train-colab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 334,
     "status": "ok",
     "timestamp": 1637594810768,
     "user": {
      "displayName": "Jorge Eduardo Avila Cartes",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpY8_qNLAKfPXnIhNebtq2L-QUHe3nTa9iwBpC=s64",
      "userId": "08087796344987933176"
     },
     "user_tz": -60
    },
    "id": "cjoDgdATm_LY",
    "outputId": "60164859-4558-4624-b14e-41ef77354277"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "# mount google drive\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 256,
     "status": "ok",
     "timestamp": 1637574726233,
     "user": {
      "displayName": "Jorge Eduardo Avila Cartes",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpY8_qNLAKfPXnIhNebtq2L-QUHe3nTa9iwBpC=s64",
      "userId": "08087796344987933176"
     },
     "user_tz": -60
    },
    "id": "Lm1rC91knB7m",
    "outputId": "72adb9bd-67d5-49ab-868a-5945a22bb629"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 2] No such file or directory: 'drive/MyDrive/supervised-dna'\n",
      "/content/drive/MyDrive/supervised-dna\n"
     ]
    }
   ],
   "source": [
    "# change to the repository \n",
    "% cd drive/MyDrive/supervised-dna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TQvpYhpQoWJb"
   },
   "outputs": [],
   "source": [
    "!  git config --global user.email \"jorgeavilacartes@gmail.com\"\n",
    "!  git config --global user.name \"Jorge Avila\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3156,
     "status": "ok",
     "timestamp": 1637574732207,
     "user": {
      "displayName": "Jorge Eduardo Avila Cartes",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpY8_qNLAKfPXnIhNebtq2L-QUHe3nTa9iwBpC=s64",
      "userId": "08087796344987933176"
     },
     "user_tz": -60
    },
    "id": "cJF6rf-mmiSa",
    "outputId": "aa8e111c-6f36-4123-d5bb-3ab99623ad18"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: biopython in /usr/local/lib/python3.7/dist-packages (1.79)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from biopython) (1.19.5)\n"
     ]
    }
   ],
   "source": [
    "! pip install biopython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aDCfLeqyxEJc"
   },
   "outputs": [],
   "source": [
    "# Extract sequences: 1 npy = 1 fcgr\n",
    "#! tar -xvf npy-8-mer-9clades.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A-raWdR1mr1w"
   },
   "source": [
    "# Train model \n",
    "0. Mount google drive\n",
    "1. Change to the repository folder using \n",
    "`% cd drive/MyDrive/supervised-dna`  [link](https://stackoverflow.com/questions/48298146/changing-directory-in-google-colab-breaking-out-of-the-python-interpreter)\n",
    "2. Change to the branch called `gisaid` [link](https://stackoverflow.com/questions/60574841/how-to-switch-between-github-branches-in-google-colab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "auhXsooDi8LO"
   },
   "outputs": [],
   "source": [
    "%tensorflow_version 2.x\n",
    "import json\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "# Check if using GPU\n",
    "device_name = tf.test.gpu_device_name()\n",
    "if device_name != '/device:GPU:0':\n",
    "  raise SystemError('GPU device not found')\n",
    "print('Found GPU at: {}'.format(device_name))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1986,
     "status": "ok",
     "timestamp": 1637499338038,
     "user": {
      "displayName": "Jorge Eduardo Avila Cartes",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpY8_qNLAKfPXnIhNebtq2L-QUHe3nTa9iwBpC=s64",
      "userId": "08087796344987933176"
     },
     "user_tz": -60
    },
    "id": "2AJtcIwpn5mk",
    "outputId": "18f8da1e-b334-4509-9ee7-643dbb5e9f56"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**Model created**\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from supervised_dna import (\n",
    "    ModelLoader,\n",
    "    DataGenerator,    \n",
    ")\n",
