",
    "    'Pancreatic ductal adenocarcinoma': 'PAAD',\n",
    "    'Breast carcinoma': 'BRCA',\n",
    "    'Glioblastoma': 'LGGGBM', # combined low grade glioma\n",
    "    'Lung squamous cell carcinoma': 'LUSC',\n",
    "    'Lung adenocarcinoma': 'LUAD',\n",
    "    'Endometrial carcinoma': 'UCEC',\n",
    "    'Head and neck squamous cell carcinoma': 'HNSC',\n",
    "    'Ovarian carcinoma': 'OV'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "bd045dda-70a5-4d68-8ba0-60e88125a587",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "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>Label</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.122449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung squamous cell carcinoma</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.338983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ovarian carcinoma</td>\n",
       "      <td>0.241379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>0.162162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.156863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.145455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung squamous cell carcinoma</td>\n",
       "      <td>0.054054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.243243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ovarian carcinoma</td>\n",
       "      <td>0.215385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>0.146341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.237288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung squamous cell carcinoma</td>\n",
       "      <td>0.095238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.367816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ovarian carcinoma</td>\n",
       "      <td>0.215385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.093023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung squamous cell carcinoma</td>\n",
       "      <td>0.038462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ovarian carcinoma</td>\n",
       "      <td>0.238806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.070175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung squamous cell carcinoma</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.340909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ovarian carcinoma</td>\n",
       "      <td>0.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>0.213333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Label  F1_Score\n",
       "0                  Breast carcinoma  0.122449\n",
       "1              Colon adenocarcinoma  0.108108\n",
       "2      Lung squamous cell carcinoma  0.050000\n",
       "3               Lung adenocarcinoma  0.338983\n",
       "4                 Ovarian carcinoma  0.241379\n",
       "5  Pancreatic ductal adenocarcinoma  0.090909\n",
       "6   Clear cell renal cell carcinoma  0.162162\n",
       "0                  Breast carcinoma  0.156863\n",
       "1              Colon adenocarcinoma  0.145455\n",
       "2      Lung squamous cell carcinoma  0.054054\n",
       "3               Lung adenocarcinoma  0.243243\n",
       "4                 Ovarian carcinoma  0.215385\n",
       "5  Pancreatic ductal adenocarcinoma  0.058824\n",
       "6   Clear cell renal cell carcinoma  0.146341\n",
       "0                  Breast carcinoma  0.237288\n",
       "1              Colon adenocarcinoma  0.160000\n",
       "2      Lung squamous cell carcinoma  0.095238\n",
       "3               Lung adenocarcinoma  0.367816\n",
       "4                 Ovarian carcinoma  0.215385\n",
       "5  Pancreatic ductal adenocarcinoma  0.080000\n",
       "6   Clear cell renal cell carcinoma  0.200000\n",
       "0                  Breast carcinoma  0.076923\n",
       "1              Colon adenocarcinoma  0.093023\n",
       "2      Lung squamous cell carcinoma  0.038462\n",
       "3               Lung adenocarcinoma  0.190476\n",
       "4                 Ovarian carcinoma  0.238806\n",
       "5  Pancreatic ductal adenocarcinoma  0.000000\n",
       "6   Clear cell renal cell carcinoma  0.150000\n",
       "0                  Breast carcinoma  0.070175\n",
       "1              Colon adenocarcinoma  0.153846\n",
       "2      Lung squamous cell carcinoma  0.076923\n",
       "3               Lung adenocarcinoma  0.340909\n",
       "4                 Ovarian carcinoma  0.370370\n",
       "5  Pancreatic ductal adenocarcinoma  0.000000\n",
       "6   Clear cell renal cell carcinoma  0.213333"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_stor_frm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "e2b8b5c1-f85c-4659-ac99-436ad7f240bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Breast carcinoma', 'Colon adenocarcinoma',\n",
       "       'Lung squamous cell carcinoma', 'Lung adenocarcinoma',\n",
       "       'Ovarian carcinoma', 'Pancreatic ductal adenocarcinoma',\n",
       "       'Clear cell renal cell carcinoma'], dtype=object)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "8fe65899-9ea6-4985-bfe1-b145f76870be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BRCA', 'COADREAD', 'LUSC', 'LUAD', 'OV', 'PAAD', 'KIRCKICH']"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapped_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "e0ae57f5-7348-4c98-aa9a-954933f7168d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapped_palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "1f75c0c0-90a4-4c45-9cde-e17e7be0224b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'BRCA'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniforge3/envs/py310tf210/lib/python3.10/site-packages/pandas/core/indexes/base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/miniforge3/envs/py310tf210/lib/python3.10/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/miniforge3/envs/py310tf210/lib/python3.10/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'BRCA'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[209], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m mapped_colors \u001b[38;5;241m=\u001b[39m [abbreviation_mapping\u001b[38;5;241m.\u001b[39mget(label) \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m unique_labels]\n\u001b[1;32m      3\u001b[0m mapped_palette \u001b[38;5;241m=\u001b[39m [tcga_colors_df\u001b[38;5;241m.\u001b[39mget(color) \u001b[38;5;28;01mfor\u001b[39;00m color \u001b[38;5;129;01min\u001b[39;00m mapped_colors]\n\u001b[0;32m----> 4\u001b[0m palette_dict \u001b[38;5;241m=\u001b[39m {label: tcga_colors_df[abbreviation_mapping[label]] \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m unique_labels}\n",
      "Cell \u001b[0;32mIn[209], line 4\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      2\u001b[0m mapped_colors \u001b[38;5;241m=\u001b[39m [abbreviation_mapping\u001b[38;5;241m.\u001b[39mget(label) \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m unique_labels]\n\u001b[1;32m      3\u001b[0m mapped_palette \u001b[38;5;241m=\u001b[39m [tcga_colors_df\u001b[38;5;241m.\u001b[39mget(color) \u001b[38;5;28;01mfor\u001b[39;00m color \u001b[38;5;129;01min\u001b[39;00m mapped_colors]\n\u001b[0;32m----> 4\u001b[0m palette_dict \u001b[38;5;241m=\u001b[39m {label: \u001b[43mtcga_colors_df\u001b[49m\u001b[43m[\u001b[49m\u001b[43mabbreviation_mapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m unique_labels}\n",
      "File \u001b[0;32m~/miniforge3/envs/py310tf210/lib/python3.10/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3760\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3763\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/miniforge3/envs/py310tf210/lib/python3.10/site-packages/pandas/core/indexes/base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3655\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3657\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3658\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3659\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3660\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'BRCA'"
     ]
    }
   ],
   "source": [
    "unique_labels = f1_stor_frm['Label'].unique()\n",
    "mapped_colors = [abbreviation_mapping.get(label) for label in unique_labels]\n",
    "mapped_palette = [tcga_colors_df.get(color) for color in mapped_colors]\n",
    "palette_dict = {label: tcga_colors_df[abbreviation_mapping[label]] for label in unique_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01533d52-7f6b-44c5-8489-d807ba719857",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "bdd0cb98-fd7a-481f-b997-c4d4208786a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "tcga_color_mapping = pd.read_csv('/Users/karlberb/Documents/work/archive/tcga_colors.tsv', sep='\\t',\n",
    "                            index_col = 0\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "b11245a0-d6e4-4d7a-a011-0be47cbc94e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Breast carcinoma', 'Colon adenocarcinoma',\n",
       "       'Lung squamous cell carcinoma', 'Lung adenocarcinoma',\n",
       "       'Ovarian carcinoma', 'Pancreatic ductal adenocarcinoma',\n",
       "       'Clear cell renal cell carcinoma'], dtype=object)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "3d84bddb-ccfa-48b3-a6ec-2e2fdf16dedd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Breast carcinoma'"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "e5b0d307-5253-4a9c-810e-954d131f3f8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BRCA'"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abbreviation_mapping.get(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "f44b63c7-630d-49b1-ab18-7da436bab373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>cohort_color</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tcga_cohorts</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACC</th>\n",
       "      <td>#C1A72F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLCA</th>\n",
       "      <td>#FAD2D9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRCA</th>\n",
       "      <td>#ED2891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CESC</th>\n",
       "      <td>#F6B667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COADREAD</th>\n",
       "      <td>#9EDDF9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cohort_color\n",
       "tcga_cohorts             \n",
       "ACC               #C1A72F\n",
       "BLCA              #FAD2D9\n",
       "BRCA              #ED2891\n",
       "CESC              #F6B667\n",
       "COADREAD          #9EDDF9"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tcga_color_mapping.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "c9652f45-bd96-4432-a1f7-99be3df508f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "de92bb5b-8447-478a-bf56-b5f04952284a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tcga_color_mapping = dict(zip(tcga_colors_df['tcga_cohorts'], tcga_colors_df['cohort_color']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "95ccaca1-04e7-4625-a04d-d35f705c63f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ACC': '#C1A72F',\n",
       " 'BLCA': '#FAD2D9',\n",
       " 'BRCA': '#ED2891',\n",
       " 'CESC': '#F6B667',\n",
       " 'COADREAD': '#9EDDF9',\n",
       " 'ESCC': '#007EB5',\n",
       " 'GEA': '#909485',\n",
       " 'HNSC': '#97D1A9',\n",
       " 'KIRCKICH': '#F8AFB3',\n",
       " 'KIRP': '#EA7075',\n",
       " 'LGGGBM': '#D49DC7',\n",
       " 'LIHCCHOL': '#CACCDB',\n",
       " 'LUAD': '#D3C3E0',\n",
       " 'LUSC': '#A084BD',\n",
       " 'MESO': '#542C88',\n",
