custumer_forecast.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-12 08:29:59.686449: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['/device:GPU:0']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-12 08:29:59.692454: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-12 08:29:59.692835: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-12 08:29:59.695538: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-11-12 08:29:59.695926: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-12 08:29:59.696240: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-12 08:29:59.696489: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-12 08:30:00.382835: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-12 08:30:00.383129: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-12 08:30:00.383370: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2021-11-12 08:30:00.383770: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4112 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import tensorflow as tf\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.layers import TimeDistributed, RepeatVector, MaxPooling1D, Conv1D, Flatten, Input, Reshape, BatchNormalization\n",
    "from keras.layers.recurrent import LSTM, GRU\n",
    "from tensorflow.keras.layers.experimental import RandomFourierFeatures\n",
    "from keras.models import Sequential, Model\n",
    "from keras import optimizers\n",
    "from keras import backend as K\n",
    "import keras\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n",
    "\n",
    "K._get_available_gpus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(dataframe, cols, target_col, win_len, pred_points, test_size, normalise_window):\n",
    "\n",
    "    data = dataframe.loc[:,cols]\n",
    "    n = data.shape[0]\n",
    "    data_train = data.iloc[:int(n*(1-test_size)),:]\n",
    "    data_test = data.iloc[int(n*(1-test_size)):,:]\n",
    "\n",
    "    dmax, dmin = 0, 0\n",
    "    if normalise_window:        \n",
    "        min_train = data_train.min()\n",
    "        max_train = data_train.max()\n",
    "        data_train = (data_train - min_train)/(max_train - min_train)\n",
    "        data_test = (data_test - min_train)/(max_train - min_train)\n",
    "        dmax = max_train[target_col]\n",
    "        dmin = min_train[target_col]\n",
    "\n",
    "    sc = {\n",
    "        'min': dmin,\n",
    "        'max': dmax\n",
    "    }\n",
    "\n",
    "    sequence_length = win_len + pred_points\n",
    "\n",
    "    X_train, y_train = build_windowed_mat(data_train, target_col, win_len, sequence_length)\n",
    "    X_test, y_test =  build_windowed_mat(data_test, target_col, win_len, sequence_length)    \n",
    "   \n",
    "    return X_train, y_train, X_test, y_test, sc\n",
    "\n",
    "def build_windowed_mat(data, target_col, win_len, sequence_length):\n",
    "\n",
    "    # X, y = [], []    \n",
    "    n_feats = data.shape[1]\n",
    "    n = data.shape[0]    \n",
    "    # for index in range(n - sequence_length):\n",
    "    #     X.append(data.iloc[index: index + win_len,:].values.T.ravel().tolist())\n",
    "    #     y.append(data[target_col].iloc[index + win_len:index + sequence_length].values.T.ravel().tolist())\n",
    "\n",
    "    X = [data.iloc[index: index + win_len,:].values.T.ravel().tolist() for index in range(n - sequence_length)]\n",
    "    y = [data[target_col].iloc[index + win_len:index + sequence_length].values.T.ravel().tolist() for index in range(n - sequence_length)]\n",
    "\n",
    "    X = np.array(X)    \n",
    "    X = np.reshape(X, (X.shape[0], win_len, n_feats), order='F')\n",
    "    y = np.array(y)\n",
    "    y = np.reshape(y, (y.shape[0], sequence_length - win_len, len(target_col)), order='F')\n",
    "   \n",
    "    return X,y\n",
    "\n",
    "def predict_sequence_full_univar(model, curr_frame, pred_points, future_points):\n",
    "    #Shift the window by 1 new prediction each time, re-run predictions on new window\n",
    "    if len(curr_frame.shape) == 1:\n",
    "        curr_frame = curr_frame[np.newaxis,:,np.newaxis]\n",
    "    elif len(curr_frame.shape) == 2:\n",
    "        curr_frame = curr_frame[np.newaxis,:,:]\n",
    "\n",
    "    predicted = []   \n",
    "    N = int(np.ceil(future_points/pred_points) )\n",
    "    for i in range(N):        \n",
    "        # predicted += model.predict(curr_frame)[0,:].tolist()\n",
