MODIS time series analysis using  LSTM using many locations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODIS time series analysis using  LSTM at many locations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "**Objectives:** To demonstrate the LSTM's application for modelling time series analysis using MODIS data such as NDVI and EVI as an example.\n",
    "\n",
    "\n",
    "## Overview\n",
    "\n",
    "## 1 download the MODIS data \n",
    "This step have been finished in R code using MODISTools packages. We get three variables for one region from 2000 to 2020: land cover, NDVI, EVI and corresponding quality layers.\n",
    "\n",
    "## 2 build a LSTM model for NDVI and EVI time series analysis\n",
    "\n",
    "### Three major sections:\n",
    "\n",
    "2.1 Use histroty NDVI data in one location to predict further date's NDVI **(regression, 1D)**.\n",
    "\n",
    "2.2 Use histroy NDVI and EVI data to predict quality score **(regression, 2D)**. Quality column in the data will tell you the NDVI and EVI were obtained with a high confidence. Good quality score is [0,1].\n",
    "\n",
    "2.3 Use histroy NDVI and EVI data to determine if the data quality is high (yes) or low (no) **(classification)**. Here, we will first classify the quality column based on score into two classes [yes, no] as y.\n",
    "\n",
    "\n",
    "### Method summary\n",
    "\n",
    "    a: Data exploration\n",
    "\n",
    "    b: Filter the data based on data quality column, assign None to low quality values and fill them with neighbour values\n",
    "\n",
    "    c: Format data into right fromat for LSTM model. This step is critical.\n",
    "\n",
    "    d: Build LSTM model (paramters are not optimzied, mainly for a demo, Bayesian optimization can be used for optimization purpose)\n",
    "\n",
    "    e: Evaluation of model results\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backgroud on LSTM\n",
    "\n",
    "There are multiple sources to understand the LSTM. Here is one good [explination](http://colah.github.io/posts/2015-08-Understanding-LSTMs/). Basically, three used three gates (forget gate, input gate and output gate)to update the cell states. As the link provide a more comprehensive explination, I will not give redudant infomration on LSTM here.\n",
    "\n",
    " \n",
    "Long Short Term Neural Network (LSTM) are a type of recurrent unit that is often used with deep neural networks. For TensorFlow, LSTM can be thought of as a layer type that can be combined with other layer types, such as dense.  \n",
    "\n",
    "LSTM makes use two transfer function types internally.  \n",
    "\n",
    "The first type of transfer function is the sigmoid.  This transfer function type is used form gates inside of the unit.  \n",
    "\n",
    "The second type of transfer function is the hyperbolic tangent (tanh) function.  This function is used to scale the output of the LSTM, similarly to how other transfer functions have been used in this course. \n",
    "\n",
    "### Why LSTM?\n",
    "\n",
    "RNN cannot handle the long term temporal dependencies as it suffer from vanishing and exploding gradient problems.\n",
    "\n",
    "With LSTM, you can have more control of flow and mixing of inputs. It has both cell state and hidden states. RNN only has hidden state.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Build a LSTM model for modelling NDVI and EVI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "#import datetime\n",
    "#import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import sequence\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Embedding\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.preprocessing import sequence\n",
    "\n",
    "from tensorflow.keras.datasets import imdb\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.read_csv(\"./data/new_ndvi_evi_qa_lc_nl_gelderland_loobos_2000_2020.csv\")\n",
    "dat.sort_values(['time','pixel'], inplace=True)  ##This is important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat2 = dat.copy() ##mainly for quality prediction 2.2 and 2.3\n",
    "dat2.set_index(\"time\",inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Predict further NDVI based on histrory data LSTM\n",
    "\n",
    "### Data overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 502550 entries, 10 to 74267\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   pixel       502550 non-null  int64  \n",
      " 1   year        502550 non-null  int64  \n",
      " 2   ndvi        502550 non-null  float64\n",
      " 3   evi         502550 non-null  float64\n",
      " 4   qa          502550 non-null  float64\n",
      " 5   lai         502550 non-null  float64\n",
      " 6   time        502550 non-null  object \n",
      " 7   land_cover  476100 non-null  float64\n",
      " 8   lc_time     476100 non-null  object \n",
      "dtypes: float64(5), int64(2), object(2)\n",
      "memory usage: 38.3+ MB\n"
     ]
    }
   ],
   "source": [
    "dat.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel</th>\n",
       "      <th>year</th>\n",
       "      <th>ndvi</th>\n",
       "      <th>evi</th>\n",
       "      <th>qa</th>\n",
       "      <th>lai</th>\n",
       "      <th>time</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>lc_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.580022</td>\n",
       "      <td>0.229722</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050433</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114507</th>\n",
       "      <td>2</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.413389</td>\n",
       "      <td>0.162797</td>\n",
       "      <td>0.545617</td>\n",
       "      <td>0.045434</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163005</th>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.376526</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.045796</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211510</th>\n",
       "      <td>4</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.532181</td>\n",
       "      <td>0.207942</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051338</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260025</th>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.719637</td>\n",
       "      <td>0.280162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055572</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pixel  year      ndvi       evi        qa       lai        time  \\\n",
       "10          1  2001  0.580022  0.229722  1.000000  0.050433  2001-01-01   \n",
       "114507      2  2001  0.413389  0.162797  0.545617  0.045434  2001-01-01   \n",
       "163005      3  2001  0.376526  0.148463  0.000000  0.045796  2001-01-01   \n",
       "211510      4  2001  0.532181  0.207942  0.000000  0.051338  2001-01-01   \n",
       "260025      5  2001  0.719637  0.280162  0.000000  0.055572  2001-01-01   \n",
       "\n",
       "        land_cover     lc_time  \n",
       "10             7.0  2001-01-01  \n",
       "114507         8.0  2001-01-01  \n",
       "163005         8.0  2001-01-01  \n",
       "211510         5.0  2001-01-01  \n",
       "260025         4.0  2001-01-01  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1023    437\n",
       "333     437\n",
       "76      437\n",
       "332     437\n",
       "588     437\n",
       "       ... \n",
       "670     437\n",
       "414     437\n",
       "158     437\n",
       "925     437\n",
       "256     437\n",
       "Name: pixel, Length: 1150, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''here each pixel represet one location and reprsent 250*250 region, \n",
    "we can see there are 437 time observations for each pixel'''\n",