       " 'OV': '#D97D25',\n",
       " 'PAAD': '#6E7BA2',\n",
       " 'PCPG': '#E8C51D',\n",
       " 'PRAD': '#7E1918',\n",
       " 'SARC': '#00A99D',\n",
       " 'SKCM': '#BBD642',\n",
       " 'TGCT': '#BE1E2D',\n",
       " 'THCA': '#F9ED32',\n",
       " 'THYM': '#CEAC8F',\n",
       " 'UCEC': '#FBE3C7',\n",
       " 'UVM': '#009444'}"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tcga_color_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "fa04511f-db84-4a08-8531-7453ac599822",
   "metadata": {},
   "outputs": [],
   "source": [
    "palette_dict = {}\n",
    "for label in unique_labels:\n",
    "    # break\n",
    "    tcga_abbreviation = abbreviation_mapping.get(label)\n",
    "    # break\n",
    "    color = tcga_color_mapping.get(tcga_abbreviation)\n",
    "    # break\n",
    "    if color:\n",
    "        palette_dict[label] = color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "c46dc9d8-4027-4e80-a6a3-d0172ec99b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Label</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.122449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung squamous cell carcinoma</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.338983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ovarian carcinoma</td>\n",
       "      <td>0.241379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>0.162162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.156863</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.145455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung squamous cell carcinoma</td>\n",
       "      <td>0.054054</td>\n",
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       "      <th>3</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.243243</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ovarian carcinoma</td>\n",
       "      <td>0.215385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>0.146341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.237288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung squamous cell carcinoma</td>\n",
       "      <td>0.095238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.367816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ovarian carcinoma</td>\n",
       "      <td>0.215385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.093023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung squamous cell carcinoma</td>\n",
       "      <td>0.038462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ovarian carcinoma</td>\n",
       "      <td>0.238806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.070175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lung squamous cell carcinoma</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.340909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ovarian carcinoma</td>\n",
       "      <td>0.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>0.213333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Label  F1_Score\n",
       "0                  Breast carcinoma  0.122449\n",
       "1              Colon adenocarcinoma  0.108108\n",
       "2      Lung squamous cell carcinoma  0.050000\n",
       "3               Lung adenocarcinoma  0.338983\n",
       "4                 Ovarian carcinoma  0.241379\n",
       "5  Pancreatic ductal adenocarcinoma  0.090909\n",
       "6   Clear cell renal cell carcinoma  0.162162\n",
       "0                  Breast carcinoma  0.156863\n",
       "1              Colon adenocarcinoma  0.145455\n",
       "2      Lung squamous cell carcinoma  0.054054\n",
       "3               Lung adenocarcinoma  0.243243\n",
       "4                 Ovarian carcinoma  0.215385\n",
       "5  Pancreatic ductal adenocarcinoma  0.058824\n",
       "6   Clear cell renal cell carcinoma  0.146341\n",
       "0                  Breast carcinoma  0.237288\n",
       "1              Colon adenocarcinoma  0.160000\n",
       "2      Lung squamous cell carcinoma  0.095238\n",
       "3               Lung adenocarcinoma  0.367816\n",
       "4                 Ovarian carcinoma  0.215385\n",
       "5  Pancreatic ductal adenocarcinoma  0.080000\n",
       "6   Clear cell renal cell carcinoma  0.200000\n",
       "0                  Breast carcinoma  0.076923\n",
       "1              Colon adenocarcinoma  0.093023\n",
       "2      Lung squamous cell carcinoma  0.038462\n",
       "3               Lung adenocarcinoma  0.190476\n",
       "4                 Ovarian carcinoma  0.238806\n",
       "5  Pancreatic ductal adenocarcinoma  0.000000\n",
       "6   Clear cell renal cell carcinoma  0.150000\n",
       "0                  Breast carcinoma  0.070175\n",
       "1              Colon adenocarcinoma  0.153846\n",
       "2      Lung squamous cell carcinoma  0.076923\n",
       "3               Lung adenocarcinoma  0.340909\n",
       "4                 Ovarian carcinoma  0.370370\n",
       "5  Pancreatic ductal adenocarcinoma  0.000000\n",
       "6   Clear cell renal cell carcinoma  0.213333"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_stor_frm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "622a290c-ec79-48d0-ac0d-f6062d8109e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cell_line_+_cptac'"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "998b4ec8-b168-4627-a9ba-656951e48ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cnvr'"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "c74e83bf-e8fb-48ec-b688-c587b15f4148",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cancer Type Logistic Regression Plot Function\n",
    "def lgrg_plt(f1_stor_frm):\n",
    "    plt.figure(figsize=(15, 8))\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    sns.set(font_scale=1.5)\n",
    "    \n",
    "    sns.barplot(\n",
    "        x='Label', y='F1_Score', data=f1_stor_frm,\n",
    "        palette=palette_dict,\n",
    "        errorbar=None)\n",
    "    sns.swarmplot(x='Label', y='F1_Score', data=f1_stor_frm,\n",
    "                  color='#333333', size=12)\n",
    "    \n",
    "    plt.title('Model type logistic regression', fontsize=24)\n",
    "    plt.xlabel('Model Type', fontsize=20)\n",
    "    plt.ylabel('F1 Score', fontsize=20)\n",
    "    \n",
    "    plt.grid(color='grey', linestyle='-', linewidth=0.25, alpha=0.5)\n",
    "    plt.savefig('../results_encoded/plat.'+plat+'_'+data_name+'_dim.'+str(\n",
    "                latent_dim)+'_epochs.'+str(\n",
    "                epochs)+'_mode.'+mode+'.png', bbox_inches='tight')\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "73146845-8e57-4782-920e-6dd616159f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgrg_plt(f1_stor_frm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbd80078-ad71-4b72-a3c2-6b50015f09d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e283d7aa-718d-4394-a937-cd916da2e753",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define some example parameters for demonstration purposes\n",
    "plat = \"cell_line\"\n",
    "data_name = \"cptac_cnvr\"\n",
    "latent_dim = 50\n",
    "epochs = 30\n",
    "mode = \"cancer_type\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "30fcf5c4-3977-431c-9651-eff43ea2c6e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cell_line_+_cptac'"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "3fc84d5c-835b-4635-97ba-9fd45823f307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cnvr'"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "42d25a02-d48a-4159-824a-31e883474454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "af8d9760-a81e-4781-a77e-914c6d8ea071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "a59fe258-6207-45a8-af50-d53c40603d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cancer_type'"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "abb65046-0f18-4570-9367-c1f9b182a436",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_name = 'Cancer type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "864cc7f4-f865-44a9-98ce-77b015f39ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modified Cancer Type Logistic Regression Plot Function\n",
    "def lgrg_plt_fnc(f1_stor_frm, plat, data_name, latent_dim, epochs, mode, mode_name):\n",
    "    plt.figure(figsize=(20, 10))\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    sns.set(font_scale=2)  # Increase the font scale for larger fonts\n",
    "    \n",
    "    ax = sns.barplot(\n",
    "        x='Label', y='F1_Score', data=f1_stor_frm,\n",
    "        palette=palette_dict,\n",
    "        errorbar=None)\n",
    "    sns.swarmplot(x='Label', y='F1_Score', data=f1_stor_frm,\n",
    "                  color='#333333', size=15)  # Increase point size\n",
    "    \n",
    "    plt.title(mode_name+' logistic regression', fontsize=30)  # Increase title font size\n",
    "    plt.xlabel(mode_name, fontsize=28)  # Increase x-axis label font size\n",
    "    plt.ylabel('F1 Score', fontsize=28)  # Increase y-axis label font size\n",
    "    \n",
    "    plt.xticks(rotation=45, ha = 'right')  # Rotate x-axis labels for better visibility\n",
    "    plt.grid(color='grey', linestyle='-', linewidth=0.25, alpha=0.5)\n",
    "    \n",
    "    # Modify the save path to use the provided parameters\n",
    "    plt.savefig('../results_encoded/fnc0_plat.'+plat+'_'+data_name+'_dim.'+str(\n",
    "                latent_dim)+'_epochs.'+str(\n",
    "                epochs)+'_mode.'+mode+'.png', bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "96b458b2-dafa-4b65-9486-1f467814cc6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgrg_plt_fnc(f1_stor_frm, plat, data_name, latent_dim, epochs, mode, mode_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dccda9ff-273d-4f71-9ad1-37d561948ee1",
   "metadata": {},
   "source": [
    "#### devel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "a62543c2-35d8-413d-a655-a2a801a45779",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Breast carcinoma': '#ED2891',\n",
       " 'Colon adenocarcinoma': '#9EDDF9',\n",
       " 'Lung squamous cell carcinoma': '#A084BD',\n",
       " 'Lung adenocarcinoma': '#D3C3E0',\n",
       " 'Ovarian carcinoma': '#D97D25',\n",
       " 'Pancreatic ductal adenocarcinoma': '#6E7BA2',\n",
       " 'Clear cell renal cell carcinoma': '#F8AFB3'}"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "palette_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d39dd4-408d-4baf-9003-73095ad2fc6f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Template blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8accae16-994b-4c4d-b488-c49fad515f5b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "palette_dict = {label: color_mapping[abbreviation_mapping[label]] for label in unique_labels}\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(15, 8))\n",
    "sns.barplot(x='Label', y='F1_Score', data=log_reg_f1_df, palette=palette_dict)\n",
    "sns.swarmplot(x='Label', y='F1_Score', data=log_reg_f1_df, color='darkgrey')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('F1 Scores by Cancer Type')\n",
    "plt.xlabel('Cancer Type')\n",
    "plt.ylabel('F1 Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7e7f5cd-4fc3-4a96-9aa5-6bcd6f20df98",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def lgrg_plt(f1_stor_frm)\n",
    "    plt.figure(figsize=(15, 8))\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    sns.set(font_scale=1.5)\n",
    "    \n",
    "    sns.barplot(x='Label', y='F1_Score', data=f1_stor_frm, palette=['#666666', '#999999'],\n",
    "               errorbar=None)\n",
