initial commit

.ipynb_checkpoints/DeepDrug-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DeepDrug3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create pocket lists\n",
+    "4 pockets are created :\n",
+    "  + control\n",
+    "  + steroid\n",
+    "  + heme\n",
+    "  + nucleotide"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"control.list\", \"r\") as filin:\n",
+    "    control = filin.read()\n",
+    "control = control.split(\"\\n\")\n",
+    "control.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"steroid.list\", \"r\") as filin:\n",
+    "    steroid = filin.read()\n",
+    "steroid = steroid.split(\"\\n\")\n",
+    "steroid.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"heme.list\", \"r\") as filin:\n",
+    "    heme = filin.read()\n",
+    "heme = heme.split(\"\\n\")\n",
+    "heme.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"nucleotide.list\", \"r\") as filin:\n",
+    "    nucleotide = filin.read()\n",
+    "nucleotide = nucleotide.split(\"\\n\")\n",
+    "nucleotide.pop()"
+   ]
+  }
+ ],
+ "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

.ipynb_checkpoints/secondary_struct-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keras\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Flatten, TimeDistributed\n",
+    "from keras import Input, Model\n",
+    "from keras.layers import add, Activation\n",
+    "#from keras.utils import plot_model  # Needs pydot.\n",
+    "from keras.layers import Conv1D, AveragePooling1D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def file_to_dataframe(filename):\n",
+    "    \"\"\"\n",
+    "    Returns a pandas dataframe ncol = len(longest_sequence),\n",
+    "                               nrow = number of sequences\n",
+    "                               \n",
+    "    Arguments :\n",
+    "        - filename : str\n",
+    "            path to file\n",
+    "    Takes a path to a files containing any sequences, must be 1 sequence\n",
+    "    per line.\n",
+    "    \n",
+    "    Sequences shorter than longest_sequence are completed with '0' char.\n",
+    "    \"\"\"\n",
+    "    filin = open(filename)\n",
+    "    fastas0 = filin.read()\n",
+    "    fastas0 = fastas0.split('\\n')\n",
+    "    nmax = len(max(fastas0, key=len))\n",
+    "    fastas = []\n",
+    "    for fasta in fastas0:\n",
+    "        fastas.append(fasta + (nmax - len(fasta)) * '0')\n",
+    "    \n",
+    "    seqs = pd.DataFrame(index=range(len(fastas)), columns=range(nmax))\n",
+    "    \n",
+    "    for i, fasta in enumerate(fastas):\n",
+    "        seqs.loc[i] = pd.Series(list(fasta))\n",
+    "    \n",
+    "    return seqs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_sequential(): # créer un objet modèle\n",
+    "    \"\"\"\n",
+    "    Return a simple sequentiel model\n",
+    "    \n",
+    "    Returns :\n",
+    "        - model : keras.Model\n",
+    "    \"\"\"\n",
+    "    inputs = Input(shape=(759,21)) # 759 aa, 21 car onehot\n",
+    "    conv_1 = Conv1D(25, (5), padding=\"same\", activation=\"relu\",\n",
+    "                        kernel_initializer=\"he_normal\")(inputs)\n",
+    "    conv_2 = Conv1D(35, (5), padding=\"same\", activation=\"relu\",\n",
+    "                        kernel_initializer=\"he_normal\")(conv_1)\n",
+    "    output = TimeDistributed(Dense(4, activation='softmax'))(conv_2)\n",
+    "    model = Model(inputs=inputs, outputs=output)\n",
+    "    print(model.summary)\n",
+    "    model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\",\n",
+    "                  metrics=[\"accuracy\"])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def char_to_onehot(df, ncol=0):\n",
+    "    \"\"\"\n",
+    "    Returns the given str-encoded dataframe into a onehot encoded dataframe as an array object\n",
+    "    \n",
+    "    * Arguments:\n",
+    "        - df : pandas.DataFrame, the dataframe containing the sequences, cell \n",
+    "                               containing strings.\n",
+    "        - ncol : int, Optionnal. the number of col used to build the LabelEncoder\n",
