Deep_Auto_ViML_Timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "\u001b[31mERROR: Could not find a version that satisfies the requirement tensorflow-estimato (from versions: none)\u001b[0m\n",
      "\u001b[31mERROR: No matching distribution found for tensorflow-estimato\u001b[0m\n",
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Requirement already satisfied: tensorflow-text==2.7.3 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (2.7.3)\n",
      "Requirement already satisfied: tensorflow<2.8,>=2.7.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow-text==2.7.3) (2.7.0)\n",
      "Requirement already satisfied: tensorflow-hub>=0.8.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow-text==2.7.3) (0.12.0)\n",
      "Requirement already satisfied: gast<0.5.0,>=0.2.1 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (0.4.0)\n",
      "Requirement already satisfied: six>=1.12.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.15.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.6.3)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (3.3.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.1.0)\n",
      "Requirement already satisfied: absl-py>=0.4.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (0.15.0)\n",
      "Requirement already satisfied: numpy>=1.14.5 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.19.5)\n",
      "Requirement already satisfied: libclang>=9.0.1 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (13.0.0)\n",
      "Requirement already satisfied: h5py>=2.9.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (3.1.0)\n",
      "Requirement already satisfied: protobuf>=3.9.2 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (3.19.1)\n",
      "Requirement already satisfied: tensorflow-estimator<2.8,~=2.7.0rc0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (2.7.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (3.7.4.3)\n",
      "Requirement already satisfied: wheel<1.0,>=0.32.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (0.37.1)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.1 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.1.2)\n",
      "Requirement already satisfied: flatbuffers<3.0,>=1.12 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.12)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.21.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (0.23.1)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (0.2.0)\n",
      "Requirement already satisfied: tensorboard~=2.6 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (2.7.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.12.1)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.43.0)\n",
      "Requirement already satisfied: keras<2.8,>=2.7.0rc0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (2.7.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (3.3.6)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.8.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (0.6.1)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (2.26.0)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (58.0.4)\n",
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      "Requirement already satisfied: werkzeug>=0.11.15 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (2.0.2)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (2.3.3)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /home/adarsh/.local/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /home/adarsh/.local/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (4.8)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /home/adarsh/.local/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (4.2.4)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /home/adarsh/.local/lib/python3.8/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.3.0)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in /home/adarsh/.local/lib/python3.8/site-packages (from markdown>=2.6.8->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (4.10.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /home/adarsh/.local/lib/python3.8/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (3.6.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /home/adarsh/.local/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (0.4.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (2021.10.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (1.26.7)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (2.0.9)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (3.3)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow<2.8,>=2.7.0->tensorflow-text==2.7.3) (3.1.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade tensorflow-estimato\n",
    "!pip install tensorflow-text==2.7.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Requirement already satisfied: tensorflow in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (2.7.0)\n",
      "Requirement already satisfied: keras in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (2.7.0)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.1 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (1.1.2)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow) (1.43.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (1.1.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (3.3.0)\n",
      "Requirement already satisfied: gast<0.5.0,>=0.2.1 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (0.4.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow) (3.7.4.3)\n",
      "Requirement already satisfied: numpy>=1.14.5 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow) (1.19.5)\n",
      "Requirement already satisfied: absl-py>=0.4.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow) (0.15.0)\n",