    "from parameters import PARAMETERS\n",
    "\n",
    "# General parameters\n",
    "KMER = PARAMETERS[\"KMER\"]\n",
    "CLADES = PARAMETERS[\"CLADES\"]\n",
    "\n",
    "# Train parameters\n",
    "BATCH_SIZE = PARAMETERS[\"BATCH_SIZE\"]\n",
    "EPOCHS     = PARAMETERS[\"EPOCHS\"]\n",
    "MODEL      = PARAMETERS[\"MODEL\"]\n",
    "# -1- Model selection\n",
    "loader = ModelLoader()\n",
    "model  = loader(\n",
    "            model_name=MODEL,\n",
    "            n_outputs=len(CLADES)\n",
    "            ) # get compiled model from ./supervised_dna/models\n",
    "\n",
    "# -2- Datasets\n",
    "# load list of images for train and validation sets\n",
    "with open(\"datasets.json\",\"r\") as f:\n",
    "    datasets = json.load(f)\n",
    "list_train = datasets[\"train\"]\n",
    "list_val   = datasets[\"val\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FD8x29kXrn8_"
   },
   "source": [
    "## Prepare datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OUMaeGBTqa0s"
   },
   "outputs": [],
   "source": [
    "# prepare datasets to feed the model\n",
    "preprocessing = lambda npy: np.expand_dims(npy, axis=-1)/ npy.sum()  # add channel (last) axis and divide by the sum of all elements\n",
    "\n",
    "# Instantiate DataGenerator for training set\n",
    "ds_train = DataGenerator(\n",
    "    list_train,\n",
    "    order_output_model = CLADES,\n",
    "    batch_size = BATCH_SIZE,\n",
    "    shuffle = True,\n",
    "    kmer = KMER,\n",
    "    preprocessing = preprocessing,\n",
    ")\n",
    "\n",
    "ds_val = DataGenerator(\n",
    "    list_val,\n",
    "    order_output_model = CLADES,\n",
    "    batch_size = BATCH_SIZE,\n",
    "    shuffle = False,\n",
    "    kmer = KMER,\n",
    "    preprocessing = preprocessing,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QTZQmjborwAT"
   },
   "source": [
    "## Training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "5VLKM81ZrEgQ",
    "outputId": "dc50a7e2-a63d-4dee-fb6c-391d87edb67d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "2536/2536 [==============================] - ETA: 0s - loss: 0.3085 - accuracy: 0.9086\n",
      "Epoch 00001: val_loss improved from inf to 1.62271, saving model to checkpoints/model-01-0.733.hdf5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  layer_config = serialize_layer_fn(layer)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2536/2536 [==============================] - 10130s 4s/step - loss: 0.3085 - accuracy: 0.9086 - val_loss: 1.6227 - val_accuracy: 0.7328 - lr: 0.0010\n",
      "Epoch 2/20\n",
      "2536/2536 [==============================] - ETA: 0s - loss: 0.1415 - accuracy: 0.9616\n",
      "Epoch 00002: val_loss improved from 1.62271 to 0.11326, saving model to checkpoints/model-02-0.969.hdf5\n",
      "2536/2536 [==============================] - 9341s 4s/step - loss: 0.1415 - accuracy: 0.9616 - val_loss: 0.1133 - val_accuracy: 0.9694 - lr: 0.0010\n",
      "Epoch 3/20\n",
      "2536/2536 [==============================] - ETA: 0s - loss: 0.0976 - accuracy: 0.9733"
     ]
    }
   ],
   "source": [
    "# -3- Training\n",
    "## Callbacks\n",
    "\n",
    "# checkpoint: save best weights\n",
    "cb_checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath='checkpoints/model-{epoch:02d}-{val_accuracy:.3f}.hdf5',\n",
    "    monitor='val_loss',\n",
    "    mode='min',\n",
    "    save_best_only=True,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "# reduce learning rate\n",
    "cb_reducelr = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "    monitor='val_loss',\n",
    "    mode='min',\n",
    "    factor=0.1,\n",
    "    patience=8,\n",
    "    verbose=1,\n",
    "    min_lr=0.00001\n",
    ")\n",
    "\n",
    "# stop training if\n",
    "cb_earlystop = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    mode='min',\n",
    "    min_delta=0.001,\n",