+    "                    which will transform the df cells into int(categorical).\n",
+    "    \"\"\"\n",
+    "    classes = LabelEncoder()\n",
+    "    classes.fit(df[ncol])\n",
+    "    df_categorical = df.apply(func=classes.transform, axis=0)\n",
+    "    df_onehot = keras.utils.to_categorical(df_categorical)\n",
+    "    return df_onehot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def true_accuracy(predictions, onehot_Y_test):\n",
+    "    \"\"\"\n",
+    "    Computes the accuracy ignoring the \"0\" of the DataFrame.\n",
+    "    \n",
+    "    * Arguments :\n",
+    "        - predictions : numpy.array, output of the model, onehot encoded.\n",
+    "        - onehot_Y_test : numpy.array, the true values, onehot encoded.\n",
+    "        \n",
+    "    * Returns :\n",
+    "        - acc_coor : int, accuracy corrected, not considering '0' predictions\n",
+    "    \"\"\"\n",
+    "    tp = 0\n",
+    "    tn = 0\n",
+    "    fn = 0\n",
+    "    fp = 0\n",
+    "    tot = 0\n",
+    "\n",
+    "    for i in range(len(predictions)):\n",
+    "        for j in range(len(predictions[i])):\n",
+    "            if onehot_Y_test[i, j, 3] != 0.:\n",
+    "                predmax = -1\n",
+    "                predict_class = -1\n",
+    "                true_class = -1\n",
+    "                for k in range(len(predictions[i, j])):\n",
+    "                    if predmax < predictions[i, j, k]:\n",
+    "                        predmax = predictions[i, j, k]\n",
+    "                        predict_class = k\n",
+    "                    if onehot_Y_test[i, j, k] == 1.:\n",
+    "                        true_class = k\n",
+    "                if predict_class == true_class:\n",
+    "                    tp = tp+1\n",
+    "                tot = tot + 1\n",
+    "    acc_corr = tp/tot*100\n",
+    "    return acc_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dataframe from files\n",
+    "fasta = file_to_dataframe(\"data/train.fasta\")\n",
+    "fasta = fasta.replace(\"X\", \"A\") # 'X' in fasta sequences are replace with 'A'\n",
+    "dssp = file_to_dataframe(\"data/train.dssp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Writes Dataframes to csv files\n",
+    "fasta.to_csv(path_or_buf=\"data/train_fasta.csv\")\n",
+    "dssp.to_csv(path_or_buf=\"data/train_dssp.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To onehot\n",
+    "fasta_onehot = char_to_onehot(fasta, 0)\n",
+    "dssp_onehot = char_to_onehot(dssp, 2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Divides the dataset in train and test subsets\n",
+    "fasta_train = fasta_onehot[0:1000,]\n",
+    "dssp_train = dssp_onehot[0:1000,]\n",
+    "fasta_test = fasta_onehot[1000:,]\n",
+    "dssp_test = dssp_onehot[1000:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<bound method Network.summary of <keras.engine.training.Model object at 0x7f68cfb7b0d0>>\n",
+      "Epoch 1/30\n",
+      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.7467 - acc: 0.7447\n",
+      "Epoch 2/30\n",
+      "1000/1000 [==============================] - 1s 974us/step - loss: 0.2610 - acc: 0.8944\n",
+      "Epoch 3/30\n",
+      "1000/1000 [==============================] - 1s 964us/step - loss: 0.2053 - acc: 0.9086\n",
+      "Epoch 4/30\n",
+      "1000/1000 [==============================] - 1s 994us/step - loss: 0.1899 - acc: 0.9154\n",
+      "Epoch 5/30\n",
+      "1000/1000 [==============================] - 1s 986us/step - loss: 0.1817 - acc: 0.9193\n",
+      "Epoch 6/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1790 - acc: 0.9201\n",
+      "Epoch 7/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1759 - acc: 0.9217\n",
+      "Epoch 8/30\n",
+      "1000/1000 [==============================] - 1s 960us/step - loss: 0.1743 - acc: 0.9222\n",
+      "Epoch 9/30\n",
+      "1000/1000 [==============================] - 1s 977us/step - loss: 0.1735 - acc: 0.9226\n",
+      "Epoch 10/30\n",
+      "1000/1000 [==============================] - 1s 963us/step - loss: 0.1727 - acc: 0.9228\n",
+      "Epoch 11/30\n",