      "Requirement already satisfied: flatbuffers<3.0,>=1.12 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (1.12)\n",
      "Requirement already satisfied: h5py>=2.9.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (3.1.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.32.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (0.37.1)\n",
      "Requirement already satisfied: libclang>=9.0.1 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow) (13.0.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (1.12.1)\n",
      "Requirement already satisfied: tensorflow-estimator<2.8,~=2.7.0rc0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow) (2.7.0)\n",
      "Requirement already satisfied: tensorboard~=2.6 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (2.7.0)\n",
      "Requirement already satisfied: six>=1.12.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (1.15.0)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.21.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorflow) (0.23.1)\n",
      "Requirement already satisfied: protobuf>=3.9.2 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorflow) (3.19.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow) (0.6.1)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow) (2.3.3)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow) (2.26.0)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow) (0.4.6)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow) (2.0.2)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow) (58.0.4)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow) (3.3.6)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /home/adarsh/.local/lib/python3.8/site-packages (from tensorboard~=2.6->tensorflow) (1.8.0)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /home/adarsh/.local/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (4.2.4)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /home/adarsh/.local/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /home/adarsh/.local/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (4.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /home/adarsh/.local/lib/python3.8/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow) (1.3.0)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in /home/adarsh/.local/lib/python3.8/site-packages (from markdown>=2.6.8->tensorboard~=2.6->tensorflow) (4.10.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /home/adarsh/.local/lib/python3.8/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard~=2.6->tensorflow) (3.6.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /home/adarsh/.local/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (2.0.9)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (1.26.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow) (3.3)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /home/adarsh/anaconda3/envs/DAViML/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow) (3.1.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade tensorflow keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Silencing TF2.x warnings\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-30 18:36:35.341066: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n",
      "2022-01-30 18:36:35.341170: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: Ra\n",
      "2022-01-30 18:36:35.341232: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: Ra\n",
      "2022-01-30 18:36:35.341478: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 470.86.0\n",
      "2022-01-30 18:36:35.341579: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 470.86.0\n",
      "2022-01-30 18:36:35.341602: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 470.86.0\n",
      "2022-01-30 18:36:35.342025: I tensorflow/core/platform/cpu_feature_guard.cc:151] 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Imported deep_auto_viml. version=0.0.77\n",
      "from deep_autoviml import deep_autoviml as deepauto\n",
      "-------------------\n",
      "model, cat_vocab_dict = deepauto.fit(train, target, keras_model_type=\"fast\",\n",
      "\t\tproject_name=\"deep_autoviml\", keras_options=keras_options,  \n",
      "\t\tmodel_options=model_options, save_model_flag=True, use_my_model='',\n",
      "\t\tmodel_use_case='', verbose=0)\n",
      "\n",
      "predictions = deepauto.predict(model, project_name, test_dataset=test,\n",
      "                                 keras_model_type=keras_model_type, \n",
      "                                 cat_vocab_dict=cat_vocab_dict)\n",
      "                                \n"
     ]
    }
   ],
   "source": [
    "# use pandas numpy as usual\n",
    "import tensorflow  as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from deep_autoviml import deep_autoviml as deepauto\n",
    "from tensorflow.python.ops.math_ops import reduce_prod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.7.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__\n",
    "import keras\n",
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath = ''\n",
    "filename = 'energydata_complete.csv'\n",
    "filename = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv'\n",
    "sep = ','\n",
    "\n",
    "\n",
    "#target = ['target_1','target_2','target_3']\n",
    "target = 'Appliances'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19735, 29)\n"
     ]
    }
   ],
   "source": [
    "# load dataset\n",
    "train = pd.read_csv(filename, header=0, sep=sep)\n",
    "print(train.shape)\n",
    "train = train.dropna()\n",
    "train.to_csv(\"/home/adarsh/Desktop/OpenWorld/deep_AutoViML/dataset/energydata_complete.csv\")\n",