    "    patience=10,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "# save history of training\n",
    "cb_csvlogger = tf.keras.callbacks.CSVLogger(\n",
    "    filename='training_log.csv',\n",
    "    separator=',',\n",
    "    append=False\n",
    ")\n",
    "\n",
    "cb_tensorboard = tf.keras.callbacks.TensorBoard(\n",
    "    log_dir='logs', histogram_freq=0, write_graph=True,\n",
    "    write_images=False, write_steps_per_second=False, update_freq='epoch',\n",
    "    profile_batch=0, embeddings_freq=0, embeddings_metadata=None,\n",
    ")\n",
    "\n",
    "model.fit(\n",
    "    ds_train,\n",
    "    validation_data=ds_val,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks=[\n",
    "        cb_checkpoint,\n",
    "        cb_reducelr,\n",
    "        cb_earlystop,\n",
    "        cb_csvlogger,\n",
    "        cb_tensorboard\n",
    "        ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K9HlE3OqCaVj"
   },
   "source": [
    "## Test model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dcHr3JYBue1z"
   },
   "outputs": [],
   "source": [
    "# Test model\n",
    "\n",
    "import json\n",
    "import pandas as pd\n",
    "from parameters import PARAMETERS\n",
    "from supervised_dna import (\n",
    "    ModelLoader,\n",
    "    DataGenerator,\n",
    "    DecoderOutput,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NCl8bOzaEGHr"
   },
   "outputs": [],
   "source": [
    "KMER = PARAMETERS[\"KMER\"]\n",
    "BATCH_SIZE = 1#PARAMETERS[\"BATCH_SIZE\"]\n",
    "CLADES = PARAMETERS[\"CLADES\"]\n",
    "MODEL = PARAMETERS[\"MODEL\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Rj101bZ5EDUJ"
   },
   "outputs": [],
   "source": [
    "# prepare datasets to feed the model\n",
    "MAX_FREQ = 10.\n",
    "preprocessing = lambda npy: np.expand_dims(npy, axis=-1)/ MAX_FREQ  # add channel (last) axis\n",
    "config_generator = dict(\n",
    "    order_output_model = CLADES,\n",
    "    batch_size = BATCH_SIZE,\n",
    "    shuffle = False,\n",
    "    kmer = KMER,\n",
    "    preprocessing = preprocessing,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9273,
     "status": "ok",
     "timestamp": 1637574855574,
     "user": {
      "displayName": "Jorge Eduardo Avila Cartes",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpY8_qNLAKfPXnIhNebtq2L-QUHe3nTa9iwBpC=s64",
      "userId": "08087796344987933176"
     },
     "user_tz": -60
    },
    "id": "-xx_C2RgCtKz",
    "outputId": "f5f3e125-5a34-4c4b-a2d4-a7039d35964d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " **load model weights_path** : checkpoints/model-02-0.969.hdf5\n",
      "\n",
      "**Model created**\n"
     ]
    }
   ],
   "source": [
    "# best weights from training\n",
    "WEIGHTS_PATH = \"checkpoints/model-02-0.969.hdf5\"\n",
    "\n",
    "# -1- Load model\n",
    "loader = ModelLoader()\n",
    "model  = loader(\n",
    "            model_name=MODEL,\n",
    "            n_outputs=len(CLADES),\n",
    "            weights_path=WEIGHTS_PATH,\n",
    "            ) # get compiled model from ./supervised_dna/models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 273,
     "status": "ok",
     "timestamp": 1637575810471,
     "user": {
      "displayName": "Jorge Eduardo Avila Cartes",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpY8_qNLAKfPXnIhNebtq2L-QUHe3nTa9iwBpC=s64",
      "userId": "08087796344987933176"
     },
     "user_tz": -60
    },
    "id": "SNTnADUOESYQ",
    "outputId": "1fb8ff48-23c4-4dd8-c496-9b0501dc57b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5072"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -2- Datasets\n",
    "# load list of images for train and validation sets\n",
    "with open(\"datasets.json\",\"r\") as f:\n",
    "    datasets = json.load(f)\n",
    "list_test = datasets[\"test\"]\n",
    "\n",
    "ds_test = DataGenerator(\n",
    "    list_test,\n",
    "    **config_generator\n",
    ") \n",
