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"# DeepDrug3D"
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "## Importing library"
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+ ]
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+ },
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"cell_type": "code",
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- "execution_count": 1,
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- "metadata": {},
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "Using TensorFlow backend.\n"
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- ]
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- }
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- ],
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+ "execution_count": 6,
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+ "metadata": {},
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+ "outputs": [],
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"source": [
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"import numpy as np\n",
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- "\n",
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+ "import tensorflow as tf\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from keras.models import Sequential\n",
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- "from keras import optimizers\n",
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- "from keras.layers import Dense, Flatten, TimeDistributedn, Dropout\n",
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+ "from keras import optimizers, callbacks\n",
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+ "from keras.layers import Dense, Flatten, TimeDistributed, Dropout\n",
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"from keras import Input, Model\n",
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"from keras.layers import add, Activation\n",
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+ "from keras.layers.advanced_activations import LeakyReLU\n",
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"#from keras.utils import plot_model # Needs pydot.\n",
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- "from keras.layers import Conv3D, MaxPooling3D"
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+ "from keras.layers import Convolution3D, MaxPooling3D"
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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- "## Create pocket lists\n",
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- "4 pockets are created :\n",
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- " + control\n",
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- " + steroid\n",
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- " + heme\n",
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- " + nucleotide"
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+ "### used to store model prediction in order to plot roc curve"
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"cell_type": "code",
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- "execution_count": 2,
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "''"
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- ]
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- },
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- "execution_count": 2,
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "with open(\"control.list\", \"r\") as filin:\n",
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- " control = filin.read()\n",
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- "control = control.split(\"\\n\")\n",
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- "control.pop()\n",
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- "\n",
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- "with open(\"steroid.list\", \"r\") as filin:\n",
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- " steroid = filin.read()\n",
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- "steroid = steroid.split(\"\\n\")\n",
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- "steroid.pop()\n",
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- "\n",
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- "with open(\"heme.list\", \"r\") as filin:\n",
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- " heme = filin.read()\n",
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- "heme = heme.split(\"\\n\")\n",
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- "heme.pop()\n",
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- "\n",
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- "with open(\"nucleotide.list\", \"r\") as filin:\n",
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- " nucleotide = filin.read()\n",
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- "nucleotide = nucleotide.split(\"\\n\")\n",
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- "nucleotide.pop()"
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "class prediction_history(callbacks.Callback):\n",
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+ " def __init__(self):\n",
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+ " self.predhis = []\n",
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+ " def on_epoch_end(self, epoch, logs={}):\n",
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+ " self.predhis.append(model.predict(predictor_train))"
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@@ -92,85 +62,169 @@
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"cell_type": "code",
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- "execution_count": 3,
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+ "execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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- "data_onehot = np.ndarray(shape=(2219, 14, 32, 32, 32)) # initializing empty array\n",
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- "indices = np.random.permutation(2219)\n",
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- "output = np.ndarray(shape=(2219, 3)) # softmax 3, {steroid=1, heme=1, nucleotide=1}\n",
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- "lmin = len(steroid)\n",
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- "lmid = len(heme)\n",
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- "lmax = len(nucleotide)"
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+ "def in_out_lists(size=1000):\n",
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+ " \"\"\"\n",
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+ " returns a tuple of array used as input and output for the model\n",
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+ " Arguments:\n",
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+ " - size, int: default 1000, size of the lists to be created\n",
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+ " \n",
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+ " Returns:\n",
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+ " - tuple (data_onehot, output):\n",
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+ " -data_onehot, ndarray: containing one-hot encoded pockets\n",
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+ " -output, ndarray: containing size-3 vectors for classification\n",
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+ " \"\"\"\n",
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+ " with open(\"control.list\", \"r\") as filin:\n",
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+ " control = filin.read()\n",
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+ " control = control.split(\"\\n\")\n",