    "    sns.swarmplot(x='Label', y='F1_Score', data=f1_stor_frm, color='#333333', size=12)\n",
    "    \n",
    "    plt.title('Model type logistic regression', fontsize=24)\n",
    "    plt.xlabel('Model Type', fontsize=20)\n",
    "    plt.ylabel('F1 Score', fontsize=20)\n",
    "    \n",
    "    plt.grid(color='grey', linestyle='-', linewidth=0.25, alpha=0.5)\n",
    "    plt.savefig('../results_encoded/plat.'+plat+'_'+data_name+'_dim.'+str(\n",
    "                latent_dim)+'_epochs.'+str(\n",
    "                epochs)+'_mode.'+mode+'.png', bbox_inches='tight')\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "a004ca82-547c-44c9-9177-c02dee21c839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'enc'"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encdng_sts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0add9df-d615-4543-94b0-400d8edd79fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# know data type at this point, recycle vars "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3ae904-8db7-4399-9b13-88f862979add",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Reset euc_n var between data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "122352ae-01a0-4e6c-bd6e-fec55dc075e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "euc_n = latent_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "4af8619b-19c7-435c-ba83-fdb281e88459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>improve_sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>108162.0</th>\n",
       "      <td>1.439899</td>\n",
       "      <td>1.533506</td>\n",
       "      <td>0.628120</td>\n",
       "      <td>0.054526</td>\n",
       "      <td>-0.559137</td>\n",
       "      <td>-0.585355</td>\n",
       "      <td>-1.277910</td>\n",
       "      <td>0.693328</td>\n",
       "      <td>0.570371</td>\n",
       "      <td>0.088946</td>\n",
       "      <td>...</td>\n",
       "      <td>0.199645</td>\n",
       "      <td>0.091887</td>\n",
       "      <td>0.547483</td>\n",
       "      <td>0.072149</td>\n",
       "      <td>2.115180</td>\n",
       "      <td>1.866966</td>\n",
       "      <td>1.284497</td>\n",
       "      <td>0.188087</td>\n",
       "      <td>0.708378</td>\n",
       "      <td>0.007651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108163.0</th>\n",
       "      <td>-0.953706</td>\n",
       "      <td>-0.610869</td>\n",
       "      <td>0.140408</td>\n",
       "      <td>0.183683</td>\n",
       "      <td>0.548320</td>\n",
       "      <td>-0.586559</td>\n",
       "      <td>-0.341316</td>\n",
       "      <td>-0.113022</td>\n",
       "      <td>0.074512</td>\n",
       "      <td>-0.397311</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.099538</td>\n",
       "      <td>0.067979</td>\n",
       "      <td>-1.270018</td>\n",
       "      <td>-0.067538</td>\n",
       "      <td>0.993388</td>\n",
       "      <td>1.163411</td>\n",
       "      <td>1.241985</td>\n",
       "      <td>0.085117</td>\n",
       "      <td>-0.703866</td>\n",
       "      <td>-0.116846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108164.0</th>\n",
       "      <td>0.528911</td>\n",
       "      <td>-3.034726</td>\n",
       "      <td>0.285810</td>\n",
       "      <td>0.079621</td>\n",
       "      <td>-0.191005</td>\n",
       "      <td>-0.294464</td>\n",
       "      <td>-0.154292</td>\n",
       "      <td>-0.603271</td>\n",
       "      <td>1.639294</td>\n",
       "      <td>-0.763550</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030159</td>\n",
       "      <td>0.065285</td>\n",
       "      <td>0.101063</td>\n",
       "      <td>0.196396</td>\n",
       "      <td>0.349092</td>\n",
       "      <td>1.193105</td>\n",
       "      <td>1.093049</td>\n",
       "      <td>0.069530</td>\n",
       "      <td>-0.820990</td>\n",
       "      <td>-0.904080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108165.0</th>\n",
       "      <td>-0.114301</td>\n",
       "      <td>-0.529857</td>\n",
       "      <td>0.385949</td>\n",
       "      <td>0.114382</td>\n",
       "      <td>-0.368625</td>\n",
       "      <td>-0.642673</td>\n",
       "      <td>-0.546929</td>\n",
       "      <td>-0.953669</td>\n",
       "      <td>0.128911</td>\n",
       "      <td>-0.292151</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053926</td>\n",
       "      <td>0.077217</td>\n",
       "      <td>-1.409377</td>\n",
       "      <td>-0.026639</td>\n",
       "      <td>1.860669</td>\n",
       "      <td>1.069203</td>\n",
       "      <td>1.026686</td>\n",
       "      <td>0.091033</td>\n",
       "      <td>-1.176576</td>\n",
       "      <td>-0.373831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108166.0</th>\n",
       "      <td>0.029109</td>\n",
       "      <td>-0.588236</td>\n",
       "      <td>0.420315</td>\n",
       "      <td>-0.065497</td>\n",
       "      <td>-0.845241</td>\n",
       "      <td>-0.374031</td>\n",
       "      <td>-0.325192</td>\n",
       "      <td>-0.751451</td>\n",
       "      <td>1.378194</td>\n",
       "      <td>-0.131867</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004814</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>-1.038090</td>\n",
       "      <td>-0.079290</td>\n",
       "      <td>1.645097</td>\n",
       "      <td>1.394155</td>\n",
       "      <td>0.990790</td>\n",
       "      <td>-0.008248</td>\n",
       "      <td>0.691237</td>\n",
       "      <td>0.176602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599.0</th>\n",
       "      <td>1.073569</td>\n",
       "      <td>-5.609256</td>\n",
       "      <td>0.673468</td>\n",
       "      <td>-0.236819</td>\n",
       "      <td>-0.654580</td>\n",
       "      <td>-1.610266</td>\n",
       "      <td>-0.887134</td>\n",
       "      <td>-1.008669</td>\n",
       "      <td>0.122534</td>\n",
       "      <td>-0.022692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010750</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>-0.303314</td>\n",
       "      <td>-0.262494</td>\n",
       "      <td>1.433354</td>\n",
       "      <td>2.353862</td>\n",
       "      <td>2.949597</td>\n",
       "      <td>0.539274</td>\n",
       "      <td>-0.746637</td>\n",
       "      <td>-1.690494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600.0</th>\n",
       "      <td>1.457700</td>\n",
       "      <td>-2.708468</td>\n",
       "      <td>0.691975</td>\n",
       "      <td>-0.155513</td>\n",
       "      <td>-0.738500</td>\n",
       "      <td>-1.627340</td>\n",
       "      <td>0.300062</td>\n",
       "      <td>-1.174452</td>\n",
       "      <td>-0.060655</td>\n",
       "      <td>0.034433</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.028800</td>\n",
       "      <td>0.037832</td>\n",
       "      <td>-1.430725</td>\n",
       "      <td>-0.255326</td>\n",
       "      <td>2.557380</td>\n",
       "      <td>2.800176</td>\n",
       "      <td>3.663093</td>\n",
       "      <td>0.470992</td>\n",
       "      <td>-1.035416</td>\n",
       "      <td>-1.604021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601.0</th>\n",
       "      <td>2.678715</td>\n",
       "      <td>4.606073</td>\n",
       "      <td>0.679786</td>\n",
       "      <td>-0.120099</td>\n",
       "      <td>-0.708968</td>\n",
       "      <td>-1.523231</td>\n",
       "      <td>-0.531936</td>\n",
       "      <td>0.041633</td>\n",
       "      <td>0.094457</td>\n",
       "      <td>-0.377173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033462</td>\n",
       "      <td>0.152978</td>\n",
       "      <td>-0.122706</td>\n",
       "      <td>-0.257496</td>\n",
       "      <td>1.208074</td>\n",
       "      <td>2.762115</td>\n",
       "      <td>2.343506</td>\n",
       "      <td>0.526297</td>\n",
       "      <td>-1.190930</td>\n",
       "      <td>-1.442815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602.0</th>\n",
       "      <td>2.286318</td>\n",
       "      <td>0.861938</td>\n",
       "      <td>0.750276</td>\n",
       "      <td>-0.095188</td>\n",
       "      <td>-0.817323</td>\n",
       "      <td>-1.588656</td>\n",
       "      <td>-0.425593</td>\n",
       "      <td>0.908445</td>\n",
       "      <td>-0.142272</td>\n",
       "      <td>-0.185081</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007114</td>\n",
       "      <td>0.187012</td>\n",
       "      <td>-1.614214</td>\n",
       "      <td>-0.218267</td>\n",
       "      <td>1.654389</td>\n",
       "      <td>2.160376</td>\n",
       "      <td>3.187936</td>\n",
       "      <td>0.540135</td>\n",
       "      <td>-1.236527</td>\n",
       "      <td>-1.427157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723.0</th>\n",
       "      <td>0.893219</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.654090</td>\n",
       "      <td>-0.006956</td>\n",
       "      <td>-0.798081</td>\n",
       "      <td>-1.611042</td>\n",
       "      <td>-0.087794</td>\n",
       "      <td>-0.214276</td>\n",
       "      <td>-0.125328</td>\n",
       "      <td>-0.218141</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138072</td>\n",
       "      <td>0.089488</td>\n",
       "      <td>-0.590358</td>\n",
       "      <td>-0.324322</td>\n",
       "      <td>0.552230</td>\n",
       "      <td>2.740617</td>\n",
       "      <td>3.024638</td>\n",
       "      <td>0.533053</td>\n",
       "      <td>0.228939</td>\n",
       "      <td>-1.359070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>996 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0         1         2         3         4         5   \\\n",
       "improve_sample_id                                                               \n",
       "108162.0           1.439899  1.533506  0.628120  0.054526 -0.559137 -0.585355   \n",
       "108163.0          -0.953706 -0.610869  0.140408  0.183683  0.548320 -0.586559   \n",
       "108164.0           0.528911 -3.034726  0.285810  0.079621 -0.191005 -0.294464   \n",
       "108165.0          -0.114301 -0.529857  0.385949  0.114382 -0.368625 -0.642673   \n",
       "108166.0           0.029109 -0.588236  0.420315 -0.065497 -0.845241 -0.374031   \n",
       "...                     ...       ...       ...       ...       ...       ...   \n",
       "1599.0             1.073569 -5.609256  0.673468 -0.236819 -0.654580 -1.610266   \n",
       "1600.0             1.457700 -2.708468  0.691975 -0.155513 -0.738500 -1.627340   \n",
       "1601.0             2.678715  4.606073  0.679786 -0.120099 -0.708968 -1.523231   \n",
       "1602.0             2.286318  0.861938  0.750276 -0.095188 -0.817323 -1.588656   \n",
       "1723.0             0.893219  0.312500  0.654090 -0.006956 -0.798081 -1.611042   \n",
       "\n",
       "                         6         7         8         9   ...        40  \\\n",
       "improve_sample_id                                          ...             \n",
       "108162.0          -1.277910  0.693328  0.570371  0.088946  ...  0.199645   \n",
       "108163.0          -0.341316 -0.113022  0.074512 -0.397311  ... -0.099538   \n",
       "108164.0          -0.154292 -0.603271  1.639294 -0.763550  ...  0.030159   \n",
       "108165.0          -0.546929 -0.953669  0.128911 -0.292151  ... -0.053926   \n",
       "108166.0          -0.325192 -0.751451  1.378194 -0.131867  ...  0.004814   \n",
       "...                     ...       ...       ...       ...  ...       ...   \n",