    "len(list_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2593265,
     "status": "ok",
     "timestamp": 1637578408827,
     "user": {
      "displayName": "Jorge Eduardo Avila Cartes",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpY8_qNLAKfPXnIhNebtq2L-QUHe3nTa9iwBpC=s64",
      "userId": "08087796344987933176"
     },
     "user_tz": -60
    },
    "id": "Z-e98y7lEflL",
    "outputId": "fe4de3a1-8f92-4bd3-8892-952c076facc2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5072/5072 [==============================] - 1338s 264ms/step\n",
      "5072/5072 [==============================] - 1255s 247ms/step - loss: 0.1188 - accuracy: 0.9702\n"
     ]
    }
   ],
   "source": [
    "# Evaluate model and save metrics\n",
    "prob_result = model.predict(ds_test, verbose=1)\n",
    "result = model.evaluate(ds_test, verbose=1)\n",
    "pd.DataFrame(\n",
    "    dict(zip(model.metrics_names, result)), index=[0]) \\\n",
    "        .to_csv(\"metrics_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Wxm8mzdvEkkJ"
   },
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "Pred = namedtuple(\"Pred\",[\"filename\",\"ground_truth\",\"prediction\",\"confidence\"])\n",
    "decoder = DecoderOutput(order_output_model=CLADES, argmax=True)\n",
    "\n",
    "results_pred = []\n",
    "for prob, file in zip(prob_result, list_test):\n",
    "  gt = file.split(\"/\")[2]\n",
    "  pred = decoder.decode_output(prob, include_confidence=True)\n",
    "  label_pred = pred.get(\"decoded_output\")[0]\n",
    "  conf_pred  = pred.get(\"confidence_model\").get(label_pred)\n",
    "  \n",
    "  metadata_pred = Pred(file,gt,label_pred, conf_pred)\n",
    "  results_pred.append(metadata_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JhO5n6lnFM8O"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(results_pred).to_csv(\"results_pred.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 247,
     "status": "ok",
     "timestamp": 1637578684107,
     "user": {
      "displayName": "Jorge Eduardo Avila Cartes",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpY8_qNLAKfPXnIhNebtq2L-QUHe3nTa9iwBpC=s64",
      "userId": "08087796344987933176"
     },
     "user_tz": -60
    },
    "id": "rI76GW4uGbeS",
    "outputId": "601a41e6-892a-4efd-c586-e2b81a534e76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['S', 'L', 'G', 'V', 'GR', 'GH', 'GV', 'GK']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder.order_output_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p4YdPybYTAwS"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "from typing import List, Union, Optional\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "class DecoderOutput:\n",
    "    '''\n",
    "    From hot-encoder to list of labels\n",
    "    ___\n",
    "    Decode output of a model. Get the predicted classes using 'argmax' or 'umbral'\n",
    "    \n",
    "    argmax=True -> umbral is not used\n",
    "    Use \n",
    "    >> decoder.asJSON(\"path/to/postprocessing.json\") \n",
    "    to save decoder configuration to a json file.\n",
    "    If you want to load a postprocessing from  a json file, instantiate the class without parameters\n",
    "    >> decoder = DecoderOutput()\n",
    "    then provide the path to your json file\n",
    "    >> decoder.fromJSON(\"path/to/postprocessing.json\")\n",
    "    Otherwise, provide the inputs for:\n",
    "    \n",
    "    * Multiclass (many outputs, one choice)\n",
    "    >> decoder = DecoderOutput(order_output_model = [\"class1\", \"class2\", \"class3\"], argmax = True)\n",
    "    \n",
    "    * Multilabel (many outputs, many possible choices)\n",
    "    Using same umbral for each class (if prediction[class]>umbral then return 1, otherwise return 0)\n",
    "    >> decoder = DecoderOutput(order_output_model = [\"class1\", \"class2\", \"class3\"], umbral = 0.5)\n",
    "    Using a different umbral for each class (if prediction[class]>umbral[class] then return 1, otherwise return 0)\n",
