docs/source/_notebooks/experiments/dl_cls_exp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\tfcpu27_py39\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "D:\\C\\Anaconda3\\envs\\tfcpu27_py39\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "D:\\C\\Anaconda3\\envs\\tfcpu27_py39\\lib\\site-packages\\numpy\\.libs\\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/AtrCheema/ai4water_examples/blob/master/docs/source/_notebooks/experiments/dl_cls_exp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "\n",
    "# Neural Networks for classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\C\\Anaconda3\\envs\\tfcpu27_py39\\lib\\site-packages\\sklearn\\experimental\\enable_hist_gradient_boosting.py:16: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from ai4water.hyperopt import Categorical\n",
    "from ai4water.datasets import MtropicsLaos\n",
    "from ai4water.experiments import DLClassificationExperiments\n",
    "from ai4water.utils.utils import get_version_info\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python 3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:20:16) [MSC v.1916 64 bit (AMD64)]\n",
      "os nt\n",
      "ai4water 1.06\n",
      "lightgbm 3.3.1\n",
      "tcn 3.4.0\n",
      "catboost 0.26\n",
      "xgboost 1.5.0\n",
      "easy_mpl 0.21.3\n",
      "SeqMetrics 1.3.3\n",
      "tensorflow 2.7.0\n",
      "keras.api._v2.keras 2.7.0\n",
      "numpy 1.21.0\n",
      "pandas 1.3.4\n",
      "matplotlib 3.4.3\n",
      "h5py 3.5.0\n",
      "sklearn 1.0.1\n",
      "shapefile 2.3.0\n",
      "fiona 1.8.22\n",
      "xarray 0.20.1\n",
      "netCDF4 1.5.7\n",
      "optuna 2.10.1\n",
      "skopt 0.9.0\n",
      "hyperopt 0.2.7\n",
      "plotly 5.3.1\n",
      "lime NotDefined\n",
      "seaborn 0.11.2\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for lib, ver in get_version_info().items():\n",
    "    print(lib, ver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Not downloading the data since the directory \n",
      "    F:\\data\\MtropicsLaos already exists.\n",
      "    Use overwrite=True to remove previously saved files and download again\n"
     ]
    }
   ],
   "source": [
    "dataset = MtropicsLaos(save_as_nc=True,  # if set to True, then netcdf must be installed\n",
    "                       convert_to_csv=False,\n",
    "                       path=\"F:\\\\data\\\\MtropicsLaos\",\n",
    "                      )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4071, 9)\n"
     ]
    }
   ],
   "source": [
    "lookback = 20\n",
    "data =    dataset.make_classification(lookback_steps=lookback)\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "air_temp           0\n",
      "rel_hum            0\n",
      "wind_speed         0\n",
      "sol_rad            0\n",
      "water_level        0\n",
      "pcp                0\n",
      "susp_pm            0\n",
      "Ecoli_source       0\n",
      "Ecoli_mpn100    3813\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['air_temp',\n",
       " 'rel_hum',\n",
       " 'wind_speed',\n",
       " 'sol_rad',\n",
       " 'water_level',\n",
       " 'pcp',\n",
       " 'susp_pm',\n",
       " 'Ecoli_source']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = data.columns.tolist()[0:-1]\n",
    "inputs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Ecoli_mpn100']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs = data.columns.tolist()[-1:]\n",
    "outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      air_temp    rel_hum  wind_speed  sol_rad  water_level  \\\n",
      "2011-05-25 14:42:00  22.537861  96.169425         4.9    187.4        133.0   \n",
      "2011-05-25 14:48:00  22.537998  96.169655         4.6    177.6        133.0   \n",
      "2011-05-25 14:54:00  22.538134  96.169885         4.3    167.8        133.0   \n",
      "2011-05-25 15:00:00  22.538271  96.170115         4.0    158.0        133.0   \n",
      "2011-05-25 15:06:00  22.538407  96.170345         3.7    150.0        133.0   \n",
      "\n",
      "                     pcp  susp_pm  Ecoli_source  Ecoli_mpn100  \n",
      "2011-05-25 14:42:00  0.0      0.9  2.868160e+15           NaN  \n",
      "2011-05-25 14:48:00  0.0      0.9  2.868160e+15           NaN  \n",
      "2011-05-25 14:54:00  0.0      0.9  2.868160e+15           NaN  \n",
      "2011-05-25 15:00:00  0.0      0.9  2.868160e+15           NaN  \n",