    "\n",
    "dat['pixel'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data QA/AC\n",
    "\n",
    "1 As we noticed, there is one layer called QA which include the quality information of the pixels at different time. we will first use them to conduct filtering.\n",
    "\n",
    "2 if we have land cover type data, we can used that as a filter, we need to chose land cover type for vegetation from 1 to 10 and 12. You can refer to [here](https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf) for the land cover classification information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "##filter based on quality information, it should be within 0 and 1\n",
    "dat['qa'] = dat['qa'].apply(lambda x: x if x>=0 and x<=1 else None)\n",
    "dat2['qa'] = dat2['qa'].apply(lambda x: x if x>=0 and x<=1 else None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat['land_cover'] = dat['land_cover'].apply(lambda x: x if x>=1 and x<=10 or x ==12 else None )\n",
    "dat2['land_cover'] = dat2['land_cover'].apply(lambda x: x if x>=1 and x<=10 or x ==12 else None )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "437"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 1 get year, month and day\n",
    "dat['time'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat['date'] = pd.to_datetime(dat['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>ndvi</th>\n",
       "      <th>evi</th>\n",
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       "      <th>lai</th>\n",
       "      <th>time</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>lc_time</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.580022</td>\n",
       "      <td>0.229722</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050433</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>2001-01-01</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>2001-01-01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>2001-01-01</td>\n",
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       "    <tr>\n",
       "      <th>211510</th>\n",
       "      <td>4</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.532181</td>\n",
       "      <td>0.207942</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051338</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260025</th>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.719637</td>\n",
       "      <td>0.280162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055572</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "        pixel  year      ndvi       evi        qa       lai        time  \\\n",
       "10          1  2001  0.580022  0.229722  1.000000  0.050433  2001-01-01   \n",
       "114507      2  2001  0.413389  0.162797  0.545617  0.045434  2001-01-01   \n",
       "163005      3  2001  0.376526  0.148463  0.000000  0.045796  2001-01-01   \n",
       "211510      4  2001  0.532181  0.207942  0.000000  0.051338  2001-01-01   \n",
       "260025      5  2001  0.719637  0.280162  0.000000  0.055572  2001-01-01   \n",
       "\n",
       "        land_cover     lc_time       date  \n",
       "10             7.0  2001-01-01 2001-01-01  \n",
       "114507         8.0  2001-01-01 2001-01-01  \n",
       "163005         8.0  2001-01-01 2001-01-01  \n",
       "211510         5.0  2001-01-01 2001-01-01  \n",
       "260025         4.0  2001-01-01 2001-01-01  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat.set_index('date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "##scale the ndvi and evi\n",
    "#df['ndvi'] = df['ndvi']/10000\n",
    "#df['evi'] = df['evi']/10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2001-01-01</th>\n",
       "      <td>1</td>\n",
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       "      <th>2001-01-01</th>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.376526</td>\n",
       "      <td>0.148463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045796</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>4</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.532181</td>\n",
       "      <td>0.207942</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051338</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.719637</td>\n",
       "      <td>0.280162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055572</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pixel  year      ndvi       evi        qa       lai        time  \\\n",
       "date                                                                          \n",
       "2001-01-01      1  2001  0.580022  0.229722  1.000000  0.050433  2001-01-01   \n",
       "2001-01-01      2  2001  0.413389  0.162797  0.545617  0.045434  2001-01-01   \n",
       "2001-01-01      3  2001  0.376526  0.148463  0.000000  0.045796  2001-01-01   \n",
       "2001-01-01      4  2001  0.532181  0.207942  0.000000  0.051338  2001-01-01   \n",
       "2001-01-01      5  2001  0.719637  0.280162  0.000000  0.055572  2001-01-01   \n",
       "\n",
       "            land_cover     lc_time  \n",
       "date                                \n",
       "2001-01-01         7.0  2001-01-01  \n",
       "2001-01-01         8.0  2001-01-01  \n",
       "2001-01-01         8.0  2001-01-01  \n",
       "2001-01-01         5.0  2001-01-01  \n",
       "2001-01-01         4.0  2001-01-01  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_pixel = dat.groupby('pixel').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>ndvi</th>\n",
       "      <th>evi</th>\n",
       "      <th>qa</th>\n",
       "      <th>lai</th>\n",
       "      <th>time</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>lc_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pixel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>387</td>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>380</td>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>387</td>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>380</td>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>387</td>\n",
       "      <td>437</td>\n",
       "      <td>437</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  ndvi  evi   qa  lai  time  land_cover  lc_time\n",
       "pixel                                                      \n",
       "1       437   437  437  387  437   437         414      414\n",
       "2       437   437  437  380  437   437         414      414\n",
       "3       437   437  437  387  437   437         414      414\n",
       "4       437   437  437  380  437   437         414      414\n",
       "5       437   437  437  387  437   437         414      414"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_pixel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sel_pixel = list(dd[dd['qa'] == dd['qa'].max()].index)\n",
    "\n",
    "sel_pixels = list(sum_pixel[sum_pixel['qa'] > sum_pixel['qa'].mean()+10].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "209"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sel_pixels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the Data at one pixel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "##one location or one pixel\n",
    "sub_df = dat[dat['pixel']== sel_pixels[0]]\n",
    "sub_df1 = sub_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x263d29505c8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sub_df[['ndvi','evi']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Low quality values\n",
    "\n",
    "We can see that there are a lot of abnormal values or outliers.\n",
    "Thus, we need to filter them ased on the QA column. \n",
    "\n",
    "There are two strategies to process these low quality or abnormal values\n",
    "\n",
    "1 to remove them\n",
    "\n",