    "    >> decoder = DecoderOutput(order_output_model = [\"class1\", \"class2\", \"class3\"], umbral = [0.5, 0.7, 0.6])\n",
    "    '''\n",
    "    VERSION = 1\n",
    "\n",
    "    def __init__(self, order_output_model: Optional[List[str]] = None, argmax: bool = False, umbral: Optional[Union[float, List[float]]] = None,):\n",
    "        self.order_output_model = order_output_model\n",
    "        self.decode_output_by   = 'argmax' if argmax is True else 'umbral'\n",
    "        self.umbral = umbral\n",
    "        self.set_decoder_config()\n",
    "\n",
    "    def set_decoder_config(self,):\n",
    "        self.config = dict(\n",
    "            order_output_model=self.order_output_model,\n",
    "            decode_output_by=self.decode_output_by,\n",
    "            umbral=self.umbral\n",
    "            )\n",
    "\n",
    "    def decode_by_argmax(self, output: List[float]):\n",
    "        \"Decode output using argmax\"\n",
    "        return [self.order_output_model[np.argmax(output)]]\n",
    "\n",
    "    def decode_by_umbral(self, output: List[float]):\n",
    "        \"Decode output using umbral(s)\"\n",
    "        if isinstance(self.umbral, list):\n",
    "            assert len(self.umbral)==len(self.order_output_model), 'list of umbrals does not have the same length than output'\n",
    "            return [class_ for class_, pred, umbral in zip(self.order_output_model, output, self.umbral) if pred>=umbral]\n",
    "        elif isinstance(self.umbral, float):\n",
    "            return [class_ for class_, pred in zip(self.order_output_model, output) if pred>=self.umbral]\n",
    "        else:\n",
    "            raise(\"'umbral' must be a float or a list\")\n",
    "\n",
    "    def decode_output(self, output_model, include_confidence=False):\n",
    "        \"\"\"Decode output of the model\n",
    "        Args:\n",
    "            output_model (np.array): output of a keras model\n",
    "            include_confidence (bool, optional): whether to return or not the output of the model. Defaults to False.\n",
    "        Returns:\n",
    "            dict: dictionary with the desired outputs\n",
    "        \"\"\"     \n",
    "        # Take a list with the output of a keras model with one dense layer as output\n",
    "        output_list = output_model.tolist()\n",
    "\n",
    "        # Decode output by argmax (multiclass - 2 or more outputs / binary - 2 outputs)\n",
    "        if self.decode_output_by == 'argmax':\n",
    "            decoded_output = self.decode_by_argmax(output_list)\n",
    "        \n",
    "        # Decode output by umbral\n",
    "        elif self.decode_output_by == 'umbral':\n",
    "            # Special case: binary-1 output\n",
    "            if len(output_list)==1:\n",
    "                decoded_output = self.order_output_model[int(np.round(output_list[0]))]\n",
    "            else:\n",
    "                # multilabel-2+ outputs\n",
    "                decoded_output = self.decode_by_umbral(output_list)\n",
    "\n",
    "        # Output\n",
    "        if include_confidence: \n",
    "            return dict(\n",
    "                decoded_output=decoded_output, \n",
    "                confidence_model={class_: output \n",
    "                            for class_, output \n",
    "                            in zip(self.order_output_model, output_list)\n",
    "                            }\n",
    "                )\n",
    "        else:\n",
    "            return dict(\n",
    "                decoded_output=decoded_output\n",
    "                )\n",
    "\n",
    "    def asJSON(self, path_save=None):\n",
    "        \"\"\"Save decoder configuration to a json file\"\"\"\n",
    "        path_save = Path(path_save) if path_save else Path(\"postprocessing.json\")\n",
    "        with open(str(path_save), \"w\", encoding=\"utf8\") as fp:\n",
