ExampleNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary packages\n",
    "# General purpose and visualisation packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import KFold\n",
    "import seaborn as sn\n",
    "import math\n",
    "import tqdm\n",
    "import gc\n",
    "# For neural network\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.preprocessing import MinMaxScaler, normalize\n",
    "# For gene selection\n",
    "import CorrelationMetrics\n",
    "from scipy.stats import ks_2samp\n",
    "from functools import reduce\n",
    "import random\n",
    "import os\n",
    "from numpy.linalg import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of rhythmic genes to select\n",
    "\n",
    "N_GENES = 50\n",
    "SEED = 0\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "tf.random.set_seed(SEED)\n",
    "os.environ['PYTHONHASHSEED'] = str(SEED)\n",
    "\n",
    "# Load training, validation, test datasets\n",
    "\n",
    "df = pd.read_csv('Data//X_train_raw.csv').T\n",
    "df_valid = pd.read_csv('Data//X_valid_raw.csv').T\n",
    "df_test = pd.concat((pd.read_csv('Data//X_test_raw_A.txt').T, pd.read_csv('Data//X_test_raw_B.txt').T)).iloc[[0, 1, 2, 4, 5], :]\n",
    "rach_clusters = pd.read_csv('Data//X_train_clusters.csv')\n",
    "Y_data = df.iloc[1:, -1].astype('float64')\n",
    "Y_copy = Y_data\n",
    "Y_valid_data = df_valid.iloc[1:, -1].astype('float64')\n",
    "Y_valid_copy = Y_valid_data\n",
    "\n",
    "# Select genes present in all three datasets\n",
    "\n",
    "common_IDs = reduce(np.intersect1d, (df.iloc[0, :-1].values, df_valid.iloc[0, :-1].values, df_test.iloc[0, :].values))\n",
    "\n",
    "idx = np.where(df.iloc[0, :].isin(common_IDs))[0]\n",
    "df = df.iloc[:, idx]\n",
    "idx_valid = np.where(df_valid.iloc[0, :].isin(common_IDs))[0]\n",
    "df_valid = df_valid.iloc[:, idx_valid]\n",
    "idx_test = np.where(df_test.iloc[0, :].isin(common_IDs))[0]\n",
    "df_test = df_test.iloc[:, idx_test]\n",
    "\n",
    "X_data = df.iloc[1:, :].astype('float64')\n",
    "X_ID = df.iloc[0, :]\n",
    "X_valid_data = df_valid.iloc[1:, :].astype('float64')\n",
    "X_valid_ID = df_valid.iloc[0, :]\n",
    "X_test_data = df_test.iloc[1:, :].astype('float64')\n",
    "X_test_ID = df_test.iloc[0, :]\n",
    "\n",
    "X_ID1 = np.argsort(X_ID)\n",
    "X_ID = X_ID.iloc[X_ID1]\n",
    "X_data = X_data.iloc[:, X_ID1]\n",
    "X_data.columns = X_ID\n",
    "X_ID1 = np.argsort(X_valid_ID)\n",
    "X_valid_ID = X_valid_ID.iloc[X_ID1]\n",
    "X_valid_data = X_valid_data.iloc[:, X_ID1]\n",
    "X_valid_data.columns = X_valid_ID\n",
    "X_ID1 = np.argsort(X_test_ID)\n",
    "X_test_ID = X_test_ID.iloc[X_ID1]\n",
    "X_test_data = X_test_data.iloc[:, X_ID1]\n",
    "X_test_data.columns = X_test_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\colmer\\Anaconda3\\envs\\dota\\lib\\site-packages\\sklearn\\model_selection\\_split.py:293: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Variance threshold\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "selector = VarianceThreshold()\n",
    "selector.fit(X_data)\n",
    "var_idx = selector.variances_ > 8\n",
    "X_data = X_data.iloc[:, var_idx]\n",
    "X_ID = X_ID.iloc[var_idx]\n",
    "X_valid_data = X_valid_data.iloc[:, var_idx]\n",
    "X_valid_ID = X_valid_ID.iloc[var_idx]\n",
    "X_test_data = X_test_data.iloc[:, var_idx]\n",
    "X_test_ID = X_test_ID.iloc[var_idx]\n",
    "\n",
    "X_data.reset_index(inplace=True, drop=True)\n",
    "X_valid_data.reset_index(inplace=True, drop=True)\n",
    "X_test_data.reset_index(inplace=True, drop=True)\n",
    "\n",
    "X_ID.reset_index(inplace=True, drop=True)\n",
    "X_valid_ID.reset_index(inplace=True, drop=True)\n",
    "X_test_ID.reset_index(inplace=True, drop=True)\n",
    "\n",
    "del df\n",
    "gc.collect()\n",
    "\n",
    "n_folds = Y_data.shape[0]\n",
    "folds = KFold(n_splits=n_folds, random_state=SEED)\n",
    "\n",
    "# Transform time onto the unit circle to enable cyclical loss function\n",
    "\n",
    "y_cos = -np.cos((2 * np.pi * Y_data.astype('float64') / 24)+(np.pi/2))\n",
    "y_sin = np.sin((2 * np.pi * Y_data.astype('float64') / 24)+(np.pi/2))\n",