       "1599.0            -0.887134 -1.008669  0.122534 -0.022692  ... -0.010750   \n",
       "1600.0             0.300062 -1.174452 -0.060655  0.034433  ... -0.028800   \n",
       "1601.0            -0.531936  0.041633  0.094457 -0.377173  ...  0.033462   \n",
       "1602.0            -0.425593  0.908445 -0.142272 -0.185081  ... -0.007114   \n",
       "1723.0            -0.087794 -0.214276 -0.125328 -0.218141  ...  0.138072   \n",
       "\n",
       "                         41        42        43        44        45        46  \\\n",
       "improve_sample_id                                                               \n",
       "108162.0           0.091887  0.547483  0.072149  2.115180  1.866966  1.284497   \n",
       "108163.0           0.067979 -1.270018 -0.067538  0.993388  1.163411  1.241985   \n",
       "108164.0           0.065285  0.101063  0.196396  0.349092  1.193105  1.093049   \n",
       "108165.0           0.077217 -1.409377 -0.026639  1.860669  1.069203  1.026686   \n",
       "108166.0           0.002132 -1.038090 -0.079290  1.645097  1.394155  0.990790   \n",
       "...                     ...       ...       ...       ...       ...       ...   \n",
       "1599.0             0.115486 -0.303314 -0.262494  1.433354  2.353862  2.949597   \n",
       "1600.0             0.037832 -1.430725 -0.255326  2.557380  2.800176  3.663093   \n",
       "1601.0             0.152978 -0.122706 -0.257496  1.208074  2.762115  2.343506   \n",
       "1602.0             0.187012 -1.614214 -0.218267  1.654389  2.160376  3.187936   \n",
       "1723.0             0.089488 -0.590358 -0.324322  0.552230  2.740617  3.024638   \n",
       "\n",
       "                         47        48        49  \n",
       "improve_sample_id                                \n",
       "108162.0           0.188087  0.708378  0.007651  \n",
       "108163.0           0.085117 -0.703866 -0.116846  \n",
       "108164.0           0.069530 -0.820990 -0.904080  \n",
       "108165.0           0.091033 -1.176576 -0.373831  \n",
       "108166.0          -0.008248  0.691237  0.176602  \n",
       "...                     ...       ...       ...  \n",
       "1599.0             0.539274 -0.746637 -1.690494  \n",
       "1600.0             0.470992 -1.035416 -1.604021  \n",
       "1601.0             0.526297 -1.190930 -1.442815  \n",
       "1602.0             0.540135 -1.236527 -1.427157  \n",
       "1723.0             0.533053  0.228939 -1.359070  \n",
       "\n",
       "[996 rows x 50 columns]"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euc_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "046f853e-7acb-49e2-99f8-0e7e32f2dd56",
   "metadata": {
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   "outputs": [
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       "      <th></th>\n",
       "      <th>cancer_type</th>\n",
       "      <th>model_type</th>\n",
       "      <th>1.0</th>\n",
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       "      <th>improve_sample_id</th>\n",
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       "      <th>108162.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
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       "      <td>-0.066020</td>\n",
       "      <td>0.091830</td>\n",
       "      <td>0.139840</td>\n",
       "      <td>-0.190880</td>\n",
       "      <td>-0.055800</td>\n",
       "      <td>0.016700</td>\n",
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       "      <td>-0.178470</td>\n",
       "      <td>0.091830</td>\n",
       "      <td>0.091830</td>\n",
       "      <td>0.091830</td>\n",
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       "      <th>108163.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
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       "      <td>-0.019530</td>\n",
       "      <td>-0.166190</td>\n",
       "      <td>-0.068200</td>\n",
       "      <td>0.010580</td>\n",
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       "      <td>-0.043120</td>\n",
       "      <td>-0.051920</td>\n",
       "      <td>-0.051920</td>\n",
       "      <td>-0.051920</td>\n",
       "      <td>-0.075710</td>\n",
       "      <td>-0.066610</td>\n",
       "      <td>-0.051920</td>\n",
       "      <td>-0.075500</td>\n",
       "      <td>0.070210</td>\n",
       "      <td>-0.099460</td>\n",
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       "    <tr>\n",
       "      <th>108164.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>0.284680</td>\n",
       "      <td>-0.263720</td>\n",
       "      <td>0.262890</td>\n",
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       "      <td>-0.115030</td>\n",
       "      <td>0.284810</td>\n",
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       "    <tr>\n",
       "      <th>108165.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>0.003700</td>\n",
       "      <td>-0.003320</td>\n",
       "      <td>-0.007440</td>\n",
       "      <td>0.139350</td>\n",
       "      <td>0.051430</td>\n",
       "      <td>-0.047940</td>\n",
       "      <td>0.089450</td>\n",
       "      <td>0.089450</td>\n",
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       "      <td>0.039000</td>\n",
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       "      <td>Breast carcinoma</td>\n",
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       "      <td>0.135520</td>\n",
       "      <td>0.264650</td>\n",
       "      <td>-0.117420</td>\n",
       "      <td>0.099580</td>\n",
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       "      <td>0.098260</td>\n",
       "      <td>-0.236890</td>\n",
       "      <td>0.070210</td>\n",
       "      <td>-0.210850</td>\n",
       "      <td>-0.231830</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1599.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>0.991330</td>\n",
       "      <td>0.987027</td>\n",
       "      <td>0.982139</td>\n",
       "      <td>0.989569</td>\n",
       "      <td>1.190990</td>\n",
       "      <td>1.190990</td>\n",
       "      <td>1.031283</td>\n",
       "      <td>1.031283</td>\n",
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       "      <td>0.993150</td>\n",
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       "      <td>0.987201</td>\n",
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       "      <td>1.104300</td>\n",
       "      <td>0.991330</td>\n",
       "      <td>0.974191</td>\n",
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       "    <tr>\n",
       "      <th>1600.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>1.025435</td>\n",
       "      <td>1.020126</td>\n",
       "      <td>1.002917</td>\n",
       "      <td>0.989082</td>\n",
       "      <td>1.051277</td>\n",
       "      <td>0.984148</td>\n",
       "      <td>1.016712</td>\n",
       "      <td>1.022126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.979679</td>\n",
       "      <td>0.989082</td>\n",
       "      <td>0.989082</td>\n",
       "      <td>0.989082</td>\n",
       "      <td>1.019652</td>\n",
       "      <td>0.981697</td>\n",
       "      <td>0.989082</td>\n",
       "      <td>0.964601</td>\n",
       "      <td>1.025435</td>\n",
       "      <td>0.979814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>1.088740</td>\n",
       "      <td>1.098864</td>\n",
       "      <td>1.112800</td>\n",
       "      <td>0.905957</td>\n",
       "      <td>1.005205</td>\n",
       "      <td>0.905069</td>\n",
       "      <td>0.676913</td>\n",
       "      <td>0.676913</td>\n",
       "      <td>...</td>\n",
       "      <td>0.954895</td>\n",
       "      <td>0.905957</td>\n",
       "      <td>0.905957</td>\n",
       "      <td>0.905957</td>\n",
       "      <td>0.911477</td>\n",
       "      <td>0.670966</td>\n",
       "      <td>0.905957</td>\n",
       "      <td>1.051294</td>\n",
       "      <td>1.090995</td>\n",
       "      <td>0.662120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>1.042356</td>\n",
       "      <td>0.628755</td>\n",
       "      <td>1.031863</td>\n",
       "      <td>1.015091</td>\n",
       "      <td>1.105133</td>\n",
       "      <td>1.051108</td>\n",
       "      <td>0.711915</td>\n",
       "      <td>0.711915</td>\n",
       "      <td>...</td>\n",
       "      <td>0.996181</td>\n",
       "      <td>1.015091</td>\n",
       "      <td>1.015091</td>\n",
       "      <td>1.015091</td>\n",
       "      <td>0.984068</td>\n",
       "      <td>0.619404</td>\n",
       "      <td>1.015091</td>\n",
       "      <td>0.992265</td>\n",
       "      <td>1.050526</td>\n",
       "      <td>1.033771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723.0</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>1.014255</td>\n",
       "      <td>1.389122</td>\n",
       "      <td>0.859749</td>\n",
       "      <td>1.033109</td>\n",
       "      <td>1.204633</td>\n",
       "      <td>0.860665</td>\n",
       "      <td>0.867172</td>\n",
       "      <td>0.867172</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098913</td>\n",
       "      <td>1.033109</td>\n",
       "      <td>1.033109</td>\n",
       "      <td>1.033109</td>\n",
       "      <td>0.863035</td>\n",
       "      <td>0.864878</td>\n",
       "      <td>1.033109</td>\n",
       "      <td>1.102413</td>\n",
       "      <td>0.874885</td>\n",
       "      <td>1.107953</td>\n",
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       "<p>996 rows × 13903 columns</p>\n",
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      "text/plain": [
       "                                       cancer_type model_type       1.0  \\\n",
       "improve_sample_id                                                         \n",
       "108162.0                          Breast carcinoma      Tumor -0.072650   \n",
       "108163.0                          Breast carcinoma      Tumor -0.066440   \n",
       "108164.0                          Breast carcinoma      Tumor  0.284680   \n",
       "108165.0                          Breast carcinoma      Tumor  0.003700   \n",
       "108166.0                          Breast carcinoma      Tumor  0.096100   \n",
       "...                                            ...        ...       ...   \n",
       "1599.0             Clear cell renal cell carcinoma  cell line  0.991330   \n",
       "1600.0             Clear cell renal cell carcinoma  cell line  1.025435   \n",
       "1601.0             Clear cell renal cell carcinoma  cell line  1.088740   \n",
       "1602.0             Clear cell renal cell carcinoma  cell line  1.042356   \n",
       "1723.0                         Lung adenocarcinoma  cell line  1.014255   \n",
       "\n",
       "                        2.0      13.0      14.0      16.0      18.0      19.0  \\\n",
       "improve_sample_id                                                               \n",
       "108162.0          -0.116220 -0.066020  0.091830  0.139840 -0.190880 -0.055800   \n",
       "108163.0           0.314130 -0.090750 -0.051920 -0.019530 -0.166190 -0.068200   \n",
       "108164.0          -0.263720  0.262890  0.216530  0.269190 -0.389280 -0.196390   \n",
       "108165.0          -0.003320 -0.007440  0.139350  0.051430 -0.047940  0.089450   \n",
       "108166.0           0.037510  0.099620 -0.236890  0.135520  0.264650 -0.117420   \n",
       "...                     ...       ...       ...       ...       ...       ...   \n",