    "            json.dump(self.config, fp, indent=4, ensure_ascii=False)\n",
    "        print(f\"Postprocessing configuration saved at {path_save!r}\")\n",
    "\n",
    "    def fromJSON(self, path_postprocessing):\n",
    "        \"\"\"Load decoder configuration from a json file\"\"\"\n",
    "        # Rear pipeline\n",
    "        path_postprocessing = Path(path_postprocessing)\n",
    "        with open(str(path_postprocessing), \"r\", encoding=\"utf8\") as fp: \n",
    "            postprocessing = json.load(fp)\n",
    "        \n",
    "        self.order_output_model = postprocessing.get(\"order_output_model\")\n",
    "        self.decode_output_by   = postprocessing.get(\"decode_output_by\")\n",
    "        self.umbral = postprocessing.get(\"umbral\")\n",
    "        self.config = postprocessing\n",
    "        print(f\"Postprocessing loaded from {path_postprocessing!r}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bnkE9PQLTV_r"
   },
   "outputs": [],
   "source": [
    "# Precision-recall\n",
    "import pandas as pd\n",
    "from pprint import pprint\n",
    "import plotly.graph_objects as go\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 616
    },
    "executionInfo": {
     "elapsed": 1284,
     "status": "ok",
     "timestamp": 1637584094282,
     "user": {
      "displayName": "Jorge Eduardo Avila Cartes",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgpY8_qNLAKfPXnIhNebtq2L-QUHe3nTa9iwBpC=s64",
      "userId": "08087796344987933176"
     },
     "user_tz": -60
    },
    "id": "WdNWemyQbXvc",
    "outputId": "613a967f-24c7-474d-81bf-e1a79f1a4684"
   },
   "outputs": [
    {
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       "\n",
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       "</body>\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "order_output_model = CLADES\n",
    "\n",
    "list_colors = [\"darkmagenta\",   # S\n",
    "               \"crimson\",# L \n",
    "               \"cadetblue\", \n",
    "               \"aquamarine\", \n",
    "               \"indianred\",\n",
    "               \"darkblue\", \n",
    "               \"bisque\", \n",
    "               \"black\"\n",
    "               ]\n",
    "dict_colors = {class_: color for class_, color in zip(order_output_model, list_colors)}\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_shape(\n",
    "        type='line', line=dict(dash='dash'),\n",
    "        x0=0, x1=1, y0=1, y1=0\n",
    "    )\n",
    "\n",
    "for class_ in order_output_model:\n",
    "    idx_positive_class = order_output_model.index(class_)\n",
    "    # list of y_true and confidence as hot encode\n",
    "    y_true  = [yt[idx_positive_class] for yt in y_true_hot_encode]\n",
    "    y_score = [ys.tolist()[idx_positive_class] for ys in prob_result]\n",
    "\n",
    "    precision, recall, thresholds = precision_recall_curve(y_true, y_score)\n",
    "    umbral = thresholds.tolist()\n",
    "    umbral.append(1)\n",
    "\n",
    "    auc_score = average_precision_score(y_true, y_score)\n",
    "\n",
    "    name = f\"{class_} (AP={auc_score:.2f})\"\n",
    "    fig.add_trace(go.Scatter(x=recall, y=precision, name=name, mode='lines', line=go.scatter.Line(color=dict_colors.get(class_))))\n",
    "\n",
    "    # pprint(results)\n",
    "    pd.DataFrame({\"precision\": precision.tolist(), \"recall\": recall.tolist(), \"umbral\": umbral }).to_csv(\"thresholds_pr_{}.csv\".format(class_)) \n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title='Recall',\n",
    "    yaxis_title='Precision',\n",
    "    yaxis=dict(scaleanchor=\"x\", scaleratio=1),\n",
    "    xaxis=dict(constrain='domain'),\n",
    "    width=800, height=600,\n",
    "    title = \"Curve PR\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VVazysNAdLuO"
   },
   "outputs": [],
   "source": []
  }
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