    "\n",
    "Y_valid_cos = -np.cos((2 * np.pi * Y_valid_data.astype('float64') / 24)+(np.pi/2))\n",
    "Y_valid_sin = np.sin((2 * np.pi * Y_valid_data.astype('float64') / 24)+(np.pi/2))\n",
    "\n",
    "Y_data = np.concatenate((y_cos.values.reshape(-1, 1), y_sin.values.reshape(-1, 1)), axis=1)\n",
    "Y_valid_data = np.concatenate((Y_valid_cos.values.reshape(-1, 1), Y_valid_sin.values.reshape(-1, 1)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define cyclical loss function and metric\n",
    "\n",
    "def cyclical_loss(y_true, y_pred):\n",
    "    error = 0\n",
    "    for i in range(y_pred.shape[0]):\n",
    "        error += np.arccos((y_true[i, :] @ y_pred[i, :]) / (norm(y_true[i, :]) * norm(y_pred[i, :])))\n",
    "    return error\n",
    "\n",
    "def custom_loss(y_true, y_pred):\n",
    "    return tf.reduce_mean((tf.math.acos(tf.matmul(y_true, tf.transpose(y_pred)) / ((tf.norm(y_true) * tf.norm(y_pred)) + tf.keras.backend.epsilon()))**2))\n",
    "\n",
    "adam = Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
    "\n",
    "\n",
    "def larger_model():\n",
    "    # Create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(32, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(128, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(512, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(2, kernel_initializer='normal'))\n",
    "    # Compile model\n",
    "    model.compile(loss=custom_loss, optimizer=adam)\n",
    "    return model\n",
    "\n",
    "# Initialise error and prediction arrays\n",
    "error = 0\n",
    "val_errors1 = []\n",
    "test_errors1 = []\n",
    "all_preds = np.zeros((Y_data.shape[0], 2))\n",
    "all_valid_preds = np.zeros((Y_valid_data.shape[0], 2))\n",
    "early_stop = EarlyStopping(patience=50, restore_best_weights=True, monitor='val_loss', mode='min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9664/9664 [00:03<00:00, 2702.39it/s]\n",
      "100%|██████████| 9664/9664 [00:03<00:00, 2747.27it/s]\n"
     ]
    }
   ],
   "source": [
    "# Score all genes based on rhythmic metrics\n",
    "auto_indices, auto_clock_genes, auto_scores = CorrelationMetrics.get_autocorrelated_genes(X_data, X_ID)\n",
    "auto_scores = np.abs(np.array(auto_scores))\n",
    "\n",
    "cross_indices, cross_clock_genes, cross_scores = CorrelationMetrics.cross_corr(X_data, Y_copy, X_ID)\n",
    "cross_scores = np.abs(np.array(cross_scores))\n",
    "\n",
    "scores = np.concatenate((auto_scores.reshape(-1, 1), cross_scores.reshape(-1, 1)), axis=1)\n",
    "\n",
    "# Aggregate scores and select highest scoring genes\n",
    "\n",
    "auto_scores = np.argsort(np.mean(scores, axis=1))\n",
    "scores1 = np.mean(scores, axis=1)\n",
    "scores2 = np.concatenate((X_data.columns.values.reshape(-1, 1), scores1.reshape(-1, 1)), axis=1)\n",
    "auto_indices = auto_scores[-N_GENES*50:]\n",
    "auto_clock_genes = np.array(auto_clock_genes)\n",
    "auto_clock_genes = auto_clock_genes[auto_indices]\n",
    "auto_scores1 = scores1[auto_indices]\n",
    "\n",
    "idx = np.where(X_ID.isin(auto_clock_genes))[0]\n",
    "X_data = X_data.iloc[:, idx]\n",
    "scores2 = scores2[idx]\n",
    "idx_valid = np.where(X_valid_ID.isin(auto_clock_genes))[0]\n",
    "X_valid_data = X_valid_data.iloc[:, idx_valid]\n",
    "idx_test = np.where(X_test_ID.isin(auto_clock_genes))[0]\n",
    "X_test_data = X_test_data.iloc[:, idx_test]\n",
    "\n",
    "X_ID = X_ID.iloc[idx]\n",
    "X_valid_ID = X_valid_ID.iloc[idx_valid]\n",
    "X_test_ID = X_test_ID.iloc[idx_test]\n",
    "\n",
    "scores = []\n",
    "pvalues = []\n",
    "\n",
    "# Remove genes whose distribution is different between training and validation dataset\n",
    "\n",
    "for i in range(X_data.shape[1]):\n",
    "    l = ks_2samp(X_data.iloc[:, i], X_valid_data.iloc[:, i])\n",
    "    scores.append(i)\n",
    "    pvalues.append(l.pvalue)\n",
    "\n",
    "pvalues_idx = np.argsort(pvalues)\n",
    "scores = pvalues_idx[(pvalues_idx.shape[0]-30*N_GENES):]\n",
    "\n",
    "similar_genes = auto_clock_genes[scores]\n",
    "X_data = X_data.iloc[:, scores]\n",