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+ " control.pop()\n",
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+ "\n",
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+ " with open(\"steroid.list\", \"r\") as filin:\n",
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+ " steroid = filin.read()\n",
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+ " steroid = steroid.split(\"\\n\")\n",
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+ " steroid.pop()\n",
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+ "\n",
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+ " with open(\"heme.list\", \"r\") as filin:\n",
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+ " heme = filin.read()\n",
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+ " heme = heme.split(\"\\n\")\n",
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+ " heme.pop()\n",
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+ "\n",
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+ " with open(\"nucleotide.list\", \"r\") as filin:\n",
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+ " nucleotide = filin.read()\n",
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+ " nucleotide = nucleotide.split(\"\\n\")\n",
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+ " nucleotide.pop()\n",
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+ " \n",
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+ " lmin = len(heme)\n",
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+ " lmid = len(nucleotide)\n",
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+ " lmax = len(control)\n",
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+ " tot_size = lmin + lmid + lmax\n",
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+ " data_onehot = np.ndarray(shape=(size, 14, 32, 32, 32)) # initializing empty array\n",
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+ "\n",
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+ " np.random.seed(9001)\n",
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+ " indices = np.random.permutation(tot_size)\n",
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+ " indices = indices[:size]\n",
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+ " output = np.ndarray(shape=(size, 3)) # softmax 3, {steroid=1, heme=1, nucleotide=1}\n",
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+ "\n",
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+ " n = -1\n",
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+ " for i in indices:\n",
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+ " n += 1\n",
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+ " if i < lmin:\n",
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+ " data_onehot[n,] = np.load(\"deepdrug3d_voxel_data/\"+heme[i]+\".npy\")\n",
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+ " output[n,] = [1,0,0]\n",
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+ " elif i > lmin and i < (lmin + lmid):\n",
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+ " data_onehot[n,] = np.load(\"deepdrug3d_voxel_data/\"+nucleotide[i - lmin]+\".npy\")\n",
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+ " output[n,] = [0,1,0]\n",
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+ " else:\n",
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+ " data_onehot[n,] = np.load(\"deepdrug3d_voxel_data/\"+control[i - (lmin+lmid) - 1]+\".npy\")\n",
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+ " output[n,] = [0,0,1]\n",
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+ " \n",
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+ " return (data_onehot, output)"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "### Defining different model to test and compare"
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]
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},
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"cell_type": "code",
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- "execution_count": 4,
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+ "execution_count": null,
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"metadata": {},
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"source": [
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- "n = -1\n",
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- "for i in indices:\n",
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- " n += 1\n",
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- " if i < lmin:\n",
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- " data_onehot[n,] = np.load(\"deepdrug3d_voxel_data/\"+steroid[i]+\".npy\")\n",
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- " output[n,] = [1,0,0]\n",
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- " elif i > lmin and i < (lmin + lmid):\n",
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- " data_onehot[n,] = np.load(\"deepdrug3d_voxel_data/\"+heme[i - lmin]+\".npy\")\n",
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- " output[n,] = [0,1,0]\n",
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- " else:\n",
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- " data_onehot[n,] = np.load(\"deepdrug3d_voxel_data/\"+nucleotide[i - (lmin+lmid) - 1]+\".npy\")\n",
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- " output[n,] = [0,0,1]"
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+ "def model_heavy(): # créer un objet modèle\n",
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+ " \"\"\"\n",
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+ " Return a simple sequentiel model\n",
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+ " \n",
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+ " Returns :\n",
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+ " - model : keras.Model\n",
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+ " \"\"\"\n",
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+ " inputs = Input(shape=(14,32,32,32))\n",
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+ " conv_1 = Conv3D(64, (28, 28, 28), padding=\"same\", activation=\"relu\", kernel_initializer=\"he_normal\")(inputs)\n",
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+ " conv_2 = Conv3D(64, (26, 26, 26), padding=\"same\", activation=\"relu\", kernel_initializer=\"he_normal\")(conv_1)\n",
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+ " drop_1 = Dropout(0.2)(conv_2)\n",
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+ " maxpool = MaxPooling3D()(drop_1)\n",
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+ " drop_2 = Dropout(0.4)(maxpool)\n",
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+ " dense = Dense(512)(drop_2)\n",
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+ " drop_3 = Dropout(0.4)(dense)\n",
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+ " flatters = Flatten()(drop_3)\n",
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+ " #output = TimeDistributed(Dense(3, activation='softmax'))(drop_3)\n",
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+ " output = Dense(3, activation='softmax')(flatters)\n",
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+ " model = Model(inputs=inputs, outputs=output)\n",
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+ " my_opt = optimizers.Adam(learning_rate=0.000001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
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+ " print(model.summary)\n",
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+ " model.compile(optimizer=my_opt, loss=\"categorical_crossentropy\",\n",
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+ " metrics=[\"accuracy\"])\n",