    "2 to fill them with mean of neaby locations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Low quality and abnormla values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## deal with low quality values, to keep more data for following modelling, used second strategry\n",
    "def lw_process(df,cols,qual_col = \"qa\",strategies = \"fill\"):\n",
    "    for col in cols:\n",
    "        if strategies != \"fill\":\n",
    "            df[col] = df[df[qual_col].notnull()][col]\n",
    "        else:\n",
    "            df.loc[df[qual_col].isnull(),col] = None\n",
    "            ##replace with neighbours\n",
    "            df[col] = (df[col].fillna(method = 'ffill') + df[col].fillna(method = 'bfill'))/2\n",
    "    return df        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = lw_process(dat,cols = ['ndvi','evi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel</th>\n",
       "      <th>year</th>\n",
       "      <th>ndvi</th>\n",
       "      <th>evi</th>\n",
       "      <th>qa</th>\n",
       "      <th>lai</th>\n",
       "      <th>time</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>lc_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.580022</td>\n",
       "      <td>0.229722</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050433</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>2</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.413389</td>\n",
       "      <td>0.162797</td>\n",
       "      <td>0.545617</td>\n",
       "      <td>0.045434</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.376526</td>\n",
       "      <td>0.148463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045796</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>4</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.532181</td>\n",
       "      <td>0.207942</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051338</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.719637</td>\n",
       "      <td>0.280162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055572</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pixel  year      ndvi       evi        qa       lai        time  \\\n",
       "date                                                                          \n",
       "2001-01-01      1  2001  0.580022  0.229722  1.000000  0.050433  2001-01-01   \n",
       "2001-01-01      2  2001  0.413389  0.162797  0.545617  0.045434  2001-01-01   \n",
       "2001-01-01      3  2001  0.376526  0.148463  0.000000  0.045796  2001-01-01   \n",
       "2001-01-01      4  2001  0.532181  0.207942  0.000000  0.051338  2001-01-01   \n",
       "2001-01-01      5  2001  0.719637  0.280162  0.000000  0.055572  2001-01-01   \n",
       "\n",
       "            land_cover     lc_time  \n",
       "date                                \n",
       "2001-01-01         7.0  2001-01-01  \n",
       "2001-01-01         8.0  2001-01-01  \n",
       "2001-01-01         8.0  2001-01-01  \n",
       "2001-01-01         5.0  2001-01-01  \n",
       "2001-01-01         4.0  2001-01-01  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2001-01-01</th>\n",
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       "      <td>2001</td>\n",
       "      <td>0.711466</td>\n",
       "      <td>0.275823</td>\n",
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       "      <td>2001-01-01</td>\n",
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       "      <th>2001-01-01</th>\n",
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       "      <td>2001</td>\n",
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       "      <td>0.240521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.074681</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2001-01-01</td>\n",
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       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>131</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.587112</td>\n",
       "      <td>0.232783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.084101</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2001-01-01</td>\n",
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       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>450</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.552984</td>\n",
       "      <td>0.289580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.081269</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2001-01-01</td>\n",
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       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>451</td>\n",
       "      <td>2001</td>\n",
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       "      <td>0.289580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.077006</td>\n",
       "      <td>2001-01-01</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-19</th>\n",
       "      <td>1020</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.510744</td>\n",
       "      <td>0.170990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057612</td>\n",
       "      <td>2019-12-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-19</th>\n",
       "      <td>1050</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.573083</td>\n",
       "      <td>0.189842</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.063258</td>\n",
       "      <td>2019-12-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-19</th>\n",
       "      <td>1051</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.573083</td>\n",
       "      <td>0.189842</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065435</td>\n",
       "      <td>2019-12-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-19</th>\n",
       "      <td>1052</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.573083</td>\n",
       "      <td>0.189842</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066965</td>\n",
       "      <td>2019-12-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.066847</td>\n",
       "      <td>2019-12-19</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "            pixel  year      ndvi       evi  qa       lai        time  \\\n",
       "date                                                                    \n",
       "2001-01-01     65  2001  0.711466  0.275823 NaN  0.067604  2001-01-01   \n",
       "2001-01-01     98  2001  0.618754  0.240521 NaN  0.074681  2001-01-01   \n",
       "2001-01-01    131  2001  0.587112  0.232783 NaN  0.084101  2001-01-01   \n",
       "2001-01-01    450  2001  0.552984  0.289580 NaN  0.081269  2001-01-01   \n",
       "2001-01-01    451  2001  0.552984  0.289580 NaN  0.077006  2001-01-01   \n",
       "...           ...   ...       ...       ...  ..       ...         ...   \n",
       "2019-12-19   1020  2019  0.510744  0.170990 NaN  0.057612  2019-12-19   \n",
       "2019-12-19   1050  2019  0.573083  0.189842 NaN  0.063258  2019-12-19   \n",
       "2019-12-19   1051  2019  0.573083  0.189842 NaN  0.065435  2019-12-19   \n",
       "2019-12-19   1052  2019  0.573083  0.189842 NaN  0.066965  2019-12-19   \n",
       "2019-12-19   1053  2019  0.573083  0.189842 NaN  0.066847  2019-12-19   \n",
       "\n",
       "            land_cover     lc_time  \n",
       "date                                \n",
       "2001-01-01         7.0  2001-01-01  \n",
       "2001-01-01         9.0  2001-01-01  \n",
       "2001-01-01         9.0  2001-01-01  \n",
       "2001-01-01         9.0  2001-01-01  \n",
       "2001-01-01         9.0  2001-01-01  \n",
       "...                ...         ...  \n",
       "2019-12-19         NaN         NaN  \n",
       "2019-12-19         NaN         NaN  \n",
       "2019-12-19         NaN         NaN  \n",