    "scores2 = scores2[scores]\n",
    "X_ID = X_ID.iloc[scores]\n",
    "X_valid_data = X_valid_data.iloc[:, scores]\n",
    "X_test_data = X_test_data.iloc[:, scores]\n",
    "auto_scores1 = auto_scores1[scores]\n",
    "\n",
    "Y_copy_res = np.array([0, 4, 8, 12, 16, 20, 0, 4, 8, 12, 16, 20])\n",
    "\n",
    "X_ID2 = X_data.columns.values\n",
    "\n",
    "# Scale expression data before entering neural network\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_data)\n",
    "X_data = scaler.transform(X_data)\n",
    "X_valid_data = scaler.transform(X_valid_data)\n",
    "X_test_data = scaler.transform(X_test_data)\n",
    "\n",
    "X_data = pd.DataFrame(data=X_data, columns=X_ID2)\n",
    "X_valid_data = pd.DataFrame(data=X_valid_data, columns=X_ID2)\n",
    "X_test_data = pd.DataFrame(data=X_test_data, columns=X_ID2)\n",
    "\n",
    "# Remove genes whose expression level is different between training and validation\n",
    "\n",
    "column_max = np.max(X_valid_data.values, axis=0)\n",
    "column_min = np.min(X_valid_data.values, axis=0)\n",
    "column_idx = column_max < 1.4\n",
    "column_idx1 = column_min > -0.2\n",
    "column_idx = np.logical_and(column_idx, column_idx1)\n",
    "X_data = X_data.iloc[:, column_idx]\n",
    "scores2 = scores2[column_idx]\n",
    "X_ID = X_ID.iloc[column_idx]\n",
    "X_valid_data = X_valid_data.iloc[:, column_idx]\n",
    "X_test_data = X_test_data.iloc[:, column_idx]\n",
    "auto_scores1 = auto_scores1[column_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "turquoise    208\n",
      "yellow       197\n",
      "blue         168\n",
      "None         156\n",
      "brown        142\n",
      "red          103\n",
      "green        100\n",
      "pink          54\n",
      "black         40\n",
      "Name: moduleColor, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-7af684b52f0d>:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  genes.sort_values(by='score', inplace=True, ascending=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5000\n",
      "6/6 [==============================] - 0s 19ms/step - loss: 2.9822 - val_loss: 3.0073\n",
      "Epoch 2/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.7119 - val_loss: 2.9809\n",
      "Epoch 3/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.8174 - val_loss: 3.0163\n",
      "Epoch 4/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.8771 - val_loss: 3.1497\n",
      "Epoch 5/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.5422 - val_loss: 3.3714\n",
      "Epoch 6/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 3.1345 - val_loss: 3.4724\n",
      "Epoch 7/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 3.0468 - val_loss: 3.3697\n",
      "Epoch 8/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 3.0127 - val_loss: 3.1924\n",
      "Epoch 9/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.5860 - val_loss: 2.9744\n",
      "Epoch 10/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.7676 - val_loss: 2.8831\n",
      "Epoch 11/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.5973 - val_loss: 2.8770\n",
      "Epoch 12/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.6235 - val_loss: 2.8896\n",
      "Epoch 13/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.7848 - val_loss: 2.9254\n",
      "Epoch 14/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 2.9982 - val_loss: 3.0077\n",
      "Epoch 15/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.5735 - val_loss: 2.9497\n",
      "Epoch 16/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 2.5736 - val_loss: 2.8848\n",
      "Epoch 17/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.5060 - val_loss: 2.7587\n",
      "Epoch 18/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.5236 - val_loss: 2.6532\n",
      "Epoch 19/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.6200 - val_loss: 2.6251\n",
      "Epoch 20/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.3499 - val_loss: 2.5179\n",
      "Epoch 21/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.5467 - val_loss: 2.4640\n",
      "Epoch 22/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.5748 - val_loss: 2.4521\n",
