{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict regulatory regions from the DNA sequence using HDF5 datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we illustrate several variants how to predict regulatory regions (of a toy example) from the DNA sequence.\n",
    "The reference genome is made up of a concatenation of Oct4 and Mafk binding sites and we shall use all regions on chromosome 'pseudo1' as training\n",
    "and 'pseudo2' as test chromosomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wkopp/anaconda3/envs/jdev/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "from keras import Model\n",
    "from keras import backend as K\n",
    "from keras.layers import Conv2D\n",
    "\n",
    "from keras.layers import Dense\n",
    "from keras.layers import GlobalAveragePooling2D\n",
    "from keras.layers import Input\n",
    "from keras.layers import Reshape\n",
    "\n",
    "from pkg_resources import resource_filename\n",
    "\n",
    "from janggu import Janggu\n",
    "from janggu import Scorer\n",
    "from janggu import inputlayer\n",
    "from janggu import outputdense\n",
    "from janggu.data import Bioseq\n",
    "from janggu.data import Cover\n",
    "from janggu.data import ReduceDim\n",
    "from janggu.layers import DnaConv2D\n",
    "from janggu.layers import LocalAveragePooling2D\n",
    "from janggu.utils import ExportClustermap\n",
    "from janggu.utils import ExportTsne\n",
    "from janggu.utils import ExportTsv\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "np.random.seed(1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to specify the output directory in which the results are stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['JANGGU_OUTPUT'] = '/home/wkopp/janggu_examples'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the DNA sequence feature order. Order 1, 2 and 3 correspond to mono-, di- and tri-nucleotide based features (see Tutorial)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the dataset\n",
    "# The pseudo genome represents just a concatenation of all sequences\n",
    "# in sample.fa and sample2.fa. Therefore, the results should be almost\n",
    "# identically to the models obtained from classify_fasta.py.\n",
    "REFGENOME = resource_filename('janggu', 'resources/pseudo_genome.fa')\n",
    "# ROI contains regions spanning positive and negative examples\n",
    "ROI_TRAIN_FILE = resource_filename('janggu', 'resources/roi_train.bed')\n",
    "ROI_TEST_FILE = resource_filename('janggu', 'resources/roi_test.bed')\n",
    "# PEAK_FILE only contains positive examples\n",
    "PEAK_FILE = resource_filename('janggu', 'resources/scores.bed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using data directly from HDF5 can be tricky.\n",
    "For example, it might be prohibitively slow to use data \n",
    "from HDF5 with in conjunction with randomization option in the model.fit method, because of limitations of the HDF5 file format.\n",
    "HDF5 data may be read more efficiently in coherent blocks.\n",
    "\n",
    "To overcome this limitation, it is possible to randomize the dataset already at loading time.\n",
    "That way randomization may be avoided in the model.fit method such that coherent yet randomized batches are consumed\n",
    "during training.\n",
    "\n",
    "For the randomization to be effective, use store_whole_genome=False and use the same random_state for all training dataset objects to make sure that the intervals are aligned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading from lazy loader\n",
      "reload /home/wkopp/janggu_examples/datasets/dna/347df68279d48736dbac1cb3475dbdec75833b9c6b098a1c33e9a6bd757f5cbe.h5\n",
      "reload /home/wkopp/janggu_examples/datasets/peaks/15ff635afa19facb6b20276568b4447ceb85e0205ac7a6661f710141d7aa1d7f.npz\n"
     ]
    }
   ],
   "source": [
    "# Training input and labels are purely defined genomic coordinates\n",
    "# applying a random state internally randomizes the dataset\n",
    "# in this case, shuffling of the mini-batches during training\n",
    "# becomes obsolete.\n",
    "# Importantly, the same state needs to be used for all datasets\n",
    "# to enforce synchronized datasets.\n",
    "DNA = Bioseq.create_from_refgenome('dna', refgenome=REFGENOME,\n",
    "                                   roi=ROI_TRAIN_FILE,\n",
    "                                   binsize=200,\n",
    "                                   order=order,\n",
    "                                   storage='hdf5',\n",
    "                                   cache=True,\n",
    "                                   store_whole_genome=False,\n",
    "                                   random_state=43)\n",
    "\n",
    "LABELS = ReduceDim(Cover.create_from_bed('peaks', roi=ROI_TRAIN_FILE,\n",
    "                               bedfiles=PEAK_FILE,\n",
    "                               binsize=200,\n",
    "                               resolution=200,\n",
    "                               storage='sparse',\n",
    "                               cache=True,\n",
    "                               store_whole_genome=True,\n",
    "                               random_state=43))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the test sets, randomization is not required, so we just don't specify the random_state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading from lazy loader\n",
      "reload /home/wkopp/janggu_examples/datasets/dna/39fa662199640ed381bf7c4a2730b96aae0722c78836a790eaea231d88fed8c0.h5\n",
      "loading from bed lazy loader\n",
      "reload /home/wkopp/janggu_examples/datasets/peaks/a29de032bb3a21ad582a3aaa865f3ce1f6d58ebfcee455351ed4ea422e5166ea.h5\n"
     ]
    }
   ],
   "source": [
    "DNA_TEST = Bioseq.create_from_refgenome('dna', refgenome=REFGENOME,\n",
    "                                        roi=ROI_TEST_FILE,\n",
    "                                        binsize=200,\n",
    "                                        storage='hdf5',\n",
    "                                        cache=True,\n",
    "                                        order=order)\n",
    "\n",
    "LABELS_TEST = ReduceDim(Cover.create_from_bed('peaks',\n",
    "                                    bedfiles=PEAK_FILE,\n",
    "                                    roi=ROI_TEST_FILE,\n",
    "                                    binsize=200,\n",
    "                                    storage='hdf5',\n",
    "                                    cache=True,\n",
    "                                    resolution=200))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define and fit a conv net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in the other examples,\n",
    "we define the neural network using the Janggu model wrapper functionality.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "@inputlayer\n",