+      "1000/1000 [==============================] - 1s 999us/step - loss: 0.1712 - acc: 0.9239\n",
+      "Epoch 12/30\n",
+      "1000/1000 [==============================] - 1s 963us/step - loss: 0.1702 - acc: 0.9243\n",
+      "Epoch 13/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1695 - acc: 0.9249\n",
+      "Epoch 14/30\n",
+      "1000/1000 [==============================] - 1s 978us/step - loss: 0.1687 - acc: 0.9252\n",
+      "Epoch 15/30\n",
+      "1000/1000 [==============================] - 1s 981us/step - loss: 0.1678 - acc: 0.9257\n",
+      "Epoch 16/30\n",
+      "1000/1000 [==============================] - 1s 985us/step - loss: 0.1669 - acc: 0.9260\n",
+      "Epoch 17/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1661 - acc: 0.9267\n",
+      "Epoch 18/30\n",
+      "1000/1000 [==============================] - 1s 1000us/step - loss: 0.1656 - acc: 0.9269\n",
+      "Epoch 19/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1650 - acc: 0.9273\n",
+      "Epoch 20/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1640 - acc: 0.9277\n",
+      "Epoch 21/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1637 - acc: 0.9281\n",
+      "Epoch 22/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1630 - acc: 0.9285\n",
+      "Epoch 23/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1621 - acc: 0.9289\n",
+      "Epoch 24/30\n",
+      "1000/1000 [==============================] - 1s 992us/step - loss: 0.1619 - acc: 0.9289\n",
+      "Epoch 25/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1616 - acc: 0.9291\n",
+      "Epoch 26/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1616 - acc: 0.9291\n",
+      "Epoch 27/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1608 - acc: 0.9296\n",
+      "Epoch 28/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1604 - acc: 0.9297\n",
+      "Epoch 29/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1601 - acc: 0.9298\n",
+      "Epoch 30/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1597 - acc: 0.9300\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7f68cfcf6050>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = model_sequential()\n",
+    "model.fit(fasta_train, dssp_train, epochs=30, batch_size=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(fasta_test, batch_size=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy : 68.06409539780138\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"accuracy : {}\".format(true_accuracy(predictions, dssp_test)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

DeepDrug.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DeepDrug3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create pocket lists\n",
+    "4 pockets are created :\n",
+    "  + control\n",
+    "  + steroid\n",
+    "  + heme\n",
+    "  + nucleotide"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "''"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open(\"control.list\", \"r\") as filin:\n",
+    "    control = filin.read()\n",
+    "control = control.split(\"\\n\")\n",
+    "control.pop()\n",
+    "\n",
+    "with open(\"steroid.list\", \"r\") as filin:\n",
+    "    steroid = filin.read()\n",
+    "steroid = steroid.split(\"\\n\")\n",
+    "steroid.pop()\n",
+    "\n",
+    "with open(\"heme.list\", \"r\") as filin:\n",
+    "    heme = filin.read()\n",
+    "heme = heme.split(\"\\n\")\n",
+    "heme.pop()\n",
+    "\n",
+    "with open(\"nucleotide.list\", \"r\") as filin:\n",
+    "    nucleotide = filin.read()\n",
+    "nucleotide = nucleotide.split(\"\\n\")\n",
+    "nucleotide.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

config.yml

+name: projet-dl
+channels:
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - attrs=19.1.0=py37_1
+  - backcall=0.1.0=py37_0
+  - blas=1.0=mkl
+  - bleach=3.1.0=py37_0
+  - ca-certificates=2019.5.15=1
+  - certifi=2019.6.16=py37_1
+  - dbus=1.13.6=h746ee38_0
+  - decorator=4.4.0=py37_1
+  - defusedxml=0.6.0=py_0
+  - entrypoints=0.3=py37_0
+  - expat=2.2.6=he6710b0_0
+  - fontconfig=2.13.0=h9420a91_0
+  - freetype=2.9.1=h8a8886c_1
+  - glib=2.56.2=hd408876_0
+  - gmp=6.1.2=h6c8ec71_1