      "Epoch 23/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.4583 - val_loss: 2.4763\n",
      "Epoch 24/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.3348 - val_loss: 2.5028\n",
      "Epoch 25/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.2885 - val_loss: 2.4894\n",
      "Epoch 26/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.3767 - val_loss: 2.4658\n",
      "Epoch 27/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.4081 - val_loss: 2.4657\n",
      "Epoch 28/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.4144 - val_loss: 2.4901\n",
      "Epoch 29/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.6523 - val_loss: 2.4789\n",
      "Epoch 30/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.1938 - val_loss: 2.1190\n",
      "Epoch 31/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.2367 - val_loss: 1.8493\n",
      "Epoch 32/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.2313 - val_loss: 1.6727\n",
      "Epoch 33/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.2028 - val_loss: 1.4895\n",
      "Epoch 34/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.6646 - val_loss: 1.1817\n",
      "Epoch 35/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.3118 - val_loss: 0.8206\n",
      "Epoch 36/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.0297 - val_loss: 0.7097\n",
      "Epoch 37/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.0458 - val_loss: 0.5692\n",
      "Epoch 38/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.8125 - val_loss: 0.6271\n",
      "Epoch 39/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 2.0872 - val_loss: 0.6235\n",
      "Epoch 40/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.7171 - val_loss: 0.6804\n",
      "Epoch 41/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.9391 - val_loss: 0.6261\n",
      "Epoch 42/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.9264 - val_loss: 0.4580\n",
      "Epoch 43/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.2736 - val_loss: 0.4498\n",
      "Epoch 44/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.0125 - val_loss: 0.4255\n",
      "Epoch 45/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.6699 - val_loss: 0.3940\n",
      "Epoch 46/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.3903 - val_loss: 0.2696\n",
      "Epoch 47/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.7296 - val_loss: 0.2760\n",
      "Epoch 48/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 2.2515 - val_loss: 0.2686\n",
      "Epoch 49/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.9463 - val_loss: 0.4261\n",
      "Epoch 50/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.6439 - val_loss: 0.4559\n",
      "Epoch 51/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.9226 - val_loss: 0.3709\n",
      "Epoch 52/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.0327 - val_loss: 0.2888\n",
      "Epoch 53/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.6504 - val_loss: 0.2186\n",
      "Epoch 54/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.8014 - val_loss: 0.1737\n",
      "Epoch 55/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.6120 - val_loss: 0.1998\n",
      "Epoch 56/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.8709 - val_loss: 0.1743\n",
      "Epoch 57/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.6999 - val_loss: 0.2068\n",
      "Epoch 58/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.6368 - val_loss: 0.2093\n",
      "Epoch 59/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.1193 - val_loss: 0.1935\n",
      "Epoch 60/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.6758 - val_loss: 0.2070\n",
      "Epoch 61/5000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 3ms/step - loss: 1.5217 - val_loss: 0.0022\n",
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      "0.000490563715303972\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 2ms/step - loss: 2.0698 - val_loss: 0.0024\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 3/5000\n",