    "@outputdense('sigmoid')\n",
    "def double_stranded_model_dnaconv(inputs, inp, oup, params):\n",
    "    \"\"\" keras model for scanning both DNA strands.\n",
    "\n",
    "    A more elegant way of scanning both strands for motif occurrences\n",
    "    is achieved by the DnaConv2D layer wrapper, which internally\n",
    "    performs the convolution operation with the normal kernel weights\n",
    "    and the reverse complemented weights.\n",
    "    \"\"\"\n",
    "    with inputs.use('dna') as layer:\n",
    "        # the name in inputs.use() should be the same as the dataset name.\n",
    "        layer = DnaConv2D(Conv2D(params[0], (params[1], 1),\n",
    "                                 activation=params[2]))(layer)\n",
    "    output = GlobalAveragePooling2D(name='motif')(layer)\n",
    "    return inputs, output\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the model can be instantiated accordingly. We will also use a specific model name (which is optional)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelname = 'dna2peak_ex3_order{}'.format(order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dna2peak_ex3_order3'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelname"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Important: Now you use shuffle=False for the model fit method, since the dataset objects have already been randomized. This way, mini-batches can be read from the HDF5 file in coherent blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
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      "Epoch 2/100\n",
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      "Epoch 3/100\n",
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      "Epoch 6/100\n",
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      "Epoch 7/100\n",
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      "Epoch 8/100\n",
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      "Epoch 10/100\n",
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      "Epoch 12/100\n",
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      "Epoch 13/100\n",
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      "Epoch 14/100\n",
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      "Epoch 68/100\n",
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      "Epoch 70/100\n",
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      "Epoch 71/100\n",
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      "Epoch 72/100\n",
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      "Epoch 75/100\n",
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      "Epoch 76/100\n",
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      "Epoch 77/100\n",
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      "Epoch 78/100\n",
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      "Epoch 79/100\n",
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      "Epoch 81/100\n",
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      "Epoch 82/100\n",
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      "Epoch 83/100\n",
      "244/244 [==============================] - 4s 16ms/step - loss: 0.0193 - acc: 0.9983\n",
      "Epoch 84/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "244/244 [==============================] - 4s 16ms/step - loss: 0.0188 - acc: 0.9986\n",
      "Epoch 85/100\n",
      "244/244 [==============================] - 3s 14ms/step - loss: 0.0183 - acc: 0.9987\n",
      "Epoch 86/100\n",
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      "Epoch 87/100\n",
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      "Epoch 88/100\n",
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      "Epoch 89/100\n",
      "244/244 [==============================] - 3s 14ms/step - loss: 0.0165 - acc: 0.9991\n",
      "Epoch 90/100\n",
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      "Epoch 91/100\n",
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      "Epoch 92/100\n",
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      "Epoch 93/100\n",
      "244/244 [==============================] - 4s 15ms/step - loss: 0.0149 - acc: 0.9992\n",
      "Epoch 94/100\n",
      "244/244 [==============================] - 4s 16ms/step - loss: 0.0145 - acc: 0.9992\n",
      "Epoch 95/100\n",
      "244/244 [==============================] - 4s 15ms/step - loss: 0.0142 - acc: 0.9994\n",
      "Epoch 96/100\n",
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      "Epoch 97/100\n",
      "244/244 [==============================] - 4s 15ms/step - loss: 0.0135 - acc: 0.9994\n",
      "Epoch 98/100\n",
      "244/244 [==============================] - 3s 14ms/step - loss: 0.0132 - acc: 0.9995\n",
      "Epoch 99/100\n",
      "244/244 [==============================] - 4s 15ms/step - loss: 0.0128 - acc: 0.9995\n",
      "Epoch 100/100\n",
      "244/244 [==============================] - 3s 14ms/step - loss: 0.0125 - acc: 0.9995\n",
      "########################################\n",
      "loss: 0.01252954404943749, acc: 0.9994869821726305\n",
      "########################################\n"
     ]
    }
   ],
   "source": [
    "# create a new model object\n",
    "model = Janggu.create(template=double_stranded_model_dnaconv,\n",
    "                      modelparams=(30, 21, 'relu'),\n",
    "                      inputs=DNA,\n",
    "                      outputs=LABELS,\n",
    "                      name=modelname)\n",
    "\n",
    "model.compile(optimizer='adadelta', loss='binary_crossentropy',\n",
    "              metrics=['acc'])\n",
    "\n",
    "hist = model.fit(DNA, LABELS, epochs=100, shuffle=False)\n",
    "\n",
    "print('#' * 40)\n",
    "print('loss: {}, acc: {}'.format(hist.history['loss'][-1],\n",
    "                                 hist.history['acc'][-1]))\n",
    "print('#' * 40)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.10043038666248322, 0.955]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# do the evaluation on the independent test data\n",
    "model.evaluate(DNA_TEST, LABELS_TEST, datatags=['test'],\n",
    "               callbacks=['auc', 'auprc', 'roc', 'prc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_folder = os.path.join(os.environ['JANGGU_OUTPUT'], 'evaluation', modelname, 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/wkopp/janggu_examples/evaluation/dna2peak_ex3_order3/test\n"
     ]
    }
   ],
   "source": [
    "print(evaluation_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(os.path.join(evaluation_folder, 'prc.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(os.path.join(evaluation_folder, 'roc.png'))"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}