    "filename = \"/home/adarsh/Desktop/OpenWorld/deep_AutoViML/dataset/energydata_complete.csv\"\n",
    "train_datafile = datapath+filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19735, 29) (19735, 30)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>date</th>\n",
       "      <th>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>RH_4</th>\n",
       "      <th>T5</th>\n",
       "      <th>RH_5</th>\n",
       "      <th>T6</th>\n",
       "      <th>RH_6</th>\n",
       "      <th>T7</th>\n",
       "      <th>RH_7</th>\n",
       "      <th>T8</th>\n",
       "      <th>RH_8</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>rv1</th>\n",
       "      <th>rv2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-11 17:00:00</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>47.596667</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>44.730000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>45.566667</td>\n",
       "      <td>17.166667</td>\n",
       "      <td>55.200000</td>\n",
       "      <td>7.026667</td>\n",
       "      <td>84.256667</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>41.626667</td>\n",
       "      <td>18.2000</td>\n",
       "      <td>48.900000</td>\n",
       "      <td>17.033333</td>\n",
       "      <td>45.5300</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>733.5</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>13.275433</td>\n",
       "      <td>13.275433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-11 17:10:00</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>46.693333</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>44.722500</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>45.992500</td>\n",
       "      <td>17.166667</td>\n",
       "      <td>55.200000</td>\n",
       "      <td>6.833333</td>\n",
       "      <td>84.063333</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>41.560000</td>\n",
       "      <td>18.2000</td>\n",
       "      <td>48.863333</td>\n",
       "      <td>17.066667</td>\n",
       "      <td>45.5600</td>\n",
       "      <td>6.483333</td>\n",
       "      <td>733.6</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>59.166667</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>18.606195</td>\n",
       "      <td>18.606195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-11 17:20:00</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>44.626667</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>44.933333</td>\n",
       "      <td>18.926667</td>\n",
       "      <td>45.890000</td>\n",
       "      <td>17.166667</td>\n",
       "      <td>55.090000</td>\n",
       "      <td>6.560000</td>\n",
       "      <td>83.156667</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>41.433333</td>\n",
       "      <td>18.2000</td>\n",
       "      <td>48.730000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.5000</td>\n",
       "      <td>6.366667</td>\n",
       "      <td>733.7</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>28.642668</td>\n",
       "      <td>28.642668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-11 17:30:00</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>46.066667</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>45.723333</td>\n",
       "      <td>17.166667</td>\n",
       "      <td>55.090000</td>\n",
       "      <td>6.433333</td>\n",
       "      <td>83.423333</td>\n",
       "      <td>17.133333</td>\n",
       "      <td>41.290000</td>\n",
       "      <td>18.1000</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.4000</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>733.8</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>45.410389</td>\n",
       "      <td>45.410389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2016-01-11 17:40:00</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>19.890000</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>44.530000</td>\n",
       "      <td>19.790000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>45.530000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>55.090000</td>\n",
       "      <td>6.366667</td>\n",
       "      <td>84.893333</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>41.230000</td>\n",
       "      <td>18.1000</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.4000</td>\n",
       "      <td>6.133333</td>\n",
       "      <td>733.9</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>10.084097</td>\n",
       "      <td>10.084097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19730</th>\n",
       "      <td>19730</td>\n",
       "      <td>2016-05-27 17:20:00</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>25.566667</td>\n",
       "      <td>46.560000</td>\n",
       "      <td>25.890000</td>\n",
       "      <td>42.025714</td>\n",
       "      <td>27.200000</td>\n",
       "      <td>41.163333</td>\n",
       "      <td>24.700000</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>52.400000</td>\n",
       "      <td>24.796667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>24.7000</td>\n",
       "      <td>50.074000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>46.7900</td>\n",
       "      <td>22.733333</td>\n",
       "      <td>755.2</td>\n",
       "      <td>55.666667</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>43.096812</td>\n",
       "      <td>43.096812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19731</th>\n",
       "      <td>19731</td>\n",
       "      <td>2016-05-27 17:30:00</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>25.754000</td>\n",
       "      <td>42.080000</td>\n",
       "      <td>27.133333</td>\n",
       "      <td>41.223333</td>\n",