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      "0.14025677016503646\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 116/5000\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 2ms/step - loss: 1.5710 - val_loss: 0.0714\n",
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      "0.009345152731727155\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 76/5000\n",
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      "0.03472464970248088\n",
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      "0.0006621857166201141\n",
      "Epoch 1/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 16ms/step - loss: 1.8768 - val_loss: 0.4502\n",
      "Epoch 2/5000\n",
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      "6/6 [==============================] - 0s 3ms/step - loss: 1.6605 - val_loss: 0.0268\n",
      "Epoch 80/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.5534 - val_loss: 0.0331\n",
      "Epoch 81/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.4033 - val_loss: 0.0359\n",
      "Epoch 82/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.9688 - val_loss: 0.0417\n",
      "Epoch 83/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 2ms/step - loss: 1.3126 - val_loss: 0.0359\n",
      "Epoch 84/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.8324 - val_loss: 0.0204\n",
      "Epoch 85/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.4465 - val_loss: 0.0114\n",
      "Epoch 86/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.7627 - val_loss: 0.0046\n",
      "Epoch 87/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.8929 - val_loss: 0.0031\n",
      "Epoch 88/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.1028 - val_loss: 0.0014\n",
      "Epoch 89/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.6279 - val_loss: 7.4864e-05\n",
      "Epoch 90/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.7225 - val_loss: 0.0026\n",
      "Epoch 91/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.5386 - val_loss: 2.6966e-04\n",
      "Epoch 92/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 2.0951 - val_loss: 0.0037\n",
      "Epoch 93/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.1182 - val_loss: 0.0100\n",
      "Epoch 94/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.7593 - val_loss: 0.0158\n",
      "Epoch 95/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.9413 - val_loss: 0.0179\n",
      "Epoch 96/5000\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.6394 - val_loss: 0.0131\n",
      "Epoch 97/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.5568 - val_loss: 0.0074\n",
      "Epoch 98/5000\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.7983 - val_loss: 0.0018\n",
      "0.001385340905699398\n"
     ]
    }
   ],
   "source": [
    "# Primitive method of selecting genes belonging from diverse clusters, sequential method included in separate .py file\n",
    "score_df = pd.DataFrame(data=scores2, columns=['transcript', 'score'])\n",
    "cluster_score_df = pd.merge(score_df, rach_clusters, how='left', left_on='transcript', right_on='transcript')\n",
    "\n",
    "cluster_score_df.fillna(value='None', inplace=True)\n",
    "print(cluster_score_df['moduleColor'].value_counts())\n",
    "\n",
    "colours = cluster_score_df['moduleColor'].unique()\n",
    "\n",
    "results = {'idx': [], 'train_error': [], 'val_error': [], 'test_error': []}\n",
    "results1 = []\n",
    "idx_perm = []\n",
    "graph_res = []\n",
    "\n",
    "n_genes = 0\n",
    "i = 0\n",
    "counter = 0\n",
    "\n",
    "keep = []\n",
    "\n",
    "for i in range(colours.shape[0]):\n",
    "    colour = colours[i]\n",
    "    genes = cluster_score_df.loc[cluster_score_df['moduleColor'] == colour]\n",
    "    genes.sort_values(by='score', inplace=True, ascending=False)\n",
    "    keep.append(genes.index.values[:8])\n",
    "\n",
    "keep = np.concatenate(keep)\n",
    "cluster_score_df = cluster_score_df.iloc[keep, :]\n",
    "X_data = X_data.iloc[:, keep]\n",
    "X_valid_data = X_valid_data.iloc[:, keep]\n",
    "X_test_data = X_test_data.iloc[:, keep]\n",
    "\n",