       "2019-12-19         NaN         NaN  \n",
       "2019-12-19         NaN         NaN  \n",
       "\n",
       "[72958 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##check the results after preprocessing to fill the low quality values\n",
    "df1[df1['qa'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2001-01-01</th>\n",
       "      <td>450</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.561985</td>\n",
       "      <td>0.293409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.081269</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2001-01-01</td>\n",
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       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>451</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.543720</td>\n",
       "      <td>0.254845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.077006</td>\n",
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       "      <td>2001-01-01</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-19</th>\n",
       "      <td>1020</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.181339</td>\n",
       "      <td>0.149335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.057612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-19</th>\n",
       "      <td>1050</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.473344</td>\n",
       "      <td>0.257839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.063258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-19</th>\n",
       "      <td>1051</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.407118</td>\n",
       "      <td>0.226752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-19</th>\n",
       "      <td>1052</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.364012</td>\n",
       "      <td>0.219725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-19</th>\n",
       "      <td>1053</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.381405</td>\n",
       "      <td>0.227696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066847</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>72958 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            pixel  year      ndvi       evi  qa       lai  land_cover  \\\n",
       "time                                                                    \n",
       "2001-01-01     65  2001  0.690066  0.259516 NaN  0.067604         7.0   \n",
       "2001-01-01     98  2001  0.669664  0.315819 NaN  0.074681         9.0   \n",
       "2001-01-01    131  2001  0.619128  0.283310 NaN  0.084101         9.0   \n",
       "2001-01-01    450  2001  0.561985  0.293409 NaN  0.081269         9.0   \n",
       "2001-01-01    451  2001  0.543720  0.254845 NaN  0.077006         9.0   \n",
       "...           ...   ...       ...       ...  ..       ...         ...   \n",
       "2019-12-19   1020  2019  0.181339  0.149335 NaN  0.057612         NaN   \n",
       "2019-12-19   1050  2019  0.473344  0.257839 NaN  0.063258         NaN   \n",
       "2019-12-19   1051  2019  0.407118  0.226752 NaN  0.065435         NaN   \n",
       "2019-12-19   1052  2019  0.364012  0.219725 NaN  0.066965         NaN   \n",
       "2019-12-19   1053  2019  0.381405  0.227696 NaN  0.066847         NaN   \n",
       "\n",
       "               lc_time  \n",
       "time                    \n",
       "2001-01-01  2001-01-01  \n",
       "2001-01-01  2001-01-01  \n",
       "2001-01-01  2001-01-01  \n",
       "2001-01-01  2001-01-01  \n",
       "2001-01-01  2001-01-01  \n",
       "...                ...  \n",
       "2019-12-19         NaN  \n",
       "2019-12-19         NaN  \n",
       "2019-12-19         NaN  \n",
       "2019-12-19         NaN  \n",
       "2019-12-19         NaN  \n",
       "\n",
       "[72958 rows x 8 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## original data \n",
    "dat2[dat2['qa'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_ndf1 = dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2001-01-01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.376526</td>\n",
       "      <td>0.148463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045796</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>4</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.532181</td>\n",
       "      <td>0.207942</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051338</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.719637</td>\n",
       "      <td>0.280162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055572</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pixel  year      ndvi       evi        qa       lai        time  \\\n",
       "date                                                                          \n",
       "2001-01-01      1  2001  0.580022  0.229722  1.000000  0.050433  2001-01-01   \n",
       "2001-01-01      2  2001  0.413389  0.162797  0.545617  0.045434  2001-01-01   \n",
       "2001-01-01      3  2001  0.376526  0.148463  0.000000  0.045796  2001-01-01   \n",
       "2001-01-01      4  2001  0.532181  0.207942  0.000000  0.051338  2001-01-01   \n",
       "2001-01-01      5  2001  0.719637  0.280162  0.000000  0.055572  2001-01-01   \n",
       "\n",
       "            land_cover     lc_time  \n",
       "date                                \n",
       "2001-01-01         7.0  2001-01-01  \n",
       "2001-01-01         8.0  2001-01-01  \n",
       "2001-01-01         8.0  2001-01-01  \n",
       "2001-01-01         5.0  2001-01-01  \n",
       "2001-01-01         4.0  2001-01-01  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_ndf1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM\n",
    "\n",
    "### Data formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "##When our feature is one dimension\n",
    "def to_sequences_1D (seq_size,obs,step=1):\n",
    "    x = []\n",
    "    y = []\n",
    "\n",
    "    for i in range(0,len(obs)-SEQUENCE_SIZE,step):\n",
    "        '''\n",
    "        seq_size: the number of observations will be used for training\n",
    "        step: the change of observations to form a new training data\n",
    "        obs: the whole dataset\n",
    "        '''\n",
    "        #print(i)\n",
    "        window = obs[i:(i+SEQUENCE_SIZE)]\n",
    "        after_window = obs[i+SEQUENCE_SIZE]\n",
    "        window = [[x] for x in window]\n",
    "        #print(\"{} - {}\".format(window,after_window))\n",
    "        x.append(window)\n",
    "        y.append(after_window)\n",
    "        \n",
    "    return np.array(x),np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "## the follwoing function is more general which can deal with multiple dimensions\n",
    "\n",
    "def to_sequences_MD (seq_size,obs,xcols,ycol,step=1):\n",
    "    x = []\n",
    "    y = []\n",
    "    y_time = []\n",
    "    for i in range(0,len(obs)-SEQUENCE_SIZE,step):\n",
    "        '''\n",
    "        seq_size: the number of observations will be used for training\n",
    "        step: the change of observations to form a new training data\n",
    "        obs: the whole dataset\n",
    "        '''\n",
    "        #print(i)\n",
    "        window = obs[i:(i+SEQUENCE_SIZE)][xcols]\n",
    "        after_window = obs[(i+SEQUENCE_SIZE -1):(i+SEQUENCE_SIZE)][ycol]\n",
    "        #window = [[x] for x in window]\n",
    "        time_index = after_window.index.values[0]\n",
    "        win_values = window.values\n",
    "        #print(\"{} - {}\".format(window,after_window))\n",
    "        x.append(win_values)\n",
    "        y.append(after_window.values)\n",
    "        y_time.append(time_index)\n",
    "        \n",
