nb_model_task1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "# Task 1 Models\n",
    "\n",
    "1. Train the PCA models\n",
    "\n",
    "        This results in pickled PCA models.\n",
    "        \n",
    "2. Train autoencoder models (as replacement for PCA)\n",
    "\n",
    "        This results in saved autoencoder models.\n",
    "        \n",
    "3. Train a k-means cluster that sits on top of PCA & autoencoder\n",
    "\n",
    "        This results in pickled k-means models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "## LOAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "task_name = 'task1_sandwich_openface'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "model_path = './models/{0}/'.format(task_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import pickle\n",
    "\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "save_path = './data/tmp_analysis/{0}'.format(task_name)\n",
    "save_train_path = save_path + '_train.csv'\n",
    "save_test_path = save_path + '_test.csv'\n",
    "save_valid_path = save_path + '_valid.csv'\n",
    "\n",
    "\n",
    "Train = pd.read_csv(save_train_path)\n",
    "Test = pd.read_csv(save_test_path)\n",
    "Valid = pd.read_csv(save_valid_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "X = np.asarray(Train.to_numpy()[:,3:]).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "del Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "Xtest = np.asarray(Test.to_numpy()[:,3:]).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "del Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "Xvalid = np.asarray(Valid.to_numpy()[:,3:]).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "del Valid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA Fit\n",
    "I found 32 components to be roughly the inflection curve.\n",
    "I am also creating an 18  component variant just in case we want a direct comparison with action units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.14004417e+06 1.15850393e+06 6.29960089e+04 2.88934186e+04\n",
      " 1.32535819e+04 7.62428729e+03 4.51251183e+03 1.85471213e+03\n",
      " 1.25396775e+03 8.16398949e+02 6.63457263e+02 5.54725981e+02\n",
      " 4.57581946e+02 3.25361672e+02 3.01435591e+02 2.52067206e+02\n",
      " 1.81062212e+02 1.54496626e+02 1.31638164e+02 1.10063955e+02\n",
      " 1.00150383e+02 8.13129041e+01 7.75872584e+01 6.82748313e+01\n",
      " 5.37151867e+01 4.82361960e+01 4.48726076e+01 3.74356825e+01\n",
      " 2.86976579e+01 2.78240610e+01 2.51848050e+01 2.04509484e+01]\n"
     ]
    }
   ],
   "source": [
    "pca_n_32 = PCA(n_components=32)\n",
    "face_pca_n_32 = pca_n_32.fit(X)\n",
    "\n",
    "print(face_pca_n_32.explained_variance_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "pickle.dump(face_pca_n_32,open(model_path+'pca_n_32.p','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.14004475e+06 1.15850441e+06 6.29960873e+04 2.88934367e+04\n",
      " 1.32535925e+04 7.62428578e+03 4.51251334e+03 1.85471006e+03\n",
      " 1.25396690e+03 8.16399138e+02 6.63456651e+02 5.54725557e+02\n",
      " 4.57582465e+02 3.25361931e+02 3.01435497e+02 2.52067135e+02\n",
      " 1.81062106e+02 1.54496649e+02]\n"
     ]
    }
   ],
   "source": [
    "pca_n_18 = PCA(n_components=18)\n",
    "face_pca_n_18 = pca_n_18.fit(X)\n",
    "\n",
    "print(face_pca_n_18.explained_variance_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "pickle.dump(face_pca_n_18,open(model_path+'pca_n_18.p','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoencoder\n",
    "Right now just doing a single layer \n",
    "Perhaps we can try a two layer encoder / two layer decoder next?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "32 Wide encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(169807, 136)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "input_dim = X.shape[1]\n",
    "encoding_dim = 32\n",
    "\n",
    "input_layer = keras.Input(shape=(input_dim,))\n",
    "encoded_layer = layers.Dense(encoding_dim, activation='relu')(input_layer)\n",
    "decoded_layer = layers.Dense(input_dim, activation='relu')(encoded_layer)\n",
    "\n",
    "autoencoder_32 = keras.Model(input_layer,decoded_layer)\n",
    "\n",
    "encoder_32 = keras.Model(input_layer, encoded_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "autoencoder_32.compile(optimizer='adam',\n",
    "                    loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 136)]             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                4384      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 136)               4488      \n",
      "=================================================================\n",
      "Total params: 8,872\n",