    "cluster_score_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "\n",
    "i = 0\n",
    "\n",
    "idx = [3, 12, 18, 29, 38, 44, 52, 58, 64, 8, 17, 25, 34, 49, 62]\n",
    "\n",
    "X_data = X_data.iloc[:, idx]\n",
    "X_valid_data = X_valid_data.iloc[:, idx]\n",
    "X_test_data = X_test_data.iloc[:, idx]\n",
    "\n",
    "X_data = X_data.values\n",
    "X_valid_data =X_valid_data.values\n",
    "X_test_data = X_test_data.values\n",
    "#\n",
    "valid_preds = []\n",
    "test_preds = []\n",
    "\n",
    "# K-fold cross validation fit model on train, predict on valid and test\n",
    "\n",
    "for n_fold, (train_idx, valid_idx) in enumerate(folds.split(X_data, Y_data)):\n",
    "    X_train, Y_train = X_data[train_idx], Y_data[train_idx]  # Define training data for this iteration\n",
    "    X_valid, Y_valid = X_data[valid_idx], Y_data[valid_idx]\n",
    "\n",
    "    model = larger_model()\n",
    "    model.fit(X_train.astype('float64'), Y_train.astype('float64'), validation_data=(X_valid.astype('float64'), Y_valid.astype('float64')),\n",
    "              batch_size=2, epochs=5000, callbacks=[early_stop])  # Fit the model on the training data\n",
    "    preds = normalize(model.predict(X_valid))  # Predict on the validation data\n",
    "    all_preds[valid_idx] = normalize(model.predict(X_valid))\n",
    "    all_valid_preds += (normalize(model.predict(X_valid_data)) / n_folds)\n",
    "    valid_preds.append(normalize(model.predict(X_valid_data)))\n",
    "    test_preds.append(normalize(model.predict(X_test_data)))\n",
    "    error += cyclical_loss(Y_valid.astype('float64'), preds.astype('float64'))  # Evaluate the predictions\n",
    "    print(cyclical_loss(Y_valid.astype('float64'), preds.astype('float64')) / Y_valid.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\colmer\\Anaconda3\\envs\\dota\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\colmer\\Anaconda3\\envs\\dota\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\colmer\\Anaconda3\\envs\\dota\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\colmer\\Anaconda3\\envs\\dota\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\colmer\\Anaconda3\\envs\\dota\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\colmer\\Anaconda3\\envs\\dota\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\colmer\\Anaconda3\\envs\\dota\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\colmer\\Anaconda3\\envs\\dota\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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2XtRV118LYE7QEzDf+dXLct4VaxqNOXbV0hy2rLv/GyRJvvbm5KpvNyvs0OOSBz292RgAAACAPhHABGDCOelLl+T8q9Y2GnPCkctzyIEL+lMQY/f997dOwOzWniuTR72sf/UAAABAP3R5DfmSsjXYOBFPwDzn0htz2tk/azTm4APm5/gjl3c/4JIvJt98Z7PC9jk4OeJNzcYAAAAA9JEAJgATylkXr87p517eaMxhyxbmmFVL+1QRY3b76uSsNzcYUJJnvCMZmtG3kgAAAKAvhvfoqtuSsvaef0+0EzCvv3VDTjjjwtTa/ZiFc2fl1KNXZsZQd1ew55Yrk/95VbPCdlqQPP/9rZNGAQAAACYIAUwAJoxr1q7PiWde1GjM4uHZOfmoFd1v8DN4X/7L5K5bu+//6Fckez2if/UAAABAv3R7AmbudQX5rRtSm6Qd+2jT5i057mMXZM26u7seU0py8lErsnjenC4XuTs58yXJhrUNKivJc/8t2WWvBmMAAAAA+k8AE4AJYePmLTn2I+dn7Z0bux4zVJJTjl6ZhXNn97EyxuTyc5Iffrz7/jsvTp70l/2rBwAAAPpp3p5ddVtcbknSCl3eeffm3H7Xpj4W1b13fvWynHfFmm13vJdjVy3NYcsWdT/gK/8vufb8ZoU94U+TpUc0GwMAAAAwAAKYAEwIJ33pkpx/1dpGY044cnkOOXBBfwpi7DbdlXzuxGZjnvr3yY679qUcAAAA6LsuT8CcXTZlt9x+z3++/tbxv4b8nEtvzGln/6zRmIMPmJ/jj1ze/YAffzL57r82K2z/w5MnvKHZGAAAAIABEcAEYNyddfHqnH7u5Y3GHLZsYY5ZtbRPFdET3zo1ufmy7vvv/4Tkoc/vXz0AAADQb8N7dN11SVl7z7/HO4B5/a0bcsIZF6bJTegL587KqUevzIyh0t2Am3+efOq1zQqbuyR53vuSoRnNxgEAAAAMiAAmAOPqmrXrc+KZFzUas3h4dk4+akX3G/wM3porknNP6r7/jFnJM96eFP8/BQAAYBJrFMC85Z5/X3/b+AUwN23ekuM+dkHWrLu76zGlJCcftSKL583pbsDG9cmZL07uvn3bfe9ZZCh5/vuTuYu7HwMAAAAwYAKYAIybjZu35NiPnJ+1d27sesxQSU45emUWzp3dx8oYk1qTL/xpsqnBl0ePOz5ZuKx/NQEAAMAgNApgrrnn3+N5AuY7v3pZzrtizbY73suxq5bmsGWLuh/wxTcm1/+wWWFP+svkAY9vNgYAAABgwAQwARg3J33pkpx/1dpGY044cnkOOXBBfwqiNy7+THLZl7vvv9sDksNO7Fs5AAAAMDA7LUiGZnbVdUnG/wTMcy69Maed/bNGYw4+YH6OP3J59wMuOiP5wX80K2zpk5PHndBsDAAAAMA4EMAEYFycdfHqnH7u5Y3GHLZsYY5ZtbRPFdETd93ROtWiiaeflOywY3/qAQAAgEEaGkrm7t5V11+7gnwcTsC8/tYNOeGMC1Nr92MWzp2VU49emRlDpbsBN/w0+ezrmhU2b+/kue9p/d8SAAAAYIKzgwHAwF2zdn1OPPOiRmMWD8/OyUet6H6Dn/Fx9luT267pvv9v/Hay7Mn9qwcAAAAGbV5315AvKWvv+