    "    return np.array(x),np.array(y),y_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "209"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df1[df1['pixel'].isin(sel_pixels) ]\n",
    "df['pixel'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "## our target is ndvi, the ndvi and lai will used for external variables\n",
    "\n",
    "##about 20 years data and 0.8 for training, thus set date 2016\n",
    "\n",
    "SEQUENCE_SIZE = 10\n",
    "sel_date= \"2016-01-01\"\n",
    "val_pixel = df['pixel'].nunique() - 9 ##hold out data threshold threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_thres = sel_pixels[val_pixel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "xcol = ['evi']\n",
    "ycol = ['evi']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train and test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x training set: (67335, 10, 1)\n",
      "Shape of y training set: (67335, 1)\n",
      "Shape of x hold out set: (2680, 10, 1)\n",
      "Shape of y hold out set: (2680, 1)\n"
     ]
    }
   ],
   "source": [
    "df_x_train =[]\n",
    "df_x_test =[]\n",
    "df_y_train =[]\n",
    "df_y_test =[]\n",
    "\n",
    "val_x_train =[]\n",
    "val_x_test =[]\n",
    "val_y_train =[]\n",
    "val_y_test =[]\n",
    "\n",
    "for pixel in sel_pixels:\n",
    "    \n",
    "    sub_df = df[df['pixel'] == pixel]\n",
    "    sub_train = sub_df[sub_df.index < sel_date]\n",
    "    sub_test = sub_df[sub_df.index >= sel_date ]\n",
    "    \n",
    "    x_train,y_train,train_time = to_sequences_MD(SEQUENCE_SIZE,sub_train, xcols =xcol,ycol=ycol)\n",
    "    x_test,y_test,test_time = to_sequences_MD(SEQUENCE_SIZE,sub_test, xcols =xcol,ycol=ycol)\n",
    "    \n",
    "    if pixel <= pixel_thres:\n",
    "        \n",
    "        df_x_train.append(x_train)\n",
    "        df_x_test.append(x_test)\n",
    "    \n",
    "        df_y_train.append(y_train)\n",
    "        df_y_test.append(y_test)\n",
    "        \n",
    "    else:\n",
    "        val_x_train.append(x_train)\n",
    "        val_x_test.append(x_test)\n",
    "    \n",
    "        val_y_train.append(y_train)\n",
    "        val_y_test.append(y_test)\n",
    "    \n",
    "## convert to np array\n",
    "\n",
    "df_x_train1 = np.asarray(df_x_train).reshape(-1,SEQUENCE_SIZE,len(xcol))\n",
    "df_x_test1 = np.asarray(df_x_test).reshape(-1,SEQUENCE_SIZE,len(xcol))\n",
    "\n",
    "df_y_train1 = np.asarray(df_y_train).reshape(-1,1)\n",
    "df_y_test1 = np.asarray(df_y_test).reshape(-1,1)\n",
    "\n",
    "##hold out data\n",
    "val_x_train1 = np.asarray(val_x_train).reshape(-1,SEQUENCE_SIZE,len(xcol))\n",
    "val_x_test1 = np.asarray(val_x_test).reshape(-1,SEQUENCE_SIZE,len(xcol))\n",
    "\n",
    "val_y_train1 = np.asarray(val_y_train).reshape(-1,1)\n",
    "val_y_test1 = np.asarray(val_y_test).reshape(-1,1)\n",
    "\n",
    "\n",
    "\n",
    "print(\"Shape of x training set: {}\".format(df_x_train1.shape))\n",
    "print(\"Shape of y training set: {}\".format(df_y_train1.shape))\n",
    "\n",
    "\n",
    "print(\"Shape of x hold out set: {}\".format(val_x_train1.shape))\n",
    "print(\"Shape of y hold out set: {}\".format(val_y_train1.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model building - 1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n",
      "Train on 67335 samples, validate on 16482 samples\n",
      "Epoch 1/30\n",
      "67335/67335 - 39s - loss: 0.0070 - val_loss: 0.0111\n",
      "Epoch 2/30\n",
      "67335/67335 - 34s - loss: 0.0040 - val_loss: 0.0128\n",
      "Epoch 3/30\n",
      "67335/67335 - 34s - loss: 0.0036 - val_loss: 0.0133\n",
      "Epoch 4/30\n",
      "67335/67335 - 34s - loss: 0.0033 - val_loss: 0.0154\n",
      "Epoch 5/30\n",
      "67335/67335 - 34s - loss: 0.0028 - val_loss: 0.0115\n",
      "Epoch 6/30\n",
      "67335/67335 - 34s - loss: 0.0028 - val_loss: 0.0120\n",
      "Epoch 7/30\n",
      "67335/67335 - 34s - loss: 0.0027 - val_loss: 0.0135\n",
      "Epoch 8/30\n",
      "67335/67335 - 34s - loss: 0.0026 - val_loss: 0.0140\n",
      "Epoch 9/30\n",
      "67335/67335 - 35s - loss: 0.0026 - val_loss: 0.0132\n",
      "Epoch 10/30\n",
      "67335/67335 - 34s - loss: 0.0025 - val_loss: 0.0177\n",
      "Epoch 11/30\n",
      "67335/67335 - 35s - loss: 0.0025 - val_loss: 0.0135\n",
      "Epoch 12/30\n",
      "67335/67335 - 34s - loss: 0.0024 - val_loss: 0.0166\n",
      "Epoch 13/30\n",
      "67335/67335 - 34s - loss: 0.0025 - val_loss: 0.0130\n",
      "Epoch 14/30\n",
      "67335/67335 - 34s - loss: 0.0024 - val_loss: 0.0148\n",
      "Epoch 15/30\n",
      "67335/67335 - 34s - loss: 0.0024 - val_loss: 0.0123\n",
      "Epoch 16/30\n",
      "67335/67335 - 34s - loss: 0.0023 - val_loss: 0.0108\n",
      "Epoch 17/30\n",
      "67335/67335 - 34s - loss: 0.0023 - val_loss: 0.0114\n",
      "Epoch 18/30\n",
      "67335/67335 - 34s - loss: 0.0023 - val_loss: 0.0131\n",
      "Epoch 19/30\n",
      "67335/67335 - 34s - loss: 0.0024 - val_loss: 0.0140\n",
      "Epoch 20/30\n",
      "67335/67335 - 34s - loss: 0.0023 - val_loss: 0.0139\n",
      "Epoch 21/30\n",
      "67335/67335 - 34s - loss: 0.0023 - val_loss: 0.0115\n",
      "Epoch 22/30\n",
      "67335/67335 - 34s - loss: 0.0023 - val_loss: 0.0121\n",
      "Epoch 23/30\n",
      "67335/67335 - 34s - loss: 0.0022 - val_loss: 0.0122\n",
      "Epoch 24/30\n",
      "67335/67335 - 34s - loss: 0.0023 - val_loss: 0.0118\n",
      "Epoch 25/30\n",
      "67335/67335 - 34s - loss: 0.0022 - val_loss: 0.0114\n",
      "Epoch 26/30\n",
      "67335/67335 - 34s - loss: 0.0022 - val_loss: 0.0126\n",
      "Epoch 27/30\n",
      "67335/67335 - 34s - loss: 0.0022 - val_loss: 0.0116\n",
      "Epoch 28/30\n",
      "67335/67335 - 34s - loss: 0.0022 - val_loss: 0.0119\n",
      "Epoch 29/30\n",
      "67335/67335 - 34s - loss: 0.0022 - val_loss: 0.0130\n",
      "Epoch 30/30\n",
      "67335/67335 - 34s - loss: 0.0022 - val_loss: 0.0123\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x263fa72cac8>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(128, dropout=0.3, return_sequences= True,input_shape=(None, 1)))\n",
    "model.add(LSTM(32, return_sequences=False, dropout=0.3))\n",
    "model.add(Dense(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=15, verbose=1, mode='auto', restore_best_weights=True)\n",
    "print('Train...')\n",
    "\n",
    "model.fit(df_x_train1,df_y_train1,validation_data=(df_x_test1,df_y_test1),\n",
    "          callbacks=[monitor],\n",
    "          verbose=2,epochs=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score (RMSE): 0.13040590549737704\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "pred = model.predict(val_x_train1)\n",
    "score = np.sqrt(metrics.mean_squared_error(pred,val_y_train1))\n",
    "print(\"Score (RMSE): {}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have 8 pixels in hold out data. each one will have 335 time interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({\"pred\":pred.ravel(),\n",
    "              \"test\":val_y_train1.ravel()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "output1 = output.loc[:335,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x263851c6048>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "output1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df1 = df[df['pixel'] == pixel]\n",
    "sub_train1 = sub_df1[sub_df1.index < sel_date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(345, 9)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_train1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Predicition on data quality using NDVI and EVI - regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tank\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "##Based on previous exploration, we know that pixel = 464 is best location for us to explore\n",