       "1599.0             0.987027  0.982139  0.989569  1.190990  1.190990  1.031283   \n",
       "1600.0             1.020126  1.002917  0.989082  1.051277  0.984148  1.016712   \n",
       "1601.0             1.098864  1.112800  0.905957  1.005205  0.905069  0.676913   \n",
       "1602.0             0.628755  1.031863  1.015091  1.105133  1.051108  0.711915   \n",
       "1723.0             1.389122  0.859749  1.033109  1.204633  0.860665  0.867172   \n",
       "\n",
       "                       20.0  ...  105980003.0  106479861.0  106479938.0  \\\n",
       "improve_sample_id            ...                                          \n",
       "108162.0           0.016700  ...    -0.178470     0.091830     0.091830   \n",
       "108163.0           0.010580  ...    -0.043120    -0.051920    -0.051920   \n",
       "108164.0          -0.196390  ...    -0.159240     0.216530     0.053630   \n",
       "108165.0           0.089450  ...     0.039000     0.139350     0.139350   \n",
       "108166.0           0.099580  ...     0.122140    -0.236890    -0.236890   \n",
       "...                     ...  ...          ...          ...          ...   \n",
       "1599.0             1.031283  ...     0.993150     0.989569     0.989569   \n",
       "1600.0             1.022126  ...     0.979679     0.989082     0.989082   \n",
       "1601.0             0.676913  ...     0.954895     0.905957     0.905957   \n",
       "1602.0             0.711915  ...     0.996181     1.015091     1.015091   \n",
       "1723.0             0.867172  ...     1.098913     1.033109     1.033109   \n",
       "\n",
       "                   106480180.0  106480993.0  106481981.0  110599588.0  \\\n",
       "improve_sample_id                                                       \n",
       "108162.0              0.091830    -0.020410    -0.005760     0.091830   \n",
       "108163.0             -0.051920    -0.075710    -0.066610    -0.051920   \n",
       "108164.0             -0.272270     0.217620    -0.102390     0.216530   \n",
       "108165.0              0.139350    -0.009400     0.091440     0.139350   \n",
       "108166.0             -0.236890    -0.247420     0.098260    -0.236890   \n",
       "...                        ...          ...          ...          ...   \n",
       "1599.0                0.989569     0.987201     0.976031     0.989569   \n",
       "1600.0                0.989082     1.019652     0.981697     0.989082   \n",
       "1601.0                0.905957     0.911477     0.670966     0.905957   \n",
       "1602.0                1.015091     0.984068     0.619404     1.015091   \n",
       "1723.0                1.033109     0.863035     0.864878     1.033109   \n",
       "\n",
       "                   112441434.0  122405565.0  125316803.0  \n",
       "improve_sample_id                                         \n",
       "108162.0             -0.272700    -0.046020    -0.266960  \n",
       "108163.0             -0.075500     0.070210    -0.099460  \n",
       "108164.0             -0.263480    -0.115030     0.284810  \n",
       "108165.0             -0.011510     0.003700    -0.175980  \n",
       "108166.0              0.070210    -0.210850    -0.231830  \n",
       "...                        ...          ...          ...  \n",
       "1599.0                1.104300     0.991330     0.974191  \n",
       "1600.0                0.964601     1.025435     0.979814  \n",
       "1601.0                1.051294     1.090995     0.662120  \n",
       "1602.0                0.992265     1.050526     1.033771  \n",
       "1723.0                1.102413     0.874885     1.107953  \n",
       "\n",
       "[996 rows x 13903 columns]"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl_cp_cnvr_996"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "b69d1af8-5733-423f-8206-a1cf3b0cb931",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_df.insert(\n",
    "    loc=0, column='cancer_type',\n",
    "    value=cl_cp_cnvr_996['cancer_type'])\n",
    "latent_df.insert(\n",
    "    loc=1, column='model_type',\n",
    "    value=cl_cp_cnvr_996['model_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "41a9ae18-2aa2-4f79-956e-5afe7fe20675",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>cancer_type</th>\n",
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>improve_sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>108162.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>1.439899</td>\n",
       "      <td>1.533506</td>\n",
       "      <td>0.628120</td>\n",
       "      <td>0.054526</td>\n",
       "      <td>-0.559137</td>\n",
       "      <td>-0.585355</td>\n",
       "      <td>-1.277910</td>\n",
       "      <td>0.693328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.199645</td>\n",
       "      <td>0.091887</td>\n",
       "      <td>0.547483</td>\n",
       "      <td>0.072149</td>\n",
       "      <td>2.115180</td>\n",
       "      <td>1.866966</td>\n",
       "      <td>1.284497</td>\n",
       "      <td>0.188087</td>\n",
       "      <td>0.708378</td>\n",
       "      <td>0.007651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108163.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>-0.953706</td>\n",
       "      <td>-0.610869</td>\n",
       "      <td>0.140408</td>\n",
       "      <td>0.183683</td>\n",
       "      <td>0.548320</td>\n",
       "      <td>-0.586559</td>\n",
       "      <td>-0.341316</td>\n",
       "      <td>-0.113022</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.099538</td>\n",
       "      <td>0.067979</td>\n",
       "      <td>-1.270018</td>\n",
       "      <td>-0.067538</td>\n",
       "      <td>0.993388</td>\n",
       "      <td>1.163411</td>\n",
       "      <td>1.241985</td>\n",
       "      <td>0.085117</td>\n",
       "      <td>-0.703866</td>\n",
       "      <td>-0.116846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108164.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>0.528911</td>\n",
       "      <td>-3.034726</td>\n",
       "      <td>0.285810</td>\n",
       "      <td>0.079621</td>\n",
       "      <td>-0.191005</td>\n",
       "      <td>-0.294464</td>\n",
       "      <td>-0.154292</td>\n",
       "      <td>-0.603271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030159</td>\n",
       "      <td>0.065285</td>\n",
       "      <td>0.101063</td>\n",
       "      <td>0.196396</td>\n",
       "      <td>0.349092</td>\n",
       "      <td>1.193105</td>\n",
       "      <td>1.093049</td>\n",
       "      <td>0.069530</td>\n",
       "      <td>-0.820990</td>\n",
       "      <td>-0.904080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108165.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>-0.114301</td>\n",
       "      <td>-0.529857</td>\n",
       "      <td>0.385949</td>\n",
       "      <td>0.114382</td>\n",
       "      <td>-0.368625</td>\n",
       "      <td>-0.642673</td>\n",
       "      <td>-0.546929</td>\n",
       "      <td>-0.953669</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053926</td>\n",
       "      <td>0.077217</td>\n",
       "      <td>-1.409377</td>\n",
       "      <td>-0.026639</td>\n",
       "      <td>1.860669</td>\n",
       "      <td>1.069203</td>\n",
       "      <td>1.026686</td>\n",
       "      <td>0.091033</td>\n",
       "      <td>-1.176576</td>\n",
       "      <td>-0.373831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108166.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>0.029109</td>\n",
       "      <td>-0.588236</td>\n",
       "      <td>0.420315</td>\n",
       "      <td>-0.065497</td>\n",
       "      <td>-0.845241</td>\n",
       "      <td>-0.374031</td>\n",
       "      <td>-0.325192</td>\n",
       "      <td>-0.751451</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004814</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>-1.038090</td>\n",
       "      <td>-0.079290</td>\n",
       "      <td>1.645097</td>\n",
       "      <td>1.394155</td>\n",
       "      <td>0.990790</td>\n",
       "      <td>-0.008248</td>\n",
       "      <td>0.691237</td>\n",
       "      <td>0.176602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>1.073569</td>\n",
       "      <td>-5.609256</td>\n",
       "      <td>0.673468</td>\n",
       "      <td>-0.236819</td>\n",
       "      <td>-0.654580</td>\n",
       "      <td>-1.610266</td>\n",
       "      <td>-0.887134</td>\n",
       "      <td>-1.008669</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010750</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>-0.303314</td>\n",
       "      <td>-0.262494</td>\n",
       "      <td>1.433354</td>\n",
       "      <td>2.353862</td>\n",
       "      <td>2.949597</td>\n",
       "      <td>0.539274</td>\n",
       "      <td>-0.746637</td>\n",
       "      <td>-1.690494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>1.457700</td>\n",
       "      <td>-2.708468</td>\n",
       "      <td>0.691975</td>\n",
       "      <td>-0.155513</td>\n",
       "      <td>-0.738500</td>\n",
       "      <td>-1.627340</td>\n",
       "      <td>0.300062</td>\n",
       "      <td>-1.174452</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.028800</td>\n",
       "      <td>0.037832</td>\n",
       "      <td>-1.430725</td>\n",
       "      <td>-0.255326</td>\n",
       "      <td>2.557380</td>\n",
       "      <td>2.800176</td>\n",
       "      <td>3.663093</td>\n",
       "      <td>0.470992</td>\n",
       "      <td>-1.035416</td>\n",
       "      <td>-1.604021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>2.678715</td>\n",
       "      <td>4.606073</td>\n",
       "      <td>0.679786</td>\n",
       "      <td>-0.120099</td>\n",
       "      <td>-0.708968</td>\n",
       "      <td>-1.523231</td>\n",
       "      <td>-0.531936</td>\n",
       "      <td>0.041633</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033462</td>\n",
       "      <td>0.152978</td>\n",
       "      <td>-0.122706</td>\n",
       "      <td>-0.257496</td>\n",
       "      <td>1.208074</td>\n",
       "      <td>2.762115</td>\n",
       "      <td>2.343506</td>\n",
       "      <td>0.526297</td>\n",
       "      <td>-1.190930</td>\n",
       "      <td>-1.442815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>2.286318</td>\n",
       "      <td>0.861938</td>\n",
       "      <td>0.750276</td>\n",
       "      <td>-0.095188</td>\n",
       "      <td>-0.817323</td>\n",
       "      <td>-1.588656</td>\n",
       "      <td>-0.425593</td>\n",
       "      <td>0.908445</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007114</td>\n",
       "      <td>0.187012</td>\n",
       "      <td>-1.614214</td>\n",
       "      <td>-0.218267</td>\n",
       "      <td>1.654389</td>\n",
       "      <td>2.160376</td>\n",
       "      <td>3.187936</td>\n",
       "      <td>0.540135</td>\n",
       "      <td>-1.236527</td>\n",
       "      <td>-1.427157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723.0</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>0.893219</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.654090</td>\n",
       "      <td>-0.006956</td>\n",
       "      <td>-0.798081</td>\n",
       "      <td>-1.611042</td>\n",
       "      <td>-0.087794</td>\n",
       "      <td>-0.214276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138072</td>\n",
       "      <td>0.089488</td>\n",
       "      <td>-0.590358</td>\n",
       "      <td>-0.324322</td>\n",
       "      <td>0.552230</td>\n",