       "      <td>24.700000</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>23.230000</td>\n",
       "      <td>52.326667</td>\n",
       "      <td>24.196667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.557143</td>\n",
       "      <td>44.414286</td>\n",
       "      <td>24.7000</td>\n",
       "      <td>49.790000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>46.7900</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>755.2</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>49.282940</td>\n",
       "      <td>49.282940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19732</th>\n",
       "      <td>19732</td>\n",
       "      <td>2016-05-27 17:40:00</td>\n",
       "      <td>270</td>\n",
       "      <td>10</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>46.596667</td>\n",
       "      <td>25.628571</td>\n",
       "      <td>42.768571</td>\n",
       "      <td>27.050000</td>\n",
       "      <td>41.690000</td>\n",
       "      <td>24.700000</td>\n",
       "      <td>45.730000</td>\n",
       "      <td>23.230000</td>\n",
       "      <td>52.266667</td>\n",
       "      <td>23.626667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.540000</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>24.7000</td>\n",
       "      <td>49.660000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>46.7900</td>\n",
       "      <td>22.466667</td>\n",
       "      <td>755.2</td>\n",
       "      <td>56.333333</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>25.333333</td>\n",
       "      <td>13.266667</td>\n",
       "      <td>29.199117</td>\n",
       "      <td>29.199117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19733</th>\n",
       "      <td>19733</td>\n",
       "      <td>2016-05-27 17:50:00</td>\n",
       "      <td>420</td>\n",
       "      <td>10</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>46.990000</td>\n",
       "      <td>25.414000</td>\n",
       "      <td>43.036000</td>\n",
       "      <td>26.890000</td>\n",
       "      <td>41.290000</td>\n",
       "      <td>24.700000</td>\n",
       "      <td>45.790000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>52.200000</td>\n",
       "      <td>22.433333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>44.295714</td>\n",
       "      <td>24.6625</td>\n",
       "      <td>49.518750</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>46.8175</td>\n",
       "      <td>22.333333</td>\n",
       "      <td>755.2</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>26.166667</td>\n",
       "      <td>13.233333</td>\n",
       "      <td>6.322784</td>\n",
       "      <td>6.322784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19734</th>\n",
       "      <td>19734</td>\n",
       "      <td>2016-05-27 18:00:00</td>\n",
       "      <td>430</td>\n",
       "      <td>10</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>46.600000</td>\n",
       "      <td>25.264286</td>\n",
       "      <td>42.971429</td>\n",
       "      <td>26.823333</td>\n",
       "      <td>41.156667</td>\n",
       "      <td>24.700000</td>\n",
       "      <td>45.963333</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>52.200000</td>\n",
       "      <td>21.026667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>44.054000</td>\n",
       "      <td>24.7360</td>\n",
       "      <td>49.736000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>46.8450</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>755.2</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>34.118851</td>\n",
       "      <td>34.118851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19735 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0                 date  Appliances  lights         T1  \\\n",
       "0               0  2016-01-11 17:00:00          60      30  19.890000   \n",
       "1               1  2016-01-11 17:10:00          60      30  19.890000   \n",
       "2               2  2016-01-11 17:20:00          50      30  19.890000   \n",
       "3               3  2016-01-11 17:30:00          50      40  19.890000   \n",
       "4               4  2016-01-11 17:40:00          60      40  19.890000   \n",
       "...           ...                  ...         ...     ...        ...   \n",
       "19730       19730  2016-05-27 17:20:00         100       0  25.566667   \n",
       "19731       19731  2016-05-27 17:30:00          90       0  25.500000   \n",
       "19732       19732  2016-05-27 17:40:00         270      10  25.500000   \n",
       "19733       19733  2016-05-27 17:50:00         420      10  25.500000   \n",
       "19734       19734  2016-05-27 18:00:00         430      10  25.500000   \n",
       "\n",
       "            RH_1         T2       RH_2         T3       RH_3         T4  \\\n",
       "0      47.596667  19.200000  44.790000  19.790000  44.730000  19.000000   \n",
       "1      46.693333  19.200000  44.722500  19.790000  44.790000  19.000000   \n",
       "2      46.300000  19.200000  44.626667  19.790000  44.933333  18.926667   \n",
       "3      46.066667  19.200000  44.590000  19.790000  45.000000  18.890000   \n",
       "4      46.333333  19.200000  44.530000  19.790000  45.000000  18.890000   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "19730  46.560000  25.890000  42.025714  27.200000  41.163333  24.700000   \n",
       "19731  46.500000  25.754000  42.080000  27.133333  41.223333  24.700000   \n",
       "19732  46.596667  25.628571  42.768571  27.050000  41.690000  24.700000   \n",
       "19733  46.990000  25.414000  43.036000  26.890000  41.290000  24.700000   \n",
       "19734  46.600000  25.264286  42.971429  26.823333  41.156667  24.700000   \n",