    "\n",
    "df2 = dat2[dat2['pixel'] == sel_pixel[0]]\n",
    "df2.sort_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel</th>\n",
       "      <th>year</th>\n",
       "      <th>ndvi</th>\n",
       "      <th>evi</th>\n",
       "      <th>qa</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>lc_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.510641</td>\n",
       "      <td>0.287295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-17</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.457637</td>\n",
       "      <td>0.162410</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-02</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.553835</td>\n",
       "      <td>0.315182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-18</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.079125</td>\n",
       "      <td>0.101180</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-03-06</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.522178</td>\n",
       "      <td>0.280573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pixel  year      ndvi       evi   qa  land_cover     lc_time\n",
       "time                                                                    \n",
       "2001-01-01    464  2001  0.510641  0.287295  1.0         8.0  2001-01-01\n",
       "2001-01-17    464  2001  0.457637  0.162410  1.0         8.0  2001-01-01\n",
       "2001-02-02    464  2001  0.553835  0.315182  0.0         8.0  2001-01-01\n",
       "2001-02-18    464  2001  0.079125  0.101180  2.0         8.0  2001-01-01\n",
       "2001-03-06    464  2001  0.522178  0.280573  0.0         8.0  2001-01-01"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "##if our data is >2 dimension, we are interested in predicting the \n",
    "def to_sequences_MD (seq_size,obs,xcols,ycol,step=1):\n",
    "    x = []\n",
    "    y = []\n",
    "    y_time = []\n",
    "    for i in range(0,len(obs)-SEQUENCE_SIZE,step):\n",
    "        '''\n",
    "        seq_size: the number of observations will be used for training\n",
    "        step: the change of observations to form a new training data\n",
    "        obs: the whole dataset\n",
    "        '''\n",
    "        #print(i)\n",
    "        window = obs[i:(i+SEQUENCE_SIZE)][xcols]\n",
    "        after_window = obs[(i+SEQUENCE_SIZE -1):(i+SEQUENCE_SIZE)][ycol]\n",
    "        #window = [[x] for x in window]\n",
    "        time_index = after_window.index.values[0]\n",
    "        win_values = window.values\n",
    "        #print(\"{} - {}\".format(window,after_window))\n",
    "        x.append(win_values)\n",
    "        y.append(after_window.values)\n",
    "        y_time.append(time_index)\n",
    "        \n",
    "    return np.array(x),np.array(y),y_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel</th>\n",
       "      <th>year</th>\n",
       "      <th>ndvi</th>\n",
       "      <th>evi</th>\n",
       "      <th>qa</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>lc_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.510641</td>\n",
       "      <td>0.287295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-17</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.457637</td>\n",
       "      <td>0.162410</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-02</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.553835</td>\n",
       "      <td>0.315182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-18</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.079125</td>\n",
       "      <td>0.101180</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-03-06</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.522178</td>\n",
       "      <td>0.280573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-03-22</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.570117</td>\n",
       "      <td>0.342850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-04-07</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.648065</td>\n",
       "      <td>0.475009</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-04-23</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.668908</td>\n",
       "      <td>0.390804</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-09</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.487433</td>\n",
       "      <td>0.284394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-25</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.540597</td>\n",
       "      <td>0.359430</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-06-10</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.703998</td>\n",
       "      <td>0.471850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pixel  year      ndvi       evi   qa  land_cover     lc_time\n",
       "time                                                                    \n",
       "2001-01-01    464  2001  0.510641  0.287295  1.0         8.0  2001-01-01\n",
       "2001-01-17    464  2001  0.457637  0.162410  1.0         8.0  2001-01-01\n",
       "2001-02-02    464  2001  0.553835  0.315182  0.0         8.0  2001-01-01\n",
       "2001-02-18    464  2001  0.079125  0.101180  2.0         8.0  2001-01-01\n",
       "2001-03-06    464  2001  0.522178  0.280573  0.0         8.0  2001-01-01\n",
       "2001-03-22    464  2001  0.570117  0.342850  0.0         8.0  2001-01-01\n",
       "2001-04-07    464  2001  0.648065  0.475009  3.0         8.0  2001-01-01\n",
       "2001-04-23    464  2001  0.668908  0.390804  1.0         8.0  2001-01-01\n",
       "2001-05-09    464  2001  0.487433  0.284394  0.0         8.0  2001-01-01\n",
       "2001-05-25    464  2001  0.540597  0.359430  0.0         8.0  2001-01-01\n",
       "2001-06-10    464  2001  0.703998  0.471850  1.0         8.0  2001-01-01"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train2.head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "after_win =df_train2[9:10]['qa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2001-05-25'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after_win.index.values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "##about 20 years data and 0.8 for training\n",
    "sel_date= \"2016-01-01\"\n",
    "df_train2 = df2[df2.index < sel_date ]\n",
    "df_test2 = df2[df2.index >= sel_date ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training set: (335, 10, 2)\n",
      "Shape of test set: (82, 10, 2)\n"
     ]
    }
   ],
   "source": [
    "SEQUENCE_SIZE = 10\n",
    "x_train2,y_train2,train_index = to_sequences_MD(SEQUENCE_SIZE,df_train2,xcols=['ndvi','evi'],ycol = ['qa'])\n",
    "x_test2,y_test2,test_index = to_sequences_MD(SEQUENCE_SIZE,df_test2,xcols=['ndvi','evi'],ycol = ['qa'])\n",
    "\n",
    "print(\"Shape of training set: {}\".format(x_train2.shape))\n",
    "print(\"Shape of test set: {}\".format(x_test2.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train21 = y_train2.reshape((y_train2.shape[0],))\n",
    "y_test21 = y_test2.reshape((y_test2.shape[0],))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n",
      "Train on 335 samples, validate on 82 samples\n",
      "Epoch 1/1000\n",
      "335/335 - 3s - loss: 0.8810 - val_loss: 0.7251\n",
      "Epoch 2/1000\n",
      "335/335 - 0s - loss: 0.6879 - val_loss: 0.7244\n",
      "Epoch 3/1000\n",
      "335/335 - 0s - loss: 0.6673 - val_loss: 0.7378\n",
      "Epoch 4/1000\n",
      "335/335 - 0s - loss: 0.6629 - val_loss: 0.7236\n",
      "Epoch 5/1000\n",
      "335/335 - 0s - loss: 0.6529 - val_loss: 0.7191\n",
      "Epoch 6/1000\n",
      "335/335 - 0s - loss: 0.6455 - val_loss: 0.7170\n",
      "Epoch 7/1000\n",
      "335/335 - 0s - loss: 0.6397 - val_loss: 0.7155\n",