fegA5ibNm/JcR+7IGvW3d31mFKSk49akcXz5nQ34O51yZkvTjbe2X1hQzOTF/x7stP87scAAAAAjCMBTAAGauPmLTn2I+dn7Z0bux4zVJJTjl6ZhXNn97Eyxuz6HyXf+Zfu+8+am/zmP/SvHgAAABgPw92egLnmnn8P+gryd371spx3xZptd7yXY1ctzWHLFnXXudbks69Pbvxps8Ke/LfJPo9pNgYAAABgHAlgAjBQJ33pkpx/1dpGY044cnkOOXBBfwqiN7ZsST73+qRu7n7ME/8s2WWv/tUEAAAA42G42xMwt15Bvmbd3blrU4PP1GNwzqU35rSzf9ZozMEHzM/xRy7vfsAFH0r+72PNCnvQM5ODj2k2BgAAAGCcCWACMDBnXbw6p597eaMxhy1bmGNWLe1TRfTMhf+Z/PK73fdfclDy2Ff3rx4AAAAYL10GMBfm1szI1tDlDbfd1a+K7nH9rRtywhkXptbuxyycOyunHr0yM4ZKl4v8MPn8nzQrbNf9kmed1rrnHAAAAGASEcAEYCCuWbs+J555UaMxi4dn5+SjVnS/wc/4WHdz8pU3NRvzjHckM2b2px4AAAAYT10GMGeUmoW59Z7/fN2t/b2GfNPmLTnuYxdkzbq7ux5TSnLyUSuyeN6c7gZsuC35+IuTTQ3+u8yYlbzwA8mOu3Y/BgAAAGCCEMAEoO82bt6SYz9yftbeubHrMUMlOeXolVk4d3YfK6MnvvqmZP0t2+73K4/4g2Tfx/avHgAAABhPw7t33fXe15Bff1t/A5jv/OplOe+KNY3GHLtqaQ5btqi7zrUmnz42WfPzZoX95luTPVc2GwMAAAAwQQhgAtB3J33pkpx/1dpGY044cnkOOXBBfwqid676TnLBf3bff8f5yZF/0796AAAAYLx1eQJmcp8A5q3r+1FNkuScS2/MaWf/rNGYgw+Yn+OPXN79gO+9N/nJJ5sVdtDzkke9vNkYAAAAgAlEABOAvjrr4tU5/dzLG405bNnCHLNqaZ8qomc2b0w+e0KzMU/+22Sn+f2pBwAAACaCedsbwLyrH9Xk+ls35IQzLkyt3Y9ZOHdWTj16ZWYMle4GXPOD5It/1qywBUuT3zqldc85AAAAwCQlgAlA31yzdn1OPPOiRmMWD8/OyUet6H6Dn/HznX9JbvhJ9/33OThZ8bv9qwcAAAAmgtnzkh126qrrr19B3vsTMDdt3pLjPnZB1qy7u+sxpSQnH7Uii+fN6W7A+luSM1+SbNnYfWEz5yQv/GAye7j7MQAAAAATkAAmAH2xcfOWHPuR87P2zu4334dKcsrRK7Nw7uw+VkZP3Hp1cvY/dN+/zEie+Y5kyFsPAAAAprhSkuHdu+q6JPc+AXNDz0t551cvy3lXrGk05thVS3PYskXdda41+eQxydqrmhX2jLcnSx7SbAwAAADABCQFAUBfnPSlS3L+VWsbjTnhyOU55MAF/SmI3vrCG5KN67rvf8gxvlgBAABg+hju7hrye5+Aufq23l5Bfs6lN+a0s3/WaMzBB8zP8Ucu737At9+VXPL5ZoWt+N1k5e81GwMAAAAwQQlgAtBzZ128Oqefe3mjMYctW5hjVi3tU0X01KVfSn762e77z9s7ecIb+1cPAAAATDTbFcDckC1bak+Wv/7WDTnhjAtTG0y3cO6snHr0yswYKt0NuOo7yVf+qllhix+cPP2kZmMAAAAAJjABTAB66pq163PimRc1GrN4eHZOPmpF9xv8jJ+770w+/8fNxjztH5LZc/tTDwAAAExE3V5Bfq8A5qYtNTetG/spmJs2b8lxH7sga9bd3fWYUpKTj1qRxfPmdDdg3U3JmS9N6ubuC5s1N3nhB5NZO3U/BgAAAGCCE8AEoGc2bt6SYz9yftbeubHrMUMlOeXolVk4d3YfK6NnvnFSsvaq7vsve2ryoGf2rx4AAACYiLo8AXO3ckdmZ2tQ8vpbN4x56Xd+9bKcd8WaRmOOXbU0hy1b1F3nLVuS//7D5PZrmxX2W6ckC5c1GwMAAAAwwQlgAtAzJ33pkpx/1dpGY044cnkOOXBBfwqit268JPnmqd33n7lj8vS3tY7RAAAAgOlkXncBzCRZVNbe8++xBjDPufTGnHb2zxqNOfiA+Tn+yOXdD/jG25Off61ZYY96efLQ5zcbAwAAADAJCGAC0BNnXbw6p597eaMxhy1bmGNWLe1TRfRUrcnnTky2dH+6aQ7/42S3B/StJAAAAJiwujwBM0l2z9bTKq+/bfsDmNffuiEnnHFhau1+zMK5s3Lq0SszY6jLH09efk5y9t83K2yPhydPbTgGAAAAYJKYOd4FADD5XbN2fU4886JGYxYPz87JR63ofoOfwbv16uTKbyVXfjP5xTeTmy/rfuzC5cmhx/WvNgAAAJjIhnfvuuuSsjYZCU1u7wmYmzZvyXEfuyBr1t297c4jSklOPmpFFs+b092A269P/usVSd3SfWGzd0le8IFkhy7XAAAAAJhkBDABGJONm7fk2I+cn7V3dn8y4lBJTjl6ZRbOnd3Hymik1mTN5a2w5a9Cl2uv2v75nvH2ZOas3tUHAAAAk0mDEzCXlFvu+ff2noD5zq9elvOuWLPtjvdy7KqlOWzZou46b96UfOLlybobmhX27NOS+fs3GwMAAAAwiQhgAjAmJ33pkpx/1dpGY044cnkOOXBBfwqiO1u2JDdevDVseeW3kjtW92buhx2V7H94b+YCAACAyWiHHZM5uyYb1m6z6+J7BzC34wTMcy+9Maed/bNGYw4+YH6OP3J59wPOfmty5f82K+zg1yS/8VvNxgAAAABMMgKYAGy3sy5endPPvbzRmMOWLcwxq5b2qSLa2rwpuf6ikcDlyF8XXwI1NmeX5Clv6f28AAAAMNkM79HVZ+/dy9aTK5uegLn6tg054YwLU2v3YxbsPCunHL0yM4ZKdwMu+0ryjZMa1ZW9H50c+dfNxgAAAABMQgKYAGyXa9auz4lnXtRozOLh2Tn5qBXdb/Cz/TZuSK49f+vplld9N9m4rv/rHvGmZO7i/q8DAAAAE93w7q3bJ7ZhSdbe8+/rb92QWmtK2fbeyabNW3LcRy/Izevu7rqkUpKTj1qRJfPmdDfg1quT/35l1/MnSXbcLXn+vyczZzUbBwAAADAJCWAC0NjGzVty7EfOz9o7N3Y9Zqgkpxy9Mgvnzu5jZdPYXXckV5+39XTLq7+fbL5rsDXs+YjkkS8d7JoAAAAwUc3bs6tu976C/M67N+f2uzZl3pwdtjnulLMuy3evWLPNfvf22lVLc/jyRd113rwxOfOlyfpma+Q570l23afZGAAAAIBJSgATgMZO+tIlOf+qtY3GnHDk8hxy4IL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\n",
      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average training error = 7.752380150292506 minutes\n",
      "Average validation error = 73.82027952978636 minutes\n",
      "Average test error = 168.48180602851346 minutes\n",
      "black        8\n",
      "turquoise    8\n",
      "blue         8\n",
      "None         8\n",
      "red          8\n",
      "green        8\n",
      "brown        8\n",
      "pink         8\n",
      "yellow       8\n",
      "Name: moduleColor, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Collect and print results, plot predictions with respect to ground truth\n",
    "angles = []\n",
    "for i in range(all_preds.shape[0]):\n",
    "    angles.append(math.atan2(all_preds[i, 0], all_preds[i, 1]) / math.pi * 12)\n",
