Step_5.ipynb

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    "## Step 5: Implementing a Machine Learning algorithm \n",
    "\n",
    "### Background: \n",
    "Once we have the information from the satellite band pixels and the respective soil moisture values, we can implement a machine learning algorithm that can correlate  the band pixels (we will call this input) and soil moisture (output). There are plenty of machine learning algorithms. A good website where examples and Python implemented algorithms are compiled is https://scikit-learn.org/stable/.\n",
    "\n",
    "In this case, we are going to use several machine learning algorithms available in google earth engine. Thses algorithms are a good example of a \"classical machine learning\", different of more recent algorithms such as Deep Learning (e.g. Convolutional Neural Networks). The advantage of classical machine learning is the need of much less information than deep learning algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's install the necessary python library. Open the terminal and pass the following command."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "pip install sklearn_rvm\n",
    "pip install mlxtend\n",
    "pip install eemont"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geemap\n",
    "import ee\n",
    "ee.Initialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "                .geemap-dark {\n",
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       "Map(center=[39.4037160204789, -112.63575111241983], controls=(WidgetControl(options=['position', 'transparent_…"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "in_shp = './/Results Step 1//soil_moisture.shp'\n",
    "\n",
    "sm_locations = geemap.shp_to_ee(in_shp)\n",
    "\n",
    "Map1 = geemap.Map() \n",
    "Map1.addLayer(sm_locations, {'color': 'red'}, 'Soil Moisture')\n",
    "\n",
    "Map1.centerObject(sm_locations, 12)\n",
    "\n",
    "Map1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start importing the csv file generated in Step 2:"
   ]
  },
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   "execution_count": 3,
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     "text": [
      "{'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [-112.65024, 39.38476]}, 'id': '0', 'properties': {'CROP': 'alfalfa', 'FIELD': '101A', 'SA0513': 0.6, 'SA0529': 0.09, 'SA0614': 0, 'SA0630': 0.05, 'SM0513': 0.39, 'SM0529': 0.14, 'SM0614': 0.1, 'SM0630': 0.13, 'T0513': 24.93, 'T0529': 25.8, 'T0614': 23.77, 'T0630': 28.97, 'sampling p': 1}}\n"
     ]
    }
   ],
   "source": [
    "print(sm_locations.first().getInfo())"
   ]
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   "execution_count": 4,
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    "def set_SM0513(feature):\n",
    "    # keepProperties = 'SA0513';\n",
    "    location=feature.geometry().centroid()\n",
    "    return ee.Feature(location).set('SM',feature.get('SM0513'))"
   ]
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   "execution_count": 5,
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     "text": [
      "{'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [-112.65024000000001, 39.38476]}, 'id': '0', 'properties': {'SM': 0.39}}\n"
     ]
    }
   ],
   "source": [
    "sm_0513=sm_locations.map(set_SM0513)\n",
    "print(sm_0513.first().getInfo())"
   ]
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   "execution_count": 6,
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    "def set_SM0529(feature):\n",
    "    # keepProperties = 'SA0513';\n",
    "    location=feature.geometry().centroid()\n",
    "    return ee.Feature(location).set('SM',feature.get('SM0529'))"
   ]
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     "text": [
      "{'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [-112.65024000000001, 39.38476]}, 'id': '0', 'properties': {'SM': 0.14}}\n"
     ]
    }
   ],
   "source": [
    "sm_0529=sm_locations.map(set_SM0529)\n",
    "print(sm_0529.first().getInfo())"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "def set_SM0614(feature):\n",
    "    # keepProperties = 'SA0513';\n",
    "    location=feature.geometry().centroid()\n",
    "    return ee.Feature(location).set('SM',feature.get('SM0614'))"
   ]
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   "execution_count": 9,
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       "                }\n",