+  - gst-plugins-base=1.14.0=hbbd80ab_1
+  - gstreamer=1.14.0=hb453b48_1
+  - icu=58.2=h9c2bf20_1
+  - intel-openmp=2019.4=243
+  - ipykernel=5.1.2=py37h39e3cac_0
+  - ipython=7.8.0=py37h39e3cac_0
+  - ipython_genutils=0.2.0=py37_0
+  - ipywidgets=7.5.1=py_0
+  - jedi=0.15.1=py37_0
+  - jinja2=2.10.1=py37_0
+  - jpeg=9b=h024ee3a_2
+  - json5=0.8.5=py_0
+  - jsonschema=3.0.2=py37_0
+  - jupyter=1.0.0=py37_7
+  - jupyter_client=5.3.1=py_0
+  - jupyter_console=6.0.0=py37_0
+  - jupyter_core=4.5.0=py_0
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+  - libuuid=1.0.3=h1bed415_2
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+  - libxml2=2.9.9=hea5a465_1
+  - markupsafe=1.1.1=py37h7b6447c_0
+  - mistune=0.8.4=py37h7b6447c_0
+  - mkl=2019.4=243
+  - mkl-service=2.3.0=py37he904b0f_0
+  - mkl_fft=1.0.14=py37ha843d7b_0
+  - mkl_random=1.0.2=py37hd81dba3_0
+  - nbconvert=5.5.0=py_0
+  - nbformat=4.4.0=py37_0
+  - ncurses=6.1=he6710b0_1
+  - notebook=6.0.1=py37_0
+  - numpy=1.16.5=py37h7e9f1db_0
+  - numpy-base=1.16.5=py37hde5b4d6_0
+  - openssl=1.1.1d=h7b6447c_1
+  - pandas=0.25.1=py37he6710b0_0
+  - pandoc=2.2.3.2=0
+  - pandocfilters=1.4.2=py37_1
+  - parso=0.5.1=py_0
+  - pcre=8.43=he6710b0_0
+  - pexpect=4.7.0=py37_0
+  - pickleshare=0.7.5=py37_0
+  - pip=19.2.2=py37_0
+  - prometheus_client=0.7.1=py_0
+  - prompt_toolkit=2.0.9=py37_0
+  - ptyprocess=0.6.0=py37_0
+  - pygments=2.4.2=py_0
+  - pyqt=5.9.2=py37h05f1152_2
+  - pyrsistent=0.14.11=py37h7b6447c_0
+  - python=3.7.4=h265db76_1
+  - python-dateutil=2.8.0=py37_0
+  - pytz=2019.2=py_0
+  - pyzmq=18.1.0=py37he6710b0_0
+  - qt=5.9.7=h5867ecd_1
+  - qtconsole=4.5.5=py_0
+  - readline=7.0=h7b6447c_5
+  - scipy=1.3.1=py37h7c811a0_0
+  - send2trash=1.5.0=py37_0
+  - setuptools=41.0.1=py37_0
+  - sip=4.19.8=py37hf484d3e_0
+  - six=1.12.0=py37_0
+  - sqlite=3.29.0=h7b6447c_0
+  - terminado=0.8.2=py37_0
+  - testpath=0.4.2=py37_0
+  - tk=8.6.8=hbc83047_0
+  - tornado=6.0.3=py37h7b6447c_0
+  - traitlets=4.3.2=py37_0
+  - wcwidth=0.1.7=py37_0
+  - webencodings=0.5.1=py37_1
+  - wheel=0.33.4=py37_0
+  - widgetsnbextension=3.5.1=py37_0
+  - xz=5.2.4=h14c3975_4
+  - zeromq=4.3.1=he6710b0_3
+  - zlib=1.2.11=h7b6447c_3
+  - pip:
+    - biopython==1.74
+    - cycler==0.10.0
+    - griddataformats==0.5.0
+    - gsd==1.8.1
+    - joblib==0.13.2
+    - kiwisolver==1.1.0
+    - matplotlib==3.1.1
+    - mdanalysis==0.20.1
+    - mmtf-python==1.1.2
+    - mock==3.0.5
+    - msgpack==0.6.1
+    - networkx==2.3
+    - pbxplore==1.3.8
+    - pillow==6.1.0
+    - pyclustering==0.9.1
+    - pyparsing==2.4.2
+    - keras
+    - sklearn

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secondary_struct.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keras\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Flatten, TimeDistributed\n",
+    "from keras import Input, Model\n",
+    "from keras.layers import add, Activation\n",
+    "#from keras.utils import plot_model  # Needs pydot.\n",
+    "from keras.layers import Conv1D, AveragePooling1D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def file_to_dataframe(filename):\n",
+    "    \"\"\"\n",
+    "    Returns a pandas dataframe ncol = len(longest_sequence),\n",
+    "                               nrow = number of sequences\n",
+    "                               \n",
+    "    Arguments :\n",
+    "        - filename : str\n",
+    "            path to file\n",
+    "    Takes a path to a files containing any sequences, must be 1 sequence\n",
+    "    per line.\n",
+    "    \n",
+    "    Sequences shorter than longest_sequence are completed with '0' char.\n",
+    "    \"\"\"\n",
+    "    filin = open(filename)\n",
+    "    fastas0 = filin.read()\n",
+    "    fastas0 = fastas0.split('\\n')\n",
+    "    nmax = len(max(fastas0, key=len))\n",
+    "    fastas = []\n",
+    "    for fasta in fastas0:\n",
+    "        fastas.append(fasta + (nmax - len(fasta)) * '0')\n",
+    "    \n",
+    "    seqs = pd.DataFrame(index=range(len(fastas)), columns=range(nmax))\n",
+    "    \n",
+    "    for i, fasta in enumerate(fastas):\n",