       "\n",
       "            RH_4         T5       RH_5         T6       RH_6         T7  \\\n",
       "0      45.566667  17.166667  55.200000   7.026667  84.256667  17.200000   \n",
       "1      45.992500  17.166667  55.200000   6.833333  84.063333  17.200000   \n",
       "2      45.890000  17.166667  55.090000   6.560000  83.156667  17.200000   \n",
       "3      45.723333  17.166667  55.090000   6.433333  83.423333  17.133333   \n",
       "4      45.530000  17.200000  55.090000   6.366667  84.893333  17.200000   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "19730  45.590000  23.200000  52.400000  24.796667   1.000000  24.500000   \n",
       "19731  45.590000  23.230000  52.326667  24.196667   1.000000  24.557143   \n",
       "19732  45.730000  23.230000  52.266667  23.626667   1.000000  24.540000   \n",
       "19733  45.790000  23.200000  52.200000  22.433333   1.000000  24.500000   \n",
       "19734  45.963333  23.200000  52.200000  21.026667   1.000000  24.500000   \n",
       "\n",
       "            RH_7       T8       RH_8         T9     RH_9      T_out  \\\n",
       "0      41.626667  18.2000  48.900000  17.033333  45.5300   6.600000   \n",
       "1      41.560000  18.2000  48.863333  17.066667  45.5600   6.483333   \n",
       "2      41.433333  18.2000  48.730000  17.000000  45.5000   6.366667   \n",
       "3      41.290000  18.1000  48.590000  17.000000  45.4000   6.250000   \n",
       "4      41.230000  18.1000  48.590000  17.000000  45.4000   6.133333   \n",
       "...          ...      ...        ...        ...      ...        ...   \n",
       "19730  44.500000  24.7000  50.074000  23.200000  46.7900  22.733333   \n",
       "19731  44.414286  24.7000  49.790000  23.200000  46.7900  22.600000   \n",
       "19732  44.400000  24.7000  49.660000  23.200000  46.7900  22.466667   \n",
       "19733  44.295714  24.6625  49.518750  23.200000  46.8175  22.333333   \n",
       "19734  44.054000  24.7360  49.736000  23.200000  46.8450  22.200000   \n",
       "\n",
       "       Press_mm_hg     RH_out  Windspeed  Visibility  Tdewpoint        rv1  \\\n",
       "0            733.5  92.000000   7.000000   63.000000   5.300000  13.275433   \n",
       "1            733.6  92.000000   6.666667   59.166667   5.200000  18.606195   \n",
       "2            733.7  92.000000   6.333333   55.333333   5.100000  28.642668   \n",
       "3            733.8  92.000000   6.000000   51.500000   5.000000  45.410389   \n",
       "4            733.9  92.000000   5.666667   47.666667   4.900000  10.084097   \n",
       "...            ...        ...        ...         ...        ...        ...   \n",
       "19730        755.2  55.666667   3.333333   23.666667  13.333333  43.096812   \n",
       "19731        755.2  56.000000   3.500000   24.500000  13.300000  49.282940   \n",
       "19732        755.2  56.333333   3.666667   25.333333  13.266667  29.199117   \n",
       "19733        755.2  56.666667   3.833333   26.166667  13.233333   6.322784   \n",
       "19734        755.2  57.000000   4.000000   27.000000  13.200000  34.118851   \n",
       "\n",
       "             rv2  \n",
       "0      13.275433  \n",
       "1      18.606195  \n",
       "2      28.642668  \n",
       "3      45.410389  \n",
       "4      10.084097  \n",
       "...          ...  \n",
       "19730  43.096812  \n",
       "19731  49.282940  \n",
       "19732  29.199117  \n",
       "19733   6.322784  \n",
       "19734  34.118851  \n",
       "\n",
       "[19735 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "url = datapath + filename\n",
    "test = pd.read_csv(url, header=0, sep=sep)\n",
    "print(train.shape, test.shape)\n",
    "test = test.dropna()\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['date'] = pd.to_datetime(train['date'],infer_datetime_format= True)\n",
    "test['date'] = pd.to_datetime(test['date'],infer_datetime_format= True)\n",
    "\n",
    "train.set_index('date')['T_out'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras_model_type = \"time series\" ## always try \"fast\" first, then \"fast1\", \"fast2\", \"auto\" in that order\n",
    "project_name = \"energydata_complete\"\n",
    "model_options = {'features':['Appliances','T_out','RH_1','Visibility'], 'window_length':720, 'sampling_rate':1,'stride':64, 'validation_size':0.2, 'prebuilt-model':'lstm'}\n",
    "keras_options = {\"patience\":10, 'class_weight': True, 'early_stopping': True, \n",
    "                 'lr_scheduler': '', \"optimizer\": 'adam', 'epochs':1, 'batch_size':128}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only CPU found on this device\n",
      "Setting CPU strategy using 1 devices\n",
      "\n",
      "Model and logs being saved in deep_autoviml/time series/model_2022_01_30-18_36_45\n",
      "['Appliances', 'T_out', 'RH_1', 'Visibility'], features will be considered\n",
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 720, 128)          68096     \n",
      "                                                                 \n",
      " leaky_re_lu (LeakyReLU)     (None, 720, 128)          0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 720, 128)          131584    \n",
      "                                                                 \n",
      " leaky_re_lu_1 (LeakyReLU)   (None, 720, 128)          0         \n",
      "                                                                 \n",
      " dropout_13 (Dropout)        (None, 720, 128)          0         \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 64)                49408     \n",
      "                                                                 \n",