       "      <td>2.740617</td>\n",
       "      <td>3.024638</td>\n",
       "      <td>0.533053</td>\n",
       "      <td>0.228939</td>\n",
       "      <td>-1.359070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>996 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       cancer_type model_type         0  \\\n",
       "improve_sample_id                                                         \n",
       "108162.0                          Breast carcinoma      Tumor  1.439899   \n",
       "108163.0                          Breast carcinoma      Tumor -0.953706   \n",
       "108164.0                          Breast carcinoma      Tumor  0.528911   \n",
       "108165.0                          Breast carcinoma      Tumor -0.114301   \n",
       "108166.0                          Breast carcinoma      Tumor  0.029109   \n",
       "...                                            ...        ...       ...   \n",
       "1599.0             Clear cell renal cell carcinoma  cell line  1.073569   \n",
       "1600.0             Clear cell renal cell carcinoma  cell line  1.457700   \n",
       "1601.0             Clear cell renal cell carcinoma  cell line  2.678715   \n",
       "1602.0             Clear cell renal cell carcinoma  cell line  2.286318   \n",
       "1723.0                         Lung adenocarcinoma  cell line  0.893219   \n",
       "\n",
       "                          1         2         3         4         5         6  \\\n",
       "improve_sample_id                                                               \n",
       "108162.0           1.533506  0.628120  0.054526 -0.559137 -0.585355 -1.277910   \n",
       "108163.0          -0.610869  0.140408  0.183683  0.548320 -0.586559 -0.341316   \n",
       "108164.0          -3.034726  0.285810  0.079621 -0.191005 -0.294464 -0.154292   \n",
       "108165.0          -0.529857  0.385949  0.114382 -0.368625 -0.642673 -0.546929   \n",
       "108166.0          -0.588236  0.420315 -0.065497 -0.845241 -0.374031 -0.325192   \n",
       "...                     ...       ...       ...       ...       ...       ...   \n",
       "1599.0            -5.609256  0.673468 -0.236819 -0.654580 -1.610266 -0.887134   \n",
       "1600.0            -2.708468  0.691975 -0.155513 -0.738500 -1.627340  0.300062   \n",
       "1601.0             4.606073  0.679786 -0.120099 -0.708968 -1.523231 -0.531936   \n",
       "1602.0             0.861938  0.750276 -0.095188 -0.817323 -1.588656 -0.425593   \n",
       "1723.0             0.312500  0.654090 -0.006956 -0.798081 -1.611042 -0.087794   \n",
       "\n",
       "                          7  ...        40        41        42        43  \\\n",
       "improve_sample_id            ...                                           \n",
       "108162.0           0.693328  ...  0.199645  0.091887  0.547483  0.072149   \n",
       "108163.0          -0.113022  ... -0.099538  0.067979 -1.270018 -0.067538   \n",
       "108164.0          -0.603271  ...  0.030159  0.065285  0.101063  0.196396   \n",
       "108165.0          -0.953669  ... -0.053926  0.077217 -1.409377 -0.026639   \n",
       "108166.0          -0.751451  ...  0.004814  0.002132 -1.038090 -0.079290   \n",
       "...                     ...  ...       ...       ...       ...       ...   \n",
       "1599.0            -1.008669  ... -0.010750  0.115486 -0.303314 -0.262494   \n",
       "1600.0            -1.174452  ... -0.028800  0.037832 -1.430725 -0.255326   \n",
       "1601.0             0.041633  ...  0.033462  0.152978 -0.122706 -0.257496   \n",
       "1602.0             0.908445  ... -0.007114  0.187012 -1.614214 -0.218267   \n",
       "1723.0            -0.214276  ...  0.138072  0.089488 -0.590358 -0.324322   \n",
       "\n",
       "                         44        45        46        47        48        49  \n",
       "improve_sample_id                                                              \n",
       "108162.0           2.115180  1.866966  1.284497  0.188087  0.708378  0.007651  \n",
       "108163.0           0.993388  1.163411  1.241985  0.085117 -0.703866 -0.116846  \n",
       "108164.0           0.349092  1.193105  1.093049  0.069530 -0.820990 -0.904080  \n",
       "108165.0           1.860669  1.069203  1.026686  0.091033 -1.176576 -0.373831  \n",
       "108166.0           1.645097  1.394155  0.990790 -0.008248  0.691237  0.176602  \n",
       "...                     ...       ...       ...       ...       ...       ...  \n",
       "1599.0             1.433354  2.353862  2.949597  0.539274 -0.746637 -1.690494  \n",
       "1600.0             2.557380  2.800176  3.663093  0.470992 -1.035416 -1.604021  \n",
       "1601.0             1.208074  2.762115  2.343506  0.526297 -1.190930 -1.442815  \n",
       "1602.0             1.654389  2.160376  3.187936  0.540135 -1.236527 -1.427157  \n",
       "1723.0             0.552230  2.740617  3.024638  0.533053  0.228939 -1.359070  \n",
       "\n",
       "[996 rows x 52 columns]"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "7e665ffb-b808-4029-a145-162e345bb90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_columns = euc_n.columns[2:]\n",
    "euc_n['mdl_typ_dstncs'] = euc_n.apply(\n",
    "    lambda row: mdl_typ_dist(row, feature_columns, euc_n), axis=1)\n",
    "euc_n['cncr_typ_dstncs'] = euc_n.apply(\n",
    "    lambda row: cncr_typ_dist(row, feature_columns, euc_n), axis=1)\n",
    "new_cols = ['cancer_type', 'model_type', 'cncr_typ_dstncs', 'mdl_typ_dstncs'] + list(feature_columns)\n",
    "euc_n = euc_n[new_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "05575188-9ff5-4477-b0f4-5769b3018cbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cancer_type</th>\n",
       "      <th>model_type</th>\n",
       "      <th>cncr_typ_dstncs</th>\n",
       "      <th>mdl_typ_dstncs</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>...</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>improve_sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>108162.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>16.348655</td>\n",
       "      <td>32.727076</td>\n",
       "      <td>1.439899</td>\n",
       "      <td>1.533506</td>\n",
       "      <td>0.628120</td>\n",
       "      <td>0.054526</td>\n",
       "      <td>-0.559137</td>\n",
       "      <td>-0.585355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.199645</td>\n",
       "      <td>0.091887</td>\n",
       "      <td>0.547483</td>\n",
       "      <td>0.072149</td>\n",
       "      <td>2.115180</td>\n",
       "      <td>1.866966</td>\n",
       "      <td>1.284497</td>\n",
       "      <td>0.188087</td>\n",
       "      <td>0.708378</td>\n",
       "      <td>0.007651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108163.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>17.218782</td>\n",
       "      <td>18.775142</td>\n",
       "      <td>-0.953706</td>\n",
       "      <td>-0.610869</td>\n",
       "      <td>0.140408</td>\n",
       "      <td>0.183683</td>\n",
       "      <td>0.548320</td>\n",
       "      <td>-0.586559</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.099538</td>\n",
       "      <td>0.067979</td>\n",
       "      <td>-1.270018</td>\n",
       "      <td>-0.067538</td>\n",
       "      <td>0.993388</td>\n",
       "      <td>1.163411</td>\n",
       "      <td>1.241985</td>\n",
       "      <td>0.085117</td>\n",
       "      <td>-0.703866</td>\n",
       "      <td>-0.116846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108164.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>8.318765</td>\n",
       "      <td>26.687028</td>\n",
       "      <td>0.528911</td>\n",
       "      <td>-3.034726</td>\n",
       "      <td>0.285810</td>\n",
       "      <td>0.079621</td>\n",
       "      <td>-0.191005</td>\n",
       "      <td>-0.294464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030159</td>\n",
       "      <td>0.065285</td>\n",
       "      <td>0.101063</td>\n",
       "      <td>0.196396</td>\n",
       "      <td>0.349092</td>\n",
       "      <td>1.193105</td>\n",
       "      <td>1.093049</td>\n",
       "      <td>0.069530</td>\n",
       "      <td>-0.820990</td>\n",
       "      <td>-0.904080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108165.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>16.241021</td>\n",
       "      <td>40.592178</td>\n",
       "      <td>-0.114301</td>\n",
       "      <td>-0.529857</td>\n",
       "      <td>0.385949</td>\n",
       "      <td>0.114382</td>\n",
       "      <td>-0.368625</td>\n",
       "      <td>-0.642673</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053926</td>\n",
       "      <td>0.077217</td>\n",
       "      <td>-1.409377</td>\n",
       "      <td>-0.026639</td>\n",
       "      <td>1.860669</td>\n",
       "      <td>1.069203</td>\n",
       "      <td>1.026686</td>\n",
       "      <td>0.091033</td>\n",
       "      <td>-1.176576</td>\n",
       "      <td>-0.373831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108166.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>21.657577</td>\n",
       "      <td>38.186666</td>\n",
       "      <td>0.029109</td>\n",
       "      <td>-0.588236</td>\n",
       "      <td>0.420315</td>\n",
       "      <td>-0.065497</td>\n",
       "      <td>-0.845241</td>\n",
       "      <td>-0.374031</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004814</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>-1.038090</td>\n",
       "      <td>-0.079290</td>\n",
       "      <td>1.645097</td>\n",
       "      <td>1.394155</td>\n",
       "      <td>0.990790</td>\n",
       "      <td>-0.008248</td>\n",
       "      <td>0.691237</td>\n",
       "      <td>0.176602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>244.127197</td>\n",
       "      <td>246.334473</td>\n",
       "      <td>1.073569</td>\n",
       "      <td>-5.609256</td>\n",
       "      <td>0.673468</td>\n",
       "      <td>-0.236819</td>\n",
       "      <td>-0.654580</td>\n",
       "      <td>-1.610266</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010750</td>\n",
       "      <td>0.115486</td>\n",
       "      <td>-0.303314</td>\n",
       "      <td>-0.262494</td>\n",
       "      <td>1.433354</td>\n",
       "      <td>2.353862</td>\n",
       "      <td>2.949597</td>\n",
       "      <td>0.539274</td>\n",
       "      <td>-0.746637</td>\n",
       "      <td>-1.690494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>266.999794</td>\n",
       "      <td>271.470495</td>\n",
       "      <td>1.457700</td>\n",
       "      <td>-2.708468</td>\n",
       "      <td>0.691975</td>\n",
       "      <td>-0.155513</td>\n",
       "      <td>-0.738500</td>\n",
       "      <td>-1.627340</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.028800</td>\n",
       "      <td>0.037832</td>\n",
       "      <td>-1.430725</td>\n",
       "      <td>-0.255326</td>\n",
       "      <td>2.557380</td>\n",
       "      <td>2.800176</td>\n",
       "      <td>3.663093</td>\n",
       "      <td>0.470992</td>\n",
       "      <td>-1.035416</td>\n",
       "      <td>-1.604021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>91.200281</td>\n",
       "      <td>86.140240</td>\n",
       "      <td>2.678715</td>\n",
       "      <td>4.606073</td>\n",
       "      <td>0.679786</td>\n",
       "      <td>-0.120099</td>\n",
       "      <td>-0.708968</td>\n",
       "      <td>-1.523231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033462</td>\n",