      "2011-05-25 15:06:00  0.0      0.9  2.868160e+15           NaN  \n"
     ]
    }
   ],
   "source": [
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = DLClassificationExperiments(\n",
    "    input_features=inputs,\n",
    "    output_features=outputs,\n",
    "    batch_size=16,\n",
    "    epochs=500,\n",
    "    ts_args={\"lookback\": lookback},\n",
    "    save=False\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Training *****\n",
      "input_x shape:  (144, 20, 8)\n",
      "target shape:  (144, 1)\n",
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Validation *****\n",
      "input_x shape:  (36, 20, 8)\n",
      "target shape:  (36, 1)\n",
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Test *****\n",
      "input_x shape:  (78, 20, 8)\n",
      "target shape:  (78, 1)\n",
      "running  MLP model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "            argument test is deprecated and will be removed in future. Please \n",
      "            use 'predict_on_test_data' method instead.\n",
      "invalid value encountered in true_divide\n",
      "invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  CNN model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "            argument test is deprecated and will be removed in future. Please \n",
      "            use 'predict_on_test_data' method instead.\n",
      "invalid value encountered in true_divide\n",
      "invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  LSTM model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "            argument test is deprecated and will be removed in future. Please \n",
      "            use 'predict_on_test_data' method instead.\n",
      "invalid value encountered in true_divide\n",
      "invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  TFT model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "            argument test is deprecated and will be removed in future. Please \n",
      "            use 'predict_on_test_data' method instead.\n"
     ]
    }
   ],
   "source": [
    "exp.fit(data=data,\n",
    "        include=[\"MLP\", \"CNN\", \"LSTM\", \"TFT\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Training *****\n",
      "input_x shape:  (144, 20, 8)\n",
      "target shape:  (144, 1)\n",
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Validation *****\n",
      "input_x shape:  (36, 20, 8)\n",
      "target shape:  (36, 1)\n",
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Test *****\n",
      "input_x shape:  (78, 20, 8)\n",
      "target shape:  (78, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "            argument test is deprecated and will be removed in future. Please \n",
      "            use 'predict_on_test_data' method instead.\n",
      "invalid value encountered in true_divide\n",
      "invalid value encountered in true_divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = exp.compare_errors('accuracy', data=data, colors=['salmon', 'cadetblue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         train      test\n",
      "TFT   0.534722  0.641026\n",
      "LSTM  0.611111  0.525641\n",
      "MLP   0.388889  0.474359\n",
      "CNN   0.388889  0.474359\n"
     ]
    }
   ],
   "source": [
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "nbsphinx-thumbnail": {
     "tooltip": "Comparing Deep Learning Architectures for Classification"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Training *****\n",
      "input_x shape:  (144, 20, 8)\n",
      "target shape:  (144, 1)\n",
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Validation *****\n",
      "input_x shape:  (36, 20, 8)\n",
      "target shape:  (36, 1)\n",
      "\n",
      "********** Removing Examples with nan in labels  **********\n",
      "\n",
      "***** Test *****\n",
      "input_x shape:  (78, 20, 8)\n",
      "target shape:  (78, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1_score = exp.compare_errors('f1_score', data=data, cmaps=[\"Spectral\", \"Spectral\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         train      test\n",
      "TFT   0.508933  0.603486\n",
      "LSTM  0.379310  0.344538\n",
      "MLP   0.280000  0.321739\n",
      "CNN   0.280000  0.321739\n"
     ]
    }
   ],
   "source": [
    "print(f1_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = exp.loss_comparison()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}