    "        predicted += model.predict(curr_frame)[0,:,0].tolist()\n",
    "        curr_frame = np.array(curr_frame[0,pred_points:,0].tolist() + predicted[-pred_points:])    \n",
    "        curr_frame = curr_frame[np.newaxis,:,np.newaxis]\n",
    "        \n",
    "    return np.array(predicted)\n",
    "    \n",
    "def plot_results(predicted_data, true_data, dates, step):\n",
    "    fig = go.Figure()\n",
    "    x = np.array(range(0,true_data.size, step), dtype=int)\n",
    "    dates = np.array(dates)\n",
    "    fig.add_trace(go.Scatter(y=true_data, mode='lines', name='True Data'))\n",
    "    fig.add_trace(go.Scatter(y=predicted_data, mode='lines', name='Prediction'))\n",
    "    fig.update_layout(\n",
    "        xaxis = dict(\n",
    "            tickmode = 'array',\n",
    "            tickvals = x,\n",
    "            ticktext = dates[x],\n",
    "            tickangle = 60\n",
    "        ),\n",
    "        margin = dict(\n",
    "            autoexpand=False,\n",
    "            l=30,\n",
    "            r=30,\n",
    "            b=100\n",
    "        )\n",
    "    )\n",
    "    fig.show()\n",
    "\n",
    "\n",
    "def plot_time_serie(time_serie_train, time_serie_test, x_ticks):\n",
    "    train_len = len(time_serie_train)\n",
    "    test_len = len(time_serie_test)\n",
    "    time = np.arange(0, train_len + test_len, 1)\n",
    "    plt.figure(facecolor='white', figsize=(25,5)) \n",
    "    plt.plot(time[0:train_len],time_serie_train, label='Train Data') \n",
    "    plt.plot(time[train_len:],time_serie_test, label='Test data')\n",
    "    plt.xticks(time[0:time.size:100], x_ticks[0:time.size:100], rotation=90, fontsize=10)\n",
    "    plt.legend()\n",
    "    plt.xlim(0,time.size)\n",
    "    plt.title('Deliveries per hour')\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tienda = 'Tienda83'\n",
    "model_df = pd.read_csv('data/model_data_' + tienda + 'last.csv')\n",
    "model_df.set_index('FechaHora', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7652, 24, 1) (7652, 1, 1) (829, 24, 1) (829, 1, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feats = ['No_Clientes']\n",
    "pred_points = 1\n",
    "window_len = 24\n",
    "normalize = True\n",
    "test_size = .1\n",
    "X_train, y_train, X_test, y_test, sc = load_data(model_df, feats, ['No_Clientes'], window_len, pred_points, test_size, normalize)\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n",
    "n = model_df.shape[0]\n",
    "xtick_labels = model_df.index.tolist()[window_len+pred_points:int(n*(1-test_size))] + model_df.index.tolist()[int(n*(1-test_size))+window_len+pred_points:]\n",
    "plot_time_serie(y_train[:,0,-1], y_test[:,0,-1], xtick_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer gru_12 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "Model: \"sequential_12\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_24 (Conv1D)           (None, 14, 32)            384       \n",
      "_________________________________________________________________\n",
      "conv1d_25 (Conv1D)           (None, 4, 64)             22592     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_12 (MaxPooling (None, 2, 64)             0         \n",
      "_________________________________________________________________\n",
      "flatten_12 (Flatten)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "repeat_vector_12 (RepeatVect (None, 1, 128)            0         \n",
      "_________________________________________________________________\n",
      "gru_12 (GRU)                 (None, 1, 24)             11016     \n",
      "_________________________________________________________________\n",
      "dropout_12 (Dropout)         (None, 1, 24)             0         \n",
      "_________________________________________________________________\n",
      "time_distributed_24 (TimeDis (None, 1, 32)             800       \n",
      "_________________________________________________________________\n",
      "time_distributed_25 (TimeDis (None, 1, 1)              33        \n",
      "=================================================================\n",
      "Total params: 34,825\n",
      "Trainable params: 34,825\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.load_model('models/Modelo'+tienda+'Win24ConvEncUVv2')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-12 08:31:32.641535: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2021-11-12 08:31:33.640273: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8204\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7652, 1, 1)\n",