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     "text": [
      "{'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [-112.65024000000001, 39.38476]}, 'id': '0', 'properties': {'SM': 0.1}}\n"
     ]
    }
   ],
   "source": [
    "sm_0614=sm_locations.map(set_SM0614)\n",
    "print(sm_0614.first().getInfo())"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "def set_SM0630(feature):\n",
    "    # keepProperties = 'SA0513';\n",
    "    location=feature.geometry().centroid()\n",
    "    return ee.Feature(location).set('SM',feature.get('SM0630'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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     "text": [
      "{'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [-112.65024000000001, 39.38476]}, 'id': '0', 'properties': {'SM': 0.13}}\n"
     ]
    }
   ],
   "source": [
    "sm_0630=sm_locations.map(set_SM0630)\n",
    "print(sm_0630.first().getInfo())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have four feature collections that have soil moisture information only and now we need to retrieve the pixel values from the four different images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   "source": [
    "import eemont\n",
    "\n",
    "import warnings #the last command produces a lot of warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# identifying landsat 7 image on the dates of interest\n",
    "\n",
    "\n",
    "Delta_area=ee.Geometry.Point([-112.577170, 39.352178])\n",
    "\n",
    "# Let's use the bands and vegetation indices that are not correlated with others (Step 3)\n",
    "predictionBands=['SR_B1', 'SR_B4', 'SR_B5', 'ST_B6', 'AFRI1600', 'BCC', 'CVI', 'DSWI2', 'DSWI3',\n",
    " 'DSWI4', 'ExG', 'IKAW', 'NDDI', 'NDYI', 'NMDI', 'NRFIg', 'NRFIr']\n",
    "\n",
    "landsat7 = ee.ImageCollection(\"LANDSAT/LE07/C02/T1_L2\").filterBounds(Delta_area) # Landsat 7 archive.\n",
    "landsat7 = landsat7.preprocess()\n",
    "landsat7 = landsat7.spectralIndices('vegetation')\n",
    "landsat7 = landsat7.select(predictionBands);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's identify the images for the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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       "\n",
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   ],
   "source": [
    "# code for the first Landsat date\n",
    "landsat7_may_13 = ee.Image(landsat7. #the dataset\n",
    "                    filterDate('2012-05-12','2012-05-14'). # the date before and after\n",
    "                    first()) # the first image from the aplication of the two previous filters\n",
    "# code for the other three landsat dates\n",
    "landsat7_may_29 = ee.Image(landsat7.filterDate('2012-05-28','2012-05-30').first()) # all commands in one line.\n",
    "landsat7_jun_14 = ee.Image(landsat7.filterDate('2012-06-13','2012-06-15').first()) # all commands in one line.\n",
    "landsat7_jun_30 = ee.Image(landsat7.filterDate('2012-06-29','2012-07-01').first()) # all commands in one line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting pixel information into the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pixelInfo_0513(feature):\n",
    "    meanDictionary = landsat7_may_13.reduceRegion(\n",
    "    reducer= ee.Reducer.mean(),\n",
    "    geometry= feature.geometry(),\n",
    "    scale= 30,\n",
    "    maxPixels= 1e9)\n",
    "    values= ee.Feature(feature.geometry().centroid(),meanDictionary).set('SM',feature.get('SM'))\n",
    "    return values\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'type': 'Feature',\n",
       " 'geometry': {'type': 'Point', 'coordinates': [-112.65024000000001, 39.38476]},\n",
       " 'id': '0',\n",
       " 'properties': {'AFRI1600': 0.4884040935201041,\n",
       "  'BCC': 0.2455264621139362,\n",
       "  'CVI': 3.9705342399047927,\n",
       "  'DSWI2': 2.4865320898269725,\n",
       "  'DSWI3': 2.990039473198806,\n",
       "  'DSWI4': 1.2024938207842997,\n",
       "  'ExG': 0.04664000000000004,\n",
       "  'IKAW': 0.16499054495444382,\n",
       "  'NDDI': 27.31892837728061,\n",
       "  'NDYI': 0.2530898606439213,\n",
       "  'NMDI': 0.584506289420233,\n",
       "  'NRFIg': -0.10496039101006345,\n",
       "  'NRFIr': -0.19501692700599876,\n",
       "  'SM': 0.39,\n",
       "  'SR_B1': 0.04857249999999999,\n",
       "  'SR_B4': 0.3890775,\n",
       "  'SR_B5': 0.20262750000000002,\n",