      " dropout_14 (Dropout)        (None, 64)                0         \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 249,153\n",
      "Trainable params: 249,153\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "15/15 [==============================] - 89s 4s/step - loss: 0.8724 - acc: 0.0000e+00 - val_loss: 0.9873 - val_acc: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "model, cat_vocab_dict = deepauto.fit(train_datafile, target, keras_model_type=keras_model_type,\n",
    "\t\t keras_options=keras_options, model_options=model_options, \n",
    "\t\tsave_model_flag=False, use_my_model='lstm', verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from deep_autoviml.utilities.utilities import plot_classification_results, print_regression_model_stats\n",
    "from sklearn.metrics import classification_report, confusion_matrix, balanced_accuracy_score\n",
    "#from autoviml.Auto_ViML import plot_regression_scatters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading deep_autoviml model from <keras.engine.sequential.Sequential object at 0x7efd5d481220> folder. This will take time...\n",
      "\n",
      "Using <keras.engine.sequential.Sequential object at 0x7efd5d481220> model provided as input...\n",
      "Time taken to load saved model = 0 seconds\n",
      "[0.566 0.541 0.531 ... 0.539 0.549 0.539]\n",
      "(19735,)\n",
      "Boolean cols=False not converted to strings\n",
      "    Boolean columns successfully converted to Integers\n",
      "        Error in converting Boolean columns to Integers. Continuing...\n",
      "Possible Conflict: Boolean columns in train and test data were passed differently. Check your test data types.\n",
      "    number of steps needed to predict: 1\n",
      "(19015, 1)\n",
      "(19015, 3)\n",
      "(19015, 4)\n",
      "Appliances    19015\n",
      "T_out         19015\n",
      "RH_1          19015\n",
      "Visibility    19015\n",
      "dtype: int64\n",
      "(19015, 4)\n",
      "Converting model predictions into classes or other forms is erroring. Convert it yourself.\n"
     ]
    }
   ],
   "source": [
    "predictions = deepauto.predict(model, project_name, test_dataset=url,\n",
    "                                 keras_model_type=keras_model_type, \n",
    "                                 cat_vocab_dict=cat_vocab_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Appliances</th>\n",
       "      <th>T_out</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Appliances_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
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       "      <td>38.126667</td>\n",
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       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>380</td>\n",
       "      <td>1.900000</td>\n",
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       "      <td>143.588652</td>\n",
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       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>290</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>38.430000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>142.401173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>270</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>38.560000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>140.692141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19730</th>\n",
       "      <td>100</td>\n",
       "      <td>22.733333</td>\n",
       "      <td>46.560000</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>102.557396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19731</th>\n",
       "      <td>90</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>102.916843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19732</th>\n",
       "      <td>270</td>\n",
       "      <td>22.466667</td>\n",
       "      <td>46.596667</td>\n",
       "      <td>25.333333</td>\n",
       "      <td>103.335299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19733</th>\n",
       "      <td>420</td>\n",
       "      <td>22.333333</td>\n",
       "      <td>46.990000</td>\n",
       "      <td>26.166667</td>\n",
       "      <td>103.858916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19734</th>\n",
       "      <td>430</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>46.600000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>104.578281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19015 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Appliances      T_out       RH_1  Visibility  Appliances_pred\n",
       "720            80   1.600000  38.260000   40.000000       143.602037\n",
       "721           150   1.750000  38.126667   40.000000       144.060068\n",
       "722           380   1.900000  38.230000   40.000000       143.588652\n",
       "723           290   2.050000  38.430000   40.000000       142.401173\n",
       "724           270   2.200000  38.560000   40.000000       140.692141\n",
       "...           ...        ...        ...         ...              ...\n",
       "19730         100  22.733333  46.560000   23.666667       102.557396\n",
       "19731          90  22.600000  46.500000   24.500000       102.916843\n",
       "19732         270  22.466667  46.596667   25.333333       103.335299\n",
       "19733         420  22.333333  46.990000   26.166667       103.858916\n",
       "19734         430  22.200000  46.600000   27.000000       104.578281\n",
       "\n",
       "[19015 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions[['Appliances','Appliances_pred']].plot()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
  },
  "kernelspec": {
   "display_name": "Python 3.8.0 64-bit ('DAViML': conda)",
   "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}