       "      <td>0.152978</td>\n",
       "      <td>-0.122706</td>\n",
       "      <td>-0.257496</td>\n",
       "      <td>1.208074</td>\n",
       "      <td>2.762115</td>\n",
       "      <td>2.343506</td>\n",
       "      <td>0.526297</td>\n",
       "      <td>-1.190930</td>\n",
       "      <td>-1.442815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602.0</th>\n",
       "      <td>Clear cell renal cell carcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>109.059069</td>\n",
       "      <td>102.874765</td>\n",
       "      <td>2.286318</td>\n",
       "      <td>0.861938</td>\n",
       "      <td>0.750276</td>\n",
       "      <td>-0.095188</td>\n",
       "      <td>-0.817323</td>\n",
       "      <td>-1.588656</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007114</td>\n",
       "      <td>0.187012</td>\n",
       "      <td>-1.614214</td>\n",
       "      <td>-0.218267</td>\n",
       "      <td>1.654389</td>\n",
       "      <td>2.160376</td>\n",
       "      <td>3.187936</td>\n",
       "      <td>0.540135</td>\n",
       "      <td>-1.236527</td>\n",
       "      <td>-1.427157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723.0</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>cell line</td>\n",
       "      <td>403.915079</td>\n",
       "      <td>402.727439</td>\n",
       "      <td>0.893219</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.654090</td>\n",
       "      <td>-0.006956</td>\n",
       "      <td>-0.798081</td>\n",
       "      <td>-1.611042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138072</td>\n",
       "      <td>0.089488</td>\n",
       "      <td>-0.590358</td>\n",
       "      <td>-0.324322</td>\n",
       "      <td>0.552230</td>\n",
       "      <td>2.740617</td>\n",
       "      <td>3.024638</td>\n",
       "      <td>0.533053</td>\n",
       "      <td>0.228939</td>\n",
       "      <td>-1.359070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>996 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       cancer_type model_type  \\\n",
       "improve_sample_id                                               \n",
       "108162.0                          Breast carcinoma      Tumor   \n",
       "108163.0                          Breast carcinoma      Tumor   \n",
       "108164.0                          Breast carcinoma      Tumor   \n",
       "108165.0                          Breast carcinoma      Tumor   \n",
       "108166.0                          Breast carcinoma      Tumor   \n",
       "...                                            ...        ...   \n",
       "1599.0             Clear cell renal cell carcinoma  cell line   \n",
       "1600.0             Clear cell renal cell carcinoma  cell line   \n",
       "1601.0             Clear cell renal cell carcinoma  cell line   \n",
       "1602.0             Clear cell renal cell carcinoma  cell line   \n",
       "1723.0                         Lung adenocarcinoma  cell line   \n",
       "\n",
       "                   cncr_typ_dstncs  mdl_typ_dstncs         0         1  \\\n",
       "improve_sample_id                                                        \n",
       "108162.0                 16.348655       32.727076  1.439899  1.533506   \n",
       "108163.0                 17.218782       18.775142 -0.953706 -0.610869   \n",
       "108164.0                  8.318765       26.687028  0.528911 -3.034726   \n",
       "108165.0                 16.241021       40.592178 -0.114301 -0.529857   \n",
       "108166.0                 21.657577       38.186666  0.029109 -0.588236   \n",
       "...                            ...             ...       ...       ...   \n",
       "1599.0                  244.127197      246.334473  1.073569 -5.609256   \n",
       "1600.0                  266.999794      271.470495  1.457700 -2.708468   \n",
       "1601.0                   91.200281       86.140240  2.678715  4.606073   \n",
       "1602.0                  109.059069      102.874765  2.286318  0.861938   \n",
       "1723.0                  403.915079      402.727439  0.893219  0.312500   \n",
       "\n",
       "                          2         3         4         5  ...        40  \\\n",
       "improve_sample_id                                          ...             \n",
       "108162.0           0.628120  0.054526 -0.559137 -0.585355  ...  0.199645   \n",
       "108163.0           0.140408  0.183683  0.548320 -0.586559  ... -0.099538   \n",
       "108164.0           0.285810  0.079621 -0.191005 -0.294464  ...  0.030159   \n",
       "108165.0           0.385949  0.114382 -0.368625 -0.642673  ... -0.053926   \n",
       "108166.0           0.420315 -0.065497 -0.845241 -0.374031  ...  0.004814   \n",
       "...                     ...       ...       ...       ...  ...       ...   \n",
       "1599.0             0.673468 -0.236819 -0.654580 -1.610266  ... -0.010750   \n",
       "1600.0             0.691975 -0.155513 -0.738500 -1.627340  ... -0.028800   \n",
       "1601.0             0.679786 -0.120099 -0.708968 -1.523231  ...  0.033462   \n",
       "1602.0             0.750276 -0.095188 -0.817323 -1.588656  ... -0.007114   \n",
       "1723.0             0.654090 -0.006956 -0.798081 -1.611042  ...  0.138072   \n",
       "\n",
       "                         41        42        43        44        45        46  \\\n",
       "improve_sample_id                                                               \n",
       "108162.0           0.091887  0.547483  0.072149  2.115180  1.866966  1.284497   \n",
       "108163.0           0.067979 -1.270018 -0.067538  0.993388  1.163411  1.241985   \n",
       "108164.0           0.065285  0.101063  0.196396  0.349092  1.193105  1.093049   \n",
       "108165.0           0.077217 -1.409377 -0.026639  1.860669  1.069203  1.026686   \n",
       "108166.0           0.002132 -1.038090 -0.079290  1.645097  1.394155  0.990790   \n",
       "...                     ...       ...       ...       ...       ...       ...   \n",
       "1599.0             0.115486 -0.303314 -0.262494  1.433354  2.353862  2.949597   \n",
       "1600.0             0.037832 -1.430725 -0.255326  2.557380  2.800176  3.663093   \n",
       "1601.0             0.152978 -0.122706 -0.257496  1.208074  2.762115  2.343506   \n",
       "1602.0             0.187012 -1.614214 -0.218267  1.654389  2.160376  3.187936   \n",
       "1723.0             0.089488 -0.590358 -0.324322  0.552230  2.740617  3.024638   \n",
       "\n",
       "                         47        48        49  \n",
       "improve_sample_id                                \n",
       "108162.0           0.188087  0.708378  0.007651  \n",
       "108163.0           0.085117 -0.703866 -0.116846  \n",
       "108164.0           0.069530 -0.820990 -0.904080  \n",
       "108165.0           0.091033 -1.176576 -0.373831  \n",
       "108166.0          -0.008248  0.691237  0.176602  \n",
       "...                     ...       ...       ...  \n",
       "1599.0             0.539274 -0.746637 -1.690494  \n",
       "1600.0             0.470992 -1.035416 -1.604021  \n",
       "1601.0             0.526297 -1.190930 -1.442815  \n",
       "1602.0             0.540135 -1.236527 -1.427157  \n",
       "1723.0             0.533053  0.228939 -1.359070  \n",
       "\n",
       "[996 rows x 54 columns]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euc_n # good, keep, 2023-09-22"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03f67733-8cc8-4631-b073-8659d9c93023",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Toggle vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "7c406afd-f573-445d-b0af-4102625990ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "quant_mode = 'model_type' # model type, left to right, this is a black and white ridgeline plot\n",
    "dist_typ = 'mdl_typ_dstncs'\n",
    "quant_ttl = 'Model Type'\n",
    "mode = 'model_type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff465d3a-8fd2-4970-a409-fc5d9deb8a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "quant_mode = 'cancer_type'\n",
    "dist_typ = 'cncr_typ_dstncs'\n",
    "quant_ttl = 'Cancer Type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ea1e0da-00e1-44b3-b116-68319f1b57f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'cancer_type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "94f5e0c2-28ba-4cee-81ef-265ad3418863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cell_line_+_cptac'"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plat # where is this from, what else is there?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ddd13ec-7593-4f9c-a831-cd85c062f9eb",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Average distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "221cc226-3aef-474b-a6fa-a321c4aa449e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'model_type'"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant_mode # want greyscale here for model_type\n",
    "            # want TCGA colors for cancer_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "f62db86b-b04d-4407-9c55-afca1d98170d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mdl_typ_dstncs'"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_typ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1bbb542-7140-4614-b3f6-1c2479aff214",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### devel record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "3dd97bc7-1a5f-4bc7-a8e0-16e14ec4b89b",
   "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>mdl_typ_dstncs</th>\n",
       "      <th>mdl_typ_dstncs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tumor</th>\n",
       "      <td>337.222562</td>\n",
       "      <td>337.222562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell line</th>\n",
       "      <td>422.125635</td>\n",
       "      <td>422.125635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mdl_typ_dstncs  mdl_typ_dstncs\n",
       "model_type                                \n",
       "Tumor           337.222562      337.222562\n",
       "cell line       422.125635      422.125635"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euc_n.groupby('model_type')['mdl_typ_dstncs'].mean() # Failure mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "69037d14-ef2b-4bc2-95a4-8cc4a8ec4883",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cancer_type</th>\n",
       "      <th>model_type</th>\n",
       "      <th>cncr_typ_dstncs</th>\n",
       "      <th>mdl_typ_dstncs</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>...</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>improve_sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>108162.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>16.348655</td>\n",
       "      <td>32.727076</td>\n",
       "      <td>1.439899</td>\n",
       "      <td>1.533506</td>\n",
       "      <td>0.62812</td>\n",
       "      <td>0.054526</td>\n",
       "      <td>-0.559137</td>\n",
       "      <td>-0.585355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.199645</td>\n",
       "      <td>0.091887</td>\n",
       "      <td>0.547483</td>\n",
       "      <td>0.072149</td>\n",
       "      <td>2.11518</td>\n",
       "      <td>1.866966</td>\n",
       "      <td>1.284497</td>\n",
       "      <td>0.188087</td>\n",
       "      <td>0.708378</td>\n",
       "      <td>0.007651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        cancer_type model_type  cncr_typ_dstncs  \\\n",