      "(7652, 1, 1)\n"
     ]
    },
    {
     "data": {
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   "source": [
    "# Single point train reconstruction\n",
    "predictions = model.predict(X_train)\n",
    "print(predictions.shape)\n",
    "print(y_train.shape)\n",
    "if normalize:\n",
    "    predictions = predictions[:,0,0]*(sc['max']['No_Clientes'] - sc['min']['No_Clientes']) + sc['min']['No_Clientes']\n",
    "    label_train = y_train[:,0,0]*(sc['max']['No_Clientes'] - sc['min']['No_Clientes']) + sc['min']['No_Clientes']\n",
    "\n",
    "xtick_labels = model_df.index.tolist()[:int(n*(1-test_size))+window_len+pred_points]\n",
    "plot_results(predictions[:],label_train[:], xtick_labels, 110)\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error\n",
    "print(mean_squared_error(predictions,label_train))\n",
    "print(mean_absolute_error(predictions,label_train))\n",
    "print(mean_absolute_percentage_error(predictions,label_train))"
   ]
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   "metadata": {},
   "outputs": [
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     "text": [
      "(829, 1, 1)\n",
      "(829, 1, 1)\n"
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    "# Single point prediction\n",
    "predictions = model.predict(X_test)\n",
    "print(predictions.shape)\n",
    "print(y_test.shape)\n",
    "if normalize:\n",
    "    predictions = predictions[:,0,0]*(sc['max']['No_Clientes'] - sc['min']['No_Clientes']) + sc['min']['No_Clientes']\n",
    "    label_test = y_test[:,0,0]*(sc['max']['No_Clientes'] - sc['min']['No_Clientes']) + sc['min']['No_Clientes']\n",
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    "xtick_labels = model_df.index.tolist()[int(n*(1-test_size))+window_len+pred_points:]\n",
    "plot_results(predictions[:],label_test[:], xtick_labels, 20)\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error\n",
    "print(mean_squared_error(predictions,label_test))\n",
    "print(mean_absolute_error(predictions,label_test))\n",
    "print(mean_absolute_percentage_error(predictions,label_test))"
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          "2021-11-10:16",
          "2021-11-11:10",
          "2021-11-11:11",
          "2021-11-11:12",
          "2021-11-11:13",
          "2021-11-11:14",
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      "1195.976875407296\n",
      "27.84232951158827\n",
      "2.2659734874200903\n"
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   "source": [
    "# Autoregresive prediction\n",
    "num = 818\n",
    "pred_hor = 11\n",
    "test_sample = X_test[num,:,:]\n",
    "test_sample = test_sample[np.newaxis,:,:]\n",
    "predictions = predict_sequence_full_univar(model, test_sample, pred_points, pred_hor) \n",
    "print(predictions.shape)\n",
    "if normalize:\n",
    "    predictions = predictions*(sc['max']['No_Clientes'] - sc['min']['No_Clientes']) + sc['min']['No_Clientes']\n",
    "    label_test = y_test[num:num+pred_hor,0,0]*(sc['max']['No_Clientes'] - sc['min']['No_Clientes']) + sc['min']['No_Clientes']\n",
    "\n",
    "xtick_labels = model_df.index.tolist()[int(n*(1-test_size))+window_len+num:]\n",
    "plot_results(predictions,label_test, xtick_labels, 1)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error\n",
    "print(mean_squared_error(predictions,label_test))\n",
    "print(mean_absolute_error(predictions,label_test))\n",
    "print(mean_absolute_percentage_error(predictions,label_test))"
   ]
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    "num = -1\n",
    "pred_hor = 14\n",
    "test_sample = X_test[num,:,:]\n",
    "test_sample = test_sample[np.newaxis,:,:]\n",
    "predictions = predict_sequence_full_univar(model, test_sample, pred_points, pred_hor)#[1:] # the first value makes reference to the last value of the lat night\n",
    "print(predictions.shape)\n",
    "if normalize:\n",
    "    predictions = predictions*(sc['max']['No_Clientes'] - sc['min']['No_Clientes']) + sc['min']['No_Clientes']\n",
    "\n",
    "fig = go.Figure()\n",
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    "fig.show()\n"
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