      "Epoch 8/1000\n",
      "335/335 - 0s - loss: 0.6314 - val_loss: 0.7101\n",
      "Epoch 9/1000\n",
      "335/335 - 0s - loss: 0.6183 - val_loss: 0.7072\n",
      "Epoch 10/1000\n",
      "335/335 - 0s - loss: 0.6055 - val_loss: 0.6992\n",
      "Epoch 11/1000\n",
      "335/335 - 0s - loss: 0.6068 - val_loss: 0.7102\n",
      "Epoch 12/1000\n",
      "335/335 - 0s - loss: 0.5895 - val_loss: 0.6929\n",
      "Epoch 13/1000\n",
      "335/335 - 0s - loss: 0.5844 - val_loss: 0.6950\n",
      "Epoch 14/1000\n",
      "335/335 - 0s - loss: 0.5696 - val_loss: 0.6925\n",
      "Epoch 15/1000\n",
      "335/335 - 0s - loss: 0.5717 - val_loss: 0.7211\n",
      "Epoch 16/1000\n",
      "335/335 - 0s - loss: 0.5620 - val_loss: 0.7087\n",
      "Epoch 17/1000\n",
      "Restoring model weights from the end of the best epoch.\n",
      "335/335 - 0s - loss: 0.5618 - val_loss: 0.6974\n",
      "Epoch 00017: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d6cc9c1888>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(64, dropout=0.0, recurrent_dropout=0.0,input_shape=(None, 2)))##here also need to change\n",
    "model.add(Dense(32))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=1, mode='auto', restore_best_weights=True)\n",
    "print('Train...')\n",
    "\n",
    "model.fit(x_train2,y_train21,validation_data=(x_test2,y_test21),callbacks=[monitor],verbose=2,epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score (RMSE): 0.832413255469476\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "pred2 = model.predict(x_test2)\n",
    "score = np.sqrt(metrics.mean_squared_error(pred2,y_test21))\n",
    "print(\"Score (RMSE): {}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "### as we know that the good quality points should be within [0,1], we can classify them into good or not good\n",
    "output = pd.DataFrame({\"pred\":pred2.ravel(),\n",
    "              \"test\":y_test21.ravel()},index = test_index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-05-24</th>\n",
       "      <td>0.731439</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-09</th>\n",
       "      <td>0.666747</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-25</th>\n",
       "      <td>0.578940</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-11</th>\n",
       "      <td>0.529566</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-27</th>\n",
       "      <td>0.584527</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                pred  test\n",
       "2016-05-24  0.731439   1.0\n",
       "2016-06-09  0.666747   3.0\n",
       "2016-06-25  0.578940   1.0\n",
       "2016-07-11  0.529566   0.0\n",
       "2016-07-27  0.584527   3.0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-6"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(dat2['qa'].min(), -0.0003)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [min(dat2['qa'].min(), -0.0003),1.0001,dat2['qa'].max()]\n",
    "names = ['yes','no']\n",
    "\n",
    "output['pred_class'] = pd.cut(output['pred'],bins,labels = names)\n",
    "output['test_class'] = pd.cut(output['test'],bins,labels = names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(output['test_class'], output['pred_class'],labels=['yes','no'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[75,  0],\n",
       "       [ 7,  0]], dtype=int64)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_class = pd.get_dummies(output['pred_class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9146341463414634\n"
     ]
    }
   ],
   "source": [
    "correct = accuracy_score(output['pred_class'],output['test_class'])\n",
    "print(f\"Accuracy: {correct}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "We can see we can only can detect good quality, but caanot predict low quality.\n",
    "\n",
    "There are several reasons for this:\n",
    "\n",
    "    1 imblance data\n",
    "    \n",
    "    2 not enough data for LSTM\n",
    "    \n",
    "    3 There is no connection between the ndvi,evi and quality. We cannot assess the value quality based on these variables\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Predicition on data quality using NDVI and EVI - classification\n",
    "Classify y (qa) into yes (good quality) and no (low quality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tank\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df3 = dat2[dat2['pixel'] == sel_pixel[0]]\n",
    "df3.sort_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tank\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "bins = [min(df3['qa'].min()-0.01, -0.000001),1.0001,df3['qa'].max()]\n",
    "names = ['yes','no']\n",
    "\n",
    "df3['quality'] = pd.cut(df3['qa'],bins,labels = names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data format (still use the function in 2.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "##about 20 years data and 0.8 for training\n",
    "sel_date= \"2016-01-01\"\n",
    "df_train3 = df3[df3.index < sel_date ]\n",
    "df_test3 = df3[df3.index >= sel_date ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel</th>\n",
       "      <th>year</th>\n",
       "      <th>ndvi</th>\n",
       "      <th>evi</th>\n",
       "      <th>qa</th>\n",
       "      <th>land_cover</th>\n",
       "      <th>lc_time</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.510641</td>\n",
       "      <td>0.287295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-17</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.457637</td>\n",
       "      <td>0.162410</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-02</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.553835</td>\n",
       "      <td>0.315182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-18</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.079125</td>\n",
       "      <td>0.101180</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-03-06</th>\n",
       "      <td>464</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.522178</td>\n",
       "      <td>0.280573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pixel  year      ndvi       evi   qa  land_cover     lc_time  \\\n",
       "time                                                                       \n",
       "2001-01-01    464  2001  0.510641  0.287295  1.0         8.0  2001-01-01   \n",
       "2001-01-17    464  2001  0.457637  0.162410  1.0         8.0  2001-01-01   \n",
       "2001-02-02    464  2001  0.553835  0.315182  0.0         8.0  2001-01-01   \n",
       "2001-02-18    464  2001  0.079125  0.101180  2.0         8.0  2001-01-01   \n",
       "2001-03-06    464  2001  0.522178  0.280573  0.0         8.0  2001-01-01   \n",
       "\n",
       "           quality  \n",
       "time                \n",
       "2001-01-01     yes  \n",
       "2001-01-17     yes  \n",
       "2001-02-02     yes  \n",
       "2001-02-18      no  \n",
       "2001-03-06     yes  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training set: (335, 10, 2)\n",
      "Shape of test set: (82, 10, 2)\n"
     ]
    }
   ],
   "source": [
    "SEQUENCE_SIZE = 10\n",
    "x_train3,y_train3,train_index = to_sequences_MD(SEQUENCE_SIZE,df_train3,xcols=['ndvi','evi'],ycol = ['quality'])\n",
    "x_test3,y_test3,test_index = to_sequences_MD(SEQUENCE_SIZE,df_test3,xcols=['ndvi','evi'],ycol = ['quality'])\n",
    "\n",
    "print(\"Shape of training set: {}\".format(x_train2.shape))\n",
    "print(\"Shape of test set: {}\".format(x_test2.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train31 = y_train3.reshape((y_train3.shape[0],))\n",
    "y_test31 = y_test3.reshape((y_test3.shape[0],))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train32 = pd.get_dummies(pd.DataFrame(y_train31)).values\n",