       "improve_sample_id                                                 \n",
       "108162.0           Breast carcinoma      Tumor        16.348655   \n",
       "\n",
       "                   mdl_typ_dstncs         0         1        2         3  \\\n",
       "improve_sample_id                                                          \n",
       "108162.0                32.727076  1.439899  1.533506  0.62812  0.054526   \n",
       "\n",
       "                          4         5  ...        40        41        42  \\\n",
       "improve_sample_id                      ...                                 \n",
       "108162.0          -0.559137 -0.585355  ...  0.199645  0.091887  0.547483   \n",
       "\n",
       "                         43       44        45        46        47        48  \\\n",
       "improve_sample_id                                                              \n",
       "108162.0           0.072149  2.11518  1.866966  1.284497  0.188087  0.708378   \n",
       "\n",
       "                         49  \n",
       "improve_sample_id            \n",
       "108162.0           0.007651  \n",
       "\n",
       "[1 rows x 54 columns]"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euc_n.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "74e60fb4-e18f-470e-b312-2655ddbb00d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "average_distances = euc_n.groupby('model_type')['mdl_typ_dstncs'].mean().sort_values(ascending=False) # Failure mode corrected, dupe columns inserted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "176e6a40-ab80-4ec5-b6b9-bd948e7f61f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Look at both labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "9777a1a9-fdd4-4b3c-84fe-0e8600ca8c88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Tumor', 'cell line'], dtype=object)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_df.model_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "5dfee19c-f3ae-45fc-a292-06e0c6649c8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Breast carcinoma', 'Colon adenocarcinoma',\n",
       "       'Lung squamous cell carcinoma', 'Lung adenocarcinoma',\n",
       "       'Ovarian carcinoma', 'Pancreatic ductal adenocarcinoma',\n",
       "       'Clear cell renal cell carcinoma'], dtype=object)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_df.cancer_type.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5766f78e-7ba2-4e28-9217-9793722ab927",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### continue ridgeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01356bd8-7d2a-4794-8dc1-527c1f3de5b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check-in goal: mdl_sys_correction_v0, on GEXP - polished notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "b408dec0-b108-4bff-bbc8-139223893c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "average_distances = euc_n.groupby(quant_mode)[dist_typ].mean().sort_values(ascending=False) # Auto mode, keep for v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "b0f09a46-8d27-4256-87d6-bf4484787a10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model_type\n",
       "cell line    260.804179\n",
       "Tumor         35.866546\n",
       "Name: mdl_typ_dstncs, dtype: float64"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_distances "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "f3b5a236-54a1-48dd-a4ee-6f69e908a8d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_df = euc_n.loc[euc_n[quant_mode].isin(average_distances.index)]\n",
    "sorted_df[quant_mode] = pd.Categorical(\n",
    "    sorted_df[quant_mode], categories=average_distances.index, ordered=True)\n",
    "sorted_df = sorted_df.sort_values(quant_mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "0cebe72f-3071-451c-a3f7-c0df7a60b79d",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_typ = 'cnvr'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f2db4b4-7581-4a64-8d36-6278a3f10393",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### Set parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "6d80de19-3369-4aee-bc49-5ff8de58f18c",
   "metadata": {},
   "outputs": [],
   "source": [
    "grey_colors = pd.DataFrame({'quant_mode_color': ['#999999', '#666666']},\n",
    "                           index = ['cell line', 'Tumor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "78405abf-9fee-4268-89a3-6129814c6d6a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cohort_color</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tcga_cohorts</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACC</th>\n",
       "      <td>#C1A72F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLCA</th>\n",
       "      <td>#FAD2D9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRCA</th>\n",
       "      <td>#ED2891</td>\n",
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       "    <tr>\n",
       "      <th>CESC</th>\n",
       "      <td>#F6B667</td>\n",
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       "    <tr>\n",
       "      <th>COADREAD</th>\n",
       "      <td>#9EDDF9</td>\n",
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       "      <th>ESCC</th>\n",
       "      <td>#007EB5</td>\n",
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       "      <th>GEA</th>\n",
       "      <td>#909485</td>\n",
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       "    <tr>\n",
       "      <th>HNSC</th>\n",
       "      <td>#97D1A9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIRCKICH</th>\n",
       "      <td>#F8AFB3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIRP</th>\n",
       "      <td>#EA7075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGGGBM</th>\n",
       "      <td>#D49DC7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIHCCHOL</th>\n",
       "      <td>#CACCDB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUAD</th>\n",
       "      <td>#D3C3E0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUSC</th>\n",
       "      <td>#A084BD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MESO</th>\n",
       "      <td>#542C88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OV</th>\n",
       "      <td>#D97D25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAAD</th>\n",
       "      <td>#6E7BA2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCPG</th>\n",
       "      <td>#E8C51D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRAD</th>\n",
       "      <td>#7E1918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SARC</th>\n",
       "      <td>#00A99D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKCM</th>\n",
       "      <td>#BBD642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGCT</th>\n",
       "      <td>#BE1E2D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THCA</th>\n",
       "      <td>#F9ED32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THYM</th>\n",
       "      <td>#CEAC8F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UCEC</th>\n",
       "      <td>#FBE3C7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UVM</th>\n",
       "      <td>#009444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cohort_color\n",
       "tcga_cohorts             \n",
       "ACC               #C1A72F\n",
       "BLCA              #FAD2D9\n",
       "BRCA              #ED2891\n",
       "CESC              #F6B667\n",
       "COADREAD          #9EDDF9\n",
       "ESCC              #007EB5\n",
       "GEA               #909485\n",
       "HNSC              #97D1A9\n",
       "KIRCKICH          #F8AFB3\n",
       "KIRP              #EA7075\n",
       "LGGGBM            #D49DC7\n",
       "LIHCCHOL          #CACCDB\n",
       "LUAD              #D3C3E0\n",
       "LUSC              #A084BD\n",
       "MESO              #542C88\n",
       "OV                #D97D25\n",
       "PAAD              #6E7BA2\n",
       "PCPG              #E8C51D\n",
       "PRAD              #7E1918\n",
       "SARC              #00A99D\n",
       "SKCM              #BBD642\n",
       "TGCT              #BE1E2D\n",
       "THCA              #F9ED32\n",
       "THYM              #CEAC8F\n",
       "UCEC              #FBE3C7\n",
       "UVM               #009444"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tcga_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "ba4003a2-bd31-433b-bce7-cf9a7c7f177b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>from_list</strong> </div><div class=\"cmap\"><img alt=\"from_list colormap\" title=\"from_list\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#666666ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #666666ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#999999ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #999999ff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.ListedColormap at 0x2975dada0>"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custom_colormap # set at start of log reg bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "cef89182-ff70-4075-b2d8-e23eda7704c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/karlberb/Documents/work/IMPROVE/pipeline_7/production_code'"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "3c6004cb-d1ea-4074-8fba-b9aa308483e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell_line_histo.png       front_end_plots_v0.ipynb  front_end_plots_v1.ipynb\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "0378dd17-cf68-4c35-b126-7f38cd45cfb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encodable_label_terms.drawio  \u001b[34mdevel_code\u001b[m\u001b[m/\n",
      "Encodable_label_terms.png     \u001b[34mproduction_code\u001b[m\u001b[m/\n",
      "\u001b[34mcode_templates\u001b[m\u001b[m/               readme.txt\n",
      "composite_loss_vae.pptx       \u001b[34mresults\u001b[m\u001b[m/\n",
      "conda_notes.txt               \u001b[34mresults_encoded\u001b[m\u001b[m/\n"
     ]
    }
   ],
   "source": [
    "ls .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "32ca1380-60df-4c53-8c8a-3f106a1e7fca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.65, 0.2 ])"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "a5adfd69-2316-49f1-9920-16efb3430a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "joyplot(\n",
    "    data=sorted_df[[quant_mode, dist_typ]], by=quant_mode,\n",
    "    figsize=(6, 4), colormap=custom_colormap, fade=True\n",
    "    )\n",
    "# y_values = np.linspace(0.8, 0.068, 7)\n",
    "y_values = np.linspace(0.4, 0.17, 2)\n",
    "\n",
    "for y_value, cancer_type in zip(y_values, sorted_df[quant_mode].unique()):\n",
    "    count = dict(sorted_df[quant_mode].value_counts())[cancer_type]\n",
    "    x_position = sorted_df[dist_typ].max()\n",
    "    plt.annotate(f\"n={count}\", xy=(x_position, y_value), verticalalignment='center')\n",
    "\n",
    "plt.suptitle('Euclidean Distances by '+quant_ttl+', '+data_ttl,\n",
    "             # x = 0.1,\n",
    "             y = 1.06,\n",
    "             fontsize = 20\n",
    "            )\n",
    "plt.title('Cancer model systems: '+mdl_ttl+\n",
    "          # '\\nData type: '+dta_ttl+\n",
    "          '\\nn = '+str(euc_n.shape[0]),\n",
    "            x = .37, y = .89, fontsize = 18\n",
    "         )\n",
    "plt.savefig('../results_encoded/rdgln_'+quant_mode+'_'+plat+'_'+data_name+'.png', bbox_inches = 'tight',\n",
    "           dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25b258eb-405e-4b71-b598-b254fcda5083",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}