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+ " return model"
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+ "execution_count": 8,
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"source": [
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- "X_train = data_onehot[0:1664,]\n",
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- "Y_train = output[0:1664,]\n",
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- "X_test = data_onehot[1664:,]\n",
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- "Y_test = output[1664:,]"
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+ "def model_new(): # créer un objet modèle\n",
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+ " \"\"\"\n",
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+ " Return a simple sequentiel model\n",
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+ " \n",
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+ " Returns :\n",
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+ " - model : keras.Model\n",
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+ " \"\"\"\n",
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+ " inputs = Input(shape=(14,32,32,32))\n",
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+ " conv_1 = Convolution3D(filters=64, kernel_size=5, padding=\"valid\", data_format='channels_first')(inputs)\n",
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+ " activation_1 = LeakyReLU(alpha = 0.1)(conv_1)\n",
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+ " drop_1 = Dropout(0.2)(activation_1)\n",
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+ " conv_2 = Convolution3D(filters=64, kernel_size=3, padding=\"valid\", data_format='channels_first')(drop_1)\n",
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+ " activation_2 = LeakyReLU(alpha = 0.1)(conv_2)\n",
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+ " maxpool = MaxPooling3D(pool_size=(2,2,2),\n",
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+ " strides=None,\n",
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+ " padding='valid',\n",
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+ " data_format='channels_first')(activation_2)\n",
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+ " drop_2 = Dropout(0.4)(maxpool)\n",
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+ " flatters = Flatten()(drop_2)\n",
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+ " dense = Dense(128)(flatters)\n",
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+ " activation_3 = LeakyReLU(alpha = 0.1)(dense)\n",
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+ " drop_3 = Dropout(0.4)(activation_3)\n",
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+ " output = Dense(3, activation='softmax')(drop_3)\n",
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+ " model = Model(inputs=inputs, outputs=output)\n",
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+ " my_opt = optimizers.Adam(learning_rate=0.000001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
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+ " print(model.summary)\n",
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+ " model.compile(optimizer=my_opt, loss=\"categorical_crossentropy\",\n",
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+ " metrics=[\"accuracy\"])\n",
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+ " return model"
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+ "def model_light(): # créer un objet modèle\n",
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+ " conv_1 = Conv3D(32, (28, 28, 28), padding=\"same\", activation=\"relu\", kernel_initializer=\"he_normal\")(inputs)\n",
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+ " maxpool_2 = MaxPooling3D()(drop_2)\n",
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+ " drop_3 = Dropout(0.3)(maxpool_2)\n",
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+ " dense = Dense(256)(drop_3)\n",
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+ " flatters = Flatten()(drop_4)\n",
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+ "## Create pocket lists\n",
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+ "4 lists are created :\n",
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+ " + control\n",
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+ " + steroid\n",
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+ " + heme\n",
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+ " + nucleotide"
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+ ]
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+ "outputs": [],
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+ "source": [
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+ "data = in_out_lists(1400)\n",
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+ "pockets = np.cumsum(data[1], axis=0)[-1]"
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+ ]
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+ "print(\"with random seed=9001 and a 1400 pockets dataset the rates are:\\n\\\n",
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+ " {} heme, {} nucleotide, {} control\\n\\\n",
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+ " Total avaible dataset are composed of the following proportions:\\n\\\n",
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+ " {} heme, {} nucleotide, {} control\".format(pockets[0]/1400, pockets[1]/1400,pockets[2]/1400,\n",
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+ " 0.145, 0.380, 0.475))"
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+ ]
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+ },
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+ "source": [
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+ "data_onehot = data[0]\n",
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+ "output = data[1]\n",
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+ "X_train = data_onehot[0:1000,]\n",
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+ "Y_train = output[0:1000,]\n",
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+ "X_test = data_onehot[1000:,]\n",
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+ "Y_test = output[1000:,]"
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+ ]
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+ },
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+ {
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+ "outputs": [],
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+ "source": [
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+ "my_model = model_new()"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "tf.test.is_gpu_available()\n",
|
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+ "#my_model.fit(X_train, Y_train, epochs=50, batch_size=30)"
|
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+ ]
|
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
|
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+ "history_mild_2mp = mild_model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=30, batch_size=32)\n",
|
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|
+ "my_model.save('new_model_e30_b32_t1000.h5')"
|
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+ ]
|
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
|
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+ "#predictions=prediction_history()"
|
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|
+ ]
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}
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|
],
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|
"metadata": {
|
Nicolasticot