      "Trainable params: 8,872\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "autoencoder_32.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "2654/2654 [==============================] - 7s 3ms/step - loss: -5003.9410 - val_loss: -6141.4326\n",
      "Epoch 2/2\n",
      "2654/2654 [==============================] - 6s 2ms/step - loss: -6139.5642 - val_loss: -6141.5459\n"
     ]
    }
   ],
   "source": [
    "history = autoencoder_32.fit(X, X, epochs=2, batch_size=64, verbose=1, validation_data=(Xtest, Xtest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.plot([0]+history.history['loss'], label='train')\n",
    "plt.plot([0]+history.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "predictions = autoencoder_32.predict(Xvalid)\n",
    "# TODO I should probably do something with the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "autoencoder_32.save(model_path + 'autoencoder_32.h5', save_format='h5')\n",
    "encoder_32.save(model_path + 'encoder_32.h5', save_format='h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5 wide encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "input_dim = X.shape[1]\n",
    "encoding_dim = 18\n",
    "\n",
    "input_layer = keras.Input(shape=(input_dim,))\n",
    "encoded_layer = layers.Dense(encoding_dim, activation='relu')(input_layer)\n",
    "decoded_layer = layers.Dense(input_dim, activation='relu')(encoded_layer)\n",
    "\n",
    "autoencoder_18 = keras.Model(input_layer,decoded_layer)\n",
    "\n",
    "encoder_18 = keras.Model(input_layer, encoded_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "autoencoder_18.compile(optimizer='adam',\n",
    "                    loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 136)]             0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 18)                2466      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 136)               2584      \n",
      "=================================================================\n",
      "Total params: 5,050\n",
      "Trainable params: 5,050\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "autoencoder_18.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "2654/2654 [==============================] - 7s 2ms/step - loss: -3808.8768 - val_loss: -4512.2061\n",
      "Epoch 2/2\n",
      "2654/2654 [==============================] - 6s 2ms/step - loss: -4527.2244 - val_loss: -5015.5815\n"
     ]
    }
   ],
   "source": [
    "history = autoencoder_18.fit(X, X, epochs=2, batch_size=64, verbose=1, validation_data=(Xtest, Xtest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.plot([0]+history.history['loss'], label='train')\n",
    "plt.plot([0]+history.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "predictions = autoencoder_18.predict(Xvalid)\n",
    "# TODO I should probably do something with the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "autoencoder_18.save(model_path + 'autoencoder_18.h5', save_format='h5')\n",
    "encoder_18.save(model_path + 'encoder_18.h5', save_format='h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-means\n",
    "Not sure what this is going to do \n",
    "(my guess is it is going to cluster similar participants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "def visualize_labels(y):\n",
    "    idx = np.arange(len(y))\n",
    "\n",
    "    fig, (ax1,ax2) = plt.subplots(nrows=2, sharex=True)\n",
    "\n",
    "    ax1.imshow(y[np.newaxis,:], cmap=\"plasma\", aspect=\"auto\")\n",
    "    ax1.set_yticks([])\n",
    "\n",
    "    ax2.scatter(idx,y)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "X_pca = face_pca_n_32.transform(X)\n",
    "kmeans_5 = KMeans(n_clusters=5, random_state=0).fit(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = kmeans_5.predict(face_pca_n_32.transform(Xtest))\n",
    "visualize_labels(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = kmeans_5.predict(face_pca_n_32.transform(Xvalid))\n",
    "visualize_labels(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "pickle.dump(kmeans_5,open(model_path+'kmeans_5_from_pca.p','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "encoder_32 = keras.models.load_model(model_path + 'encoder_32.h5')\n",
    "\n",
    "X_auto = encoder_32.predict(X)\n",
    "kmeans_5 = KMeans(n_clusters=5, random_state=0).fit(X_auto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = kmeans_5.predict(encoder_32.predict(Xtest))\n",
    "visualize_labels(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = kmeans_5.predict(encoder_32.predict(Xvalid))\n",
    "visualize_labels(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "pickle.dump(kmeans_5,open(model_path+'kmeans_5_from_auto.p','wb'))"
   ]
  }
 ],
 "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}