+    "        seqs.loc[i] = pd.Series(list(fasta))\n",
+    "    \n",
+    "    return seqs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_sequential(): # créer un objet modèle\n",
+    "    \"\"\"\n",
+    "    Return a simple sequentiel model\n",
+    "    \n",
+    "    Returns :\n",
+    "        - model : keras.Model\n",
+    "    \"\"\"\n",
+    "    inputs = Input(shape=(759,21)) # 759 aa, 21 car onehot\n",
+    "    conv_1 = Conv1D(25, (5), padding=\"same\", activation=\"relu\",\n",
+    "                        kernel_initializer=\"he_normal\")(inputs)\n",
+    "    conv_2 = Conv1D(35, (5), padding=\"same\", activation=\"relu\",\n",
+    "                        kernel_initializer=\"he_normal\")(conv_1)\n",
+    "    output = TimeDistributed(Dense(4, activation='softmax'))(conv_2)\n",
+    "    model = Model(inputs=inputs, outputs=output)\n",
+    "    print(model.summary)\n",
+    "    model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\",\n",
+    "                  metrics=[\"accuracy\"])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def char_to_onehot(df, ncol=0):\n",
+    "    \"\"\"\n",
+    "    Returns the given str-encoded dataframe into a onehot encoded dataframe as an array object\n",
+    "    \n",
+    "    * Arguments:\n",
+    "        - df : pandas.DataFrame, the dataframe containing the sequences, cell \n",
+    "                               containing strings.\n",
+    "        - ncol : int, Optionnal. the number of col used to build the LabelEncoder\n",
+    "                    which will transform the df cells into int(categorical).\n",
+    "    \"\"\"\n",
+    "    classes = LabelEncoder()\n",
+    "    classes.fit(df[ncol])\n",
+    "    df_categorical = df.apply(func=classes.transform, axis=0)\n",
+    "    df_onehot = keras.utils.to_categorical(df_categorical)\n",
+    "    return df_onehot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def true_accuracy(predictions, onehot_Y_test):\n",
+    "    \"\"\"\n",
+    "    Computes the accuracy ignoring the \"0\" of the DataFrame.\n",
+    "    \n",
+    "    * Arguments :\n",
+    "        - predictions : numpy.array, output of the model, onehot encoded.\n",
+    "        - onehot_Y_test : numpy.array, the true values, onehot encoded.\n",
+    "        \n",
+    "    * Returns :\n",
+    "        - acc_coor : int, accuracy corrected, not considering '0' predictions\n",
+    "    \"\"\"\n",
+    "    tp = 0\n",
+    "    tn = 0\n",
+    "    fn = 0\n",
+    "    fp = 0\n",
+    "    tot = 0\n",
+    "\n",
+    "    for i in range(len(predictions)):\n",
+    "        for j in range(len(predictions[i])):\n",
+    "            if onehot_Y_test[i, j, 3] != 0.:\n",
+    "                predmax = -1\n",
+    "                predict_class = -1\n",
+    "                true_class = -1\n",
+    "                for k in range(len(predictions[i, j])):\n",
+    "                    if predmax < predictions[i, j, k]:\n",
+    "                        predmax = predictions[i, j, k]\n",
+    "                        predict_class = k\n",
+    "                    if onehot_Y_test[i, j, k] == 1.:\n",
+    "                        true_class = k\n",
+    "                if predict_class == true_class:\n",
+    "                    tp = tp+1\n",
+    "                tot = tot + 1\n",
+    "    acc_corr = tp/tot*100\n",
+    "    return acc_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dataframe from files\n",
+    "fasta = file_to_dataframe(\"data/train.fasta\")\n",
+    "fasta = fasta.replace(\"X\", \"A\") # 'X' in fasta sequences are replace with 'A'\n",
+    "dssp = file_to_dataframe(\"data/train.dssp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Writes Dataframes to csv files\n",
+    "fasta.to_csv(path_or_buf=\"data/train_fasta.csv\")\n",
+    "dssp.to_csv(path_or_buf=\"data/train_dssp.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To onehot\n",
+    "fasta_onehot = char_to_onehot(fasta, 0)\n",
+    "dssp_onehot = char_to_onehot(dssp, 2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Divides the dataset in train and test subsets\n",