    "\n",
    "for j in range(len(angles)):\n",
    "    if angles[j] < 0:\n",
    "        angles[j] = angles[j] + 24\n",
    "\n",
    "plt.figure(dpi=500)\n",
    "ax = sn.scatterplot(Y_data[:, 0], Y_data[:, 1])\n",
    "ax = sn.scatterplot(all_preds[:, 0], all_preds[:, 1])\n",
    "plt.show()\n",
    "angles_arr = np.vstack(angles)\n",
    "hour_pred = angles_arr\n",
    "\n",
    "plt.figure(dpi=500)\n",
    "ax = sn.lineplot(np.arange(Y_copy.shape[0]), Y_copy)\n",
    "ax = sn.lineplot(np.arange(Y_copy.shape[0]), angles_arr.ravel())\n",
    "plt.show()\n",
    "\n",
    "\n",
    "angles = []\n",
    "for i in range(all_preds.shape[0]):\n",
    "    angles.append(math.atan2(all_preds[i, 0], all_preds[i, 1]) / math.pi * 12)\n",
    "\n",
    "for j in range(len(angles)):\n",
    "    if angles[j] < 0:\n",
    "        angles[j] = angles[j] + 24\n",
    "\n",
    "\n",
    "valid_angles = []\n",
    "valid_preds = np.mean(valid_preds, axis=0)\n",
    "for i in range(valid_preds.shape[0]):\n",
    "    valid_angles.append(math.atan2(valid_preds[i, 0], valid_preds[i, 1]) / math.pi * 12)\n",
    "\n",
    "for j in range(len(valid_angles)):\n",
    "    if valid_angles[j] < 0:\n",
    "        valid_angles[j] = valid_angles[j] + 24\n",
    "valid_preds = normalize(valid_preds)\n",
    "plt.figure(dpi=500)\n",
    "ax = sn.scatterplot(Y_valid_data[:, 0], Y_valid_data[:, 1])\n",
    "ax = sn.scatterplot(valid_preds[:, 0], valid_preds[:, 1])\n",
    "plt.show()\n",
    "angles_arr_valid = np.vstack(valid_angles)\n",
    "hour_pred_valid = angles_arr_valid\n",
    "\n",
    "\n",
    "plt.figure(dpi=500)\n",
    "ax = sn.lineplot(np.arange(Y_valid_copy.shape[0]), Y_valid_copy)\n",
    "ax = sn.lineplot(np.arange(Y_valid_copy.shape[0]), angles_arr_valid.ravel())\n",
    "plt.show()\n",
    "\n",
    "print(\"Average training error = {} minutes\".format(60 * 12 * cyclical_loss(Y_data.astype('float64'), all_preds.astype('float64')) / (Y_data.shape[0] * np.pi)))\n",
    "\n",
    "print(\"Average validation error = {} minutes\".format(60 * 12 * cyclical_loss(Y_valid_data.astype('float64'), valid_preds.astype('float64')) / (Y_valid_data.shape[0] * np.pi)))\n",
    "\n",
    "Y_copy1 = np.array([2, 5, 8, 11, 14, 17, 20, 23, 2, 5, 8, 11, 14, 17, 20, 23])\n",
    "\n",
    "test_angles = []\n",
    "test_preds_copy = test_preds\n",
    "test_preds = np.mean(test_preds, axis=0)\n",
    "for j in range(len(test_preds_copy)):\n",
    "    for i in range(test_preds.shape[0]):\n",
    "        test_preds_copy[j][i, 0] = math.atan2(test_preds_copy[j][i, 0], test_preds_copy[j][i, 1]) / math.pi * 12\n",
    "        if test_preds_copy[j][i, 0] < 0:\n",
    "            test_preds_copy[j][i, 0] += 24\n",
    "    test_preds_copy[j] = np.delete(test_preds_copy[j], 1, 1)\n",
    "\n",
    "for i in range(test_preds.shape[0]):\n",
    "    test_angles.append(math.atan2(test_preds[i, 0], test_preds[i, 1]) / math.pi * 12)\n",
    "for j in range(len(test_angles)):\n",
    "    if test_angles[j] < 0:\n",
    "        test_angles[j] = test_angles[j] + 24\n",
    "test_preds = normalize(test_preds)\n",
    "angles_arr_test = np.vstack(test_angles)\n",
    "hour_pred_test = angles_arr_test\n",
    "Y_test = np.array([12, 0, 12, 0])\n",
    "\n",
    "Y_test_cos = -np.cos((2 * np.pi * Y_test.astype('float64') / 24) + (np.pi / 2))\n",
    "Y_test_sin = np.sin((2 * np.pi * Y_test.astype('float64') / 24) + (np.pi / 2))\n",
    "Y_test_ang = np.concatenate((Y_test_cos.reshape(-1, 1), Y_test_sin.reshape(-1, 1)), axis=1)\n",
    "print(\"Average test error = {} minutes\".format(60 * 12 * cyclical_loss(Y_test_ang.astype('float64'), test_preds.astype('float64')) / (Y_test_ang.shape[0] * np.pi)))\n",
    "val_errors1.append(60 * 12 * cyclical_loss(Y_valid_data.astype('float64'), all_valid_preds.astype('float64')) / (Y_valid_data.shape[0] * np.pi))\n",
    "test_errors1.append(60 * 12 * cyclical_loss(Y_test_ang.astype('float64'), test_preds.astype('float64')) / (Y_test_ang.shape[0] * np.pi))\n",
    "\n",
    "print(cluster_score_df['moduleColor'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}