    "y_test32 = pd.get_dummies(pd.DataFrame(y_test31)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_class = x_train3.shape[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n",
      "Train on 335 samples, validate on 82 samples\n",
      "Epoch 1/1000\n",
      "335/335 - 3s - loss: 0.4833 - val_loss: 0.2965\n",
      "Epoch 2/1000\n",
      "335/335 - 0s - loss: 0.3148 - val_loss: 0.2968\n",
      "Epoch 3/1000\n",
      "335/335 - 0s - loss: 0.3039 - val_loss: 0.2922\n",
      "Epoch 4/1000\n",
      "335/335 - 0s - loss: 0.3000 - val_loss: 0.2913\n",
      "Epoch 5/1000\n",
      "335/335 - 0s - loss: 0.2968 - val_loss: 0.2907\n",
      "Epoch 6/1000\n",
      "335/335 - 0s - loss: 0.2978 - val_loss: 0.2888\n",
      "Epoch 7/1000\n",
      "335/335 - 0s - loss: 0.2934 - val_loss: 0.2883\n",
      "Epoch 8/1000\n",
      "335/335 - 0s - loss: 0.2858 - val_loss: 0.2859\n",
      "Epoch 9/1000\n",
      "335/335 - 0s - loss: 0.2846 - val_loss: 0.2844\n",
      "Epoch 10/1000\n",
      "335/335 - 0s - loss: 0.2828 - val_loss: 0.2828\n",
      "Epoch 11/1000\n",
      "335/335 - 0s - loss: 0.2740 - val_loss: 0.2830\n",
      "Epoch 12/1000\n",
      "335/335 - 0s - loss: 0.2782 - val_loss: 0.2840\n",
      "Epoch 13/1000\n",
      "335/335 - 0s - loss: 0.2659 - val_loss: 0.2967\n",
      "Epoch 14/1000\n",
      "335/335 - 0s - loss: 0.2686 - val_loss: 0.2801\n",
      "Epoch 15/1000\n",
      "335/335 - 0s - loss: 0.2604 - val_loss: 0.3219\n",
      "Epoch 16/1000\n",
      "335/335 - 0s - loss: 0.2696 - val_loss: 0.2879\n",
      "Epoch 17/1000\n",
      "335/335 - 0s - loss: 0.2575 - val_loss: 0.2797\n",
      "Epoch 18/1000\n",
      "335/335 - 0s - loss: 0.2527 - val_loss: 0.2984\n",
      "Epoch 19/1000\n",
      "335/335 - 0s - loss: 0.2610 - val_loss: 0.2882\n",
      "Epoch 20/1000\n",
      "335/335 - 0s - loss: 0.2570 - val_loss: 0.2798\n",
      "Epoch 21/1000\n",
      "335/335 - 0s - loss: 0.2499 - val_loss: 0.2814\n",
      "Epoch 22/1000\n",
      "335/335 - 0s - loss: 0.2508 - val_loss: 0.2793\n",
      "Epoch 23/1000\n",
      "335/335 - 0s - loss: 0.2664 - val_loss: 0.2955\n",
      "Epoch 24/1000\n",
      "335/335 - 0s - loss: 0.2525 - val_loss: 0.2790\n",
      "Epoch 25/1000\n",
      "335/335 - 0s - loss: 0.2484 - val_loss: 0.2848\n",
      "Epoch 26/1000\n",
      "335/335 - 0s - loss: 0.2498 - val_loss: 0.2791\n",
      "Epoch 27/1000\n",
      "335/335 - 0s - loss: 0.2425 - val_loss: 0.2953\n",
      "Epoch 28/1000\n",
      "335/335 - 0s - loss: 0.2530 - val_loss: 0.2857\n",
      "Epoch 29/1000\n",
      "335/335 - 0s - loss: 0.2516 - val_loss: 0.2989\n",
      "Epoch 30/1000\n",
      "335/335 - 0s - loss: 0.2536 - val_loss: 0.2777\n",
      "Epoch 31/1000\n",
      "335/335 - 0s - loss: 0.2416 - val_loss: 0.2822\n",
      "Epoch 32/1000\n",
      "335/335 - 0s - loss: 0.2400 - val_loss: 0.2805\n",
      "Epoch 33/1000\n",
      "335/335 - 0s - loss: 0.2330 - val_loss: 0.2840\n",
      "Epoch 34/1000\n",
      "335/335 - 0s - loss: 0.2287 - val_loss: 0.2768\n",
      "Epoch 35/1000\n",
      "335/335 - 0s - loss: 0.2326 - val_loss: 0.2764\n",
      "Epoch 36/1000\n",
      "335/335 - 0s - loss: 0.2219 - val_loss: 0.2806\n",
      "Epoch 37/1000\n",
      "335/335 - 0s - loss: 0.2395 - val_loss: 0.3324\n",
      "Epoch 38/1000\n",
      "335/335 - 0s - loss: 0.2589 - val_loss: 0.2756\n",
      "Epoch 39/1000\n",
      "335/335 - 0s - loss: 0.2274 - val_loss: 0.2739\n",
      "Epoch 40/1000\n",
      "335/335 - 0s - loss: 0.2218 - val_loss: 0.2790\n",
      "Epoch 41/1000\n",
      "335/335 - 0s - loss: 0.2146 - val_loss: 0.2723\n",
      "Epoch 42/1000\n",
      "335/335 - 0s - loss: 0.2122 - val_loss: 0.2989\n",
      "Epoch 43/1000\n",
      "335/335 - 0s - loss: 0.2127 - val_loss: 0.2711\n",
      "Epoch 44/1000\n",
      "335/335 - 0s - loss: 0.2087 - val_loss: 0.2904\n",
      "Epoch 45/1000\n",
      "335/335 - 0s - loss: 0.1996 - val_loss: 0.2712\n",
      "Epoch 46/1000\n",
      "335/335 - 0s - loss: 0.1926 - val_loss: 0.2750\n",
      "Epoch 47/1000\n",
      "335/335 - 0s - loss: 0.1896 - val_loss: 0.3132\n",
      "Epoch 48/1000\n",
      "335/335 - 0s - loss: 0.1971 - val_loss: 0.2674\n",
      "Epoch 49/1000\n",
      "335/335 - 0s - loss: 0.1797 - val_loss: 0.3047\n",
      "Epoch 50/1000\n",
      "335/335 - 0s - loss: 0.1835 - val_loss: 0.2667\n",
      "Epoch 51/1000\n",
      "335/335 - 0s - loss: 0.1927 - val_loss: 0.2739\n",
      "Epoch 52/1000\n",
      "335/335 - 0s - loss: 0.1735 - val_loss: 0.2764\n",
      "Epoch 53/1000\n",
      "335/335 - 0s - loss: 0.1758 - val_loss: 0.2831\n",
      "Epoch 54/1000\n",
      "335/335 - 0s - loss: 0.1692 - val_loss: 0.2654\n",
      "Epoch 55/1000\n",
      "335/335 - 0s - loss: 0.1673 - val_loss: 0.3167\n",
      "Epoch 56/1000\n",
      "335/335 - 0s - loss: 0.1705 - val_loss: 0.2644\n",
      "Epoch 57/1000\n",
      "335/335 - 0s - loss: 0.1622 - val_loss: 0.2845\n",
      "Epoch 58/1000\n",
      "335/335 - 0s - loss: 0.1639 - val_loss: 0.2667\n",
      "Epoch 59/1000\n",
      "335/335 - 0s - loss: 0.1560 - val_loss: 0.2857\n",
      "Epoch 60/1000\n",
      "335/335 - 0s - loss: 0.1501 - val_loss: 0.2779\n",
      "Epoch 61/1000\n",
      "335/335 - 0s - loss: 0.1485 - val_loss: 0.2802\n",
      "Epoch 62/1000\n",
      "335/335 - 0s - loss: 0.1492 - val_loss: 0.2912\n",
      "Epoch 63/1000\n",
      "335/335 - 0s - loss: 0.1457 - val_loss: 0.2776\n",
      "Epoch 64/1000\n",
      "335/335 - 0s - loss: 0.1489 - val_loss: 0.3213\n",
      "Epoch 65/1000\n",
      "335/335 - 0s - loss: 0.1514 - val_loss: 0.2848\n",
      "Epoch 66/1000\n",
      "335/335 - 0s - loss: 0.1510 - val_loss: 0.3206\n",
      "Epoch 67/1000\n",
      "335/335 - 0s - loss: 0.1522 - val_loss: 0.2848\n",
      "Epoch 68/1000\n",
      "335/335 - 0s - loss: 0.1425 - val_loss: 0.3033\n",
      "Epoch 69/1000\n",
      "Restoring model weights from the end of the best epoch.\n",
      "335/335 - 0s - loss: 0.1494 - val_loss: 0.3008\n",
      "Epoch 00069: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1d6dbbab708>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(128, input_shape=(None, num_class)))\n",
    "model.add(Dense(num_class, activation='softmax'))\n",
    "\n",
    "#optimizer = RMSprop(lr=0.01)\n",
    "#model.compile(loss='categorical_crossentropy', optimizer=optimizer)\n",
    "             \n",
    "model.compile(loss='binary_crossentropy', optimizer='adam')\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=15, verbose=1, mode='auto', restore_best_weights=True)\n",
    "print('Train...')\n",
    "\n",
    "model.fit(x_train3,y_train32,validation_data=(x_test3,y_test32),callbacks=[monitor],verbose=2,epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(x_test3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss score: 0.26535736359996587\n"
     ]
    }
   ],
   "source": [
    "score = metrics.log_loss(y_test32, pred)\n",
    "print(\"Log loss score: {}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "##evaulation FOR CLASSIFICATION\n",
    "predict_classes = np.argmax(pred,axis=1)\n",
    "expected_classes = np.argmax(y_test32,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9146341463414634\n"
     ]
    }
   ],
   "source": [
    "correct = accuracy_score(expected_classes,predict_classes)\n",
    "print(f\"Accuracy: {correct}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  7],\n",
       "       [ 0, 75]], dtype=int64)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(expected_classes,predict_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Please look at section 2.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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