+    "fasta_train = fasta_onehot[0:1000,]\n",
+    "dssp_train = dssp_onehot[0:1000,]\n",
+    "fasta_test = fasta_onehot[1000:,]\n",
+    "dssp_test = dssp_onehot[1000:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<bound method Network.summary of <keras.engine.training.Model object at 0x7f68cfb7b0d0>>\n",
+      "Epoch 1/30\n",
+      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.7467 - acc: 0.7447\n",
+      "Epoch 2/30\n",
+      "1000/1000 [==============================] - 1s 974us/step - loss: 0.2610 - acc: 0.8944\n",
+      "Epoch 3/30\n",
+      "1000/1000 [==============================] - 1s 964us/step - loss: 0.2053 - acc: 0.9086\n",
+      "Epoch 4/30\n",
+      "1000/1000 [==============================] - 1s 994us/step - loss: 0.1899 - acc: 0.9154\n",
+      "Epoch 5/30\n",
+      "1000/1000 [==============================] - 1s 986us/step - loss: 0.1817 - acc: 0.9193\n",
+      "Epoch 6/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1790 - acc: 0.9201\n",
+      "Epoch 7/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1759 - acc: 0.9217\n",
+      "Epoch 8/30\n",
+      "1000/1000 [==============================] - 1s 960us/step - loss: 0.1743 - acc: 0.9222\n",
+      "Epoch 9/30\n",
+      "1000/1000 [==============================] - 1s 977us/step - loss: 0.1735 - acc: 0.9226\n",
+      "Epoch 10/30\n",
+      "1000/1000 [==============================] - 1s 963us/step - loss: 0.1727 - acc: 0.9228\n",
+      "Epoch 11/30\n",
+      "1000/1000 [==============================] - 1s 999us/step - loss: 0.1712 - acc: 0.9239\n",
+      "Epoch 12/30\n",
+      "1000/1000 [==============================] - 1s 963us/step - loss: 0.1702 - acc: 0.9243\n",
+      "Epoch 13/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1695 - acc: 0.9249\n",
+      "Epoch 14/30\n",
+      "1000/1000 [==============================] - 1s 978us/step - loss: 0.1687 - acc: 0.9252\n",
+      "Epoch 15/30\n",
+      "1000/1000 [==============================] - 1s 981us/step - loss: 0.1678 - acc: 0.9257\n",
+      "Epoch 16/30\n",
+      "1000/1000 [==============================] - 1s 985us/step - loss: 0.1669 - acc: 0.9260\n",
+      "Epoch 17/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1661 - acc: 0.9267\n",
+      "Epoch 18/30\n",
+      "1000/1000 [==============================] - 1s 1000us/step - loss: 0.1656 - acc: 0.9269\n",
+      "Epoch 19/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1650 - acc: 0.9273\n",
+      "Epoch 20/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1640 - acc: 0.9277\n",
+      "Epoch 21/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1637 - acc: 0.9281\n",
+      "Epoch 22/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1630 - acc: 0.9285\n",
+      "Epoch 23/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1621 - acc: 0.9289\n",
+      "Epoch 24/30\n",
+      "1000/1000 [==============================] - 1s 992us/step - loss: 0.1619 - acc: 0.9289\n",
+      "Epoch 25/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1616 - acc: 0.9291\n",
+      "Epoch 26/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1616 - acc: 0.9291\n",
+      "Epoch 27/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1608 - acc: 0.9296\n",
+      "Epoch 28/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1604 - acc: 0.9297\n",
+      "Epoch 29/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1601 - acc: 0.9298\n",
+      "Epoch 30/30\n",
+      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.1597 - acc: 0.9300\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7f68cfcf6050>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = model_sequential()\n",
+    "model.fit(fasta_train, dssp_train, epochs=30, batch_size=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(fasta_test, batch_size=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy : 68.06409539780138\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"accuracy : {}\".format(true_accuracy(predictions, dssp_test)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

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