       "  'ST_B6': 299.0681681}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_0513_pix=sm_0513.map(pixelInfo_0513)\n",
    "properties=['SR_B1']\n",
    "sm_0513_pix = sm_0513_pix.filter(ee.Filter.notNull(properties))\n",
    "sm_0513_pix.first().getInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pixelInfo_0529(feature):\n",
    "\n",
    "    meanDictionary = landsat7_may_29.reduceRegion(\n",
    "        reducer= ee.Reducer.mean(),\n",
    "        geometry= feature.geometry(),\n",
    "        scale= 30,\n",
    "        maxPixels= 1e9)\n",
    "    values= ee.Feature(feature.geometry().centroid(),meanDictionary).set('SM',feature.get('SM'))\n",
    "    return values\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'type': 'Feature',\n",
       " 'geometry': {'type': 'Point', 'coordinates': [-112.65024000000001, 39.38476]},\n",
       " 'id': '0',\n",
       " 'properties': {'AFRI1600': 0.6047176083122665,\n",
       "  'BCC': 0.20469465277101384,\n",
       "  'CVI': 5.643696085557662,\n",
       "  'DSWI2': 3.059799015756171,\n",
       "  'DSWI3': 4.444851616003846,\n",
       "  'DSWI4': 1.4526613000120159,\n",
       "  'ExG': 0.05302000000000007,\n",
       "  'IKAW': 0.2260440450762319,\n",
       "  'NDDI': 26.040424498698712,\n",
       "  'NDYI': 0.3941595318400649,\n",
       "  'NMDI': 0.6463001947375197,\n",
       "  'NRFIg': -0.1298355464392385,\n",
       "  'NRFIr': -0.3070374456216309,\n",
       "  'SM': 0.14,\n",
       "  'SR_B1': 0.026269999999999988,\n",
       "  'SR_B4': 0.49561250000000007,\n",
       "  'SR_B5': 0.18497249999999998,\n",
       "  'ST_B6': 301.37191357999995}}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_0529_pix=sm_0529.map(pixelInfo_0529)\n",
    "# (sm_0529_pix.getInfo())\n",
    "properties=['SR_B1']\n",
    "sm_0529_pix = sm_0529_pix.filter(ee.Filter.notNull(properties))\n",
    "sm_0529_pix.first().getInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pixelInfo_0614(feature):\n",
    "    meanDictionary = landsat7_jun_14.reduceRegion(\n",
    "    reducer= ee.Reducer.mean(),\n",
    "    geometry= feature.geometry(),\n",
    "    scale= 30,\n",
    "    maxPixels= 1e9)\n",
    "    values= ee.Feature(feature.geometry().centroid(),meanDictionary).set('SM',feature.get('SM'))\n",
    "    return values\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [-112.65024000000001, 39.38476]}, 'id': '0', 'properties': {'AFRI1600': 0.28778720770345995, 'BCC': 0.2541624861024487, 'CVI': 3.0758342874912277, 'DSWI2': 2.3672988931290466, 'DSWI3': 2.1743103267882677, 'DSWI4': 0.9184773131517454, 'ExG': 0.026262499999999966, 'IKAW': 0.20935921633961427, 'NDDI': -27.486873719165022, 'NDYI': 0.16836361216477, 'NMDI': 0.5060195235998723, 'NRFIg': -0.18054950525857238, 'NRFIr': -0.13912345095213424, 'SM': 0.1, 'SR_B1': 0.09372750000000002, 'SR_B4': 0.37200000000000005, 'SR_B5': 0.31172000000000005, 'ST_B6': 313.83401449999997}}\n"
     ]
    }
   ],
   "source": [
    "sm_0614_pix=sm_0614.map(pixelInfo_0614)\n",
    "properties=['SR_B1']\n",
    "sm_0614_pix = sm_0614_pix.filter(ee.Filter.notNull(properties))\n",
    "print(sm_0614_pix.first().getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pixelInfo_0630(feature):\n",
    "    meanDictionary = landsat7_jun_30.reduceRegion(\n",
    "    reducer= ee.Reducer.mean(),\n",
    "    geometry= feature.geometry(),\n",
    "    scale= 30,\n",
    "    maxPixels= 1e9)\n",
    "    values= ee.Feature(feature.geometry().centroid(),meanDictionary).set('SM',feature.get('SM'))\n",
    "    return values\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'type': 'Feature', 'geometry': {'type': 'Point', 'coordinates': [-112.65024000000001, 39.38476]}, 'id': '0', 'properties': {'AFRI1600': 0.374613034505655, 'BCC': 0.2394789252016064, 'CVI': 3.6613756470520076, 'DSWI2': 2.3904419389713834, 'DSWI3': 2.264058679706602, 'DSWI4': 0.9471297515307298, 'ExG': 0.03363250000000001, 'IKAW': 0.23982744549376211, 'NDDI': -56.668676902172194, 'NDYI': 0.21406854800905387, 'NMDI': 0.5310006916100878, 'NRFIg': -0.14094277753558632, 'NRFIr': -0.11422701021768113, 'SM': 0.13, 'SR_B1': 0.07335000000000003, 'SR_B4': 0.39292750000000004, 'SR_B5': 0.270855, 'ST_B6': 310.95604166}}\n"
     ]
    }
   ],
   "source": [
    "sm_0630_pix=sm_0630.map(pixelInfo_0630)\n",
    "properties=['SR_B1']\n",
    "sm_0630_pix = sm_0630_pix.filter(ee.Filter.notNull(properties))\n",
    "print(sm_0630_pix.first().getInfo())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have four feature collections with the soil moisture data and pixel information, we can merge them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm_trainingTesting=sm_0513_pix.merge(sm_0529_pix).merge(sm_0614_pix).merge(sm_0630_pix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# // Randomly split the data into 60% for training, and 40% for testing\n",
    "sm_trainingTesting = sm_trainingTesting.randomColumn('random',0);\n",
    "\n",
    "training = sm_trainingTesting.filter(ee.Filter.lt('random', 0.6));\n",
    "\n",
    "testing = sm_trainingTesting.filter(ee.Filter.gte('random', 0.6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non linear functions (machine learning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the linear regression isn't satisfactory, Earth Engine contains other functions that can make predictions of a continuous variable.  Unlike linear regression, other regression functions are implemented by the classifier library.  \n",
    "\n",
    "- For example, a Classification and Regression Tree (CART, see Brieman et al. 1984) is a machine learning algorithm that can learn non-linear patterns in your data.  Reusing the T table (without the constant term), train a CART as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "leafs=1\n",
    "cartRegression = ee.Classifier.smileCart(minLeafPopulation=leafs).setOutputMode('REGRESSION').train(\n",
    "      features= training, \n",
    "      classProperty= 'SM', \n",
    "      inputProperties= predictionBands\n",
    "    );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                    --jp-layout-color2: #454545;\n",
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       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
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       "<IPython.core.display.HTML object>"
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      },
      "text/plain": [
       "Map(center=[0, 0], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDataGUI(childr…"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cartRegressionImage = landsat7_jun_30.select(predictionBands).classify(cartRegression, 'y_hat');\n",
    "\n",
    "Map=geemap.Map()\n",
    "\n",
    "Map.addLayer(cartRegressionImage, {'min': 0, 'max': 0.6,\n",
    "                                   'palette':['orange','yellow','green','cyan','blue']},'CART regression');\n",
    "Map\n",
    "Map.centerObject(sm_trainingTesting)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a Random Forest regression classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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   "source": [
    "trees =5 #this value needs to be optimized\n",
    "\n",
    "rfRegression = ee.Classifier.smileRandomForest(numberOfTrees=trees, seed=0).setOutputMode('REGRESSION').train(\n",
    "      features= training, \n",
    "      classProperty= 'SM', \n",
    "      inputProperties= predictionBands\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
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       "model_id": "a6371b3b639d404bbe7f1c7ea3b56a61",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[0, 0], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDataGUI(childr…"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# // Perform the RF regression on the landsat image\n",
    "rfRegressionImage = landsat7_jun_30.select(predictionBands).classify(rfRegression, 'y_hat');\n",
    "    \n",
    "# // Visualize the RF regression\n",
    "Map2=geemap.Map()\n",
    "\n",
    "Map2.addLayer(rfRegressionImage, {'min': 0, 'max': 0.6,\n",
    "                                   'palette':['orange','yellow','green','cyan','blue']},'RF regression');\n",
    "Map2.centerObject(sm_trainingTesting)\n",
    "Map2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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     "metadata": {},
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   ],
   "source": [
    "# ee.Classifier.libsvm(decisionProcedure, svmType, kernelType, shrinking, degree, gamma, coef0, cost, nu, terminationEpsilon, lossEpsilon, oneClass)\n",
    "\n",
    "gamma_v =1 #this value needs to be optimized\n",
    "\n",
    "# // Create an SVM classifier with custom parameters.\n",
    "svrRegression = ee.Classifier.libsvm(svmType ='EPSILON_SVR',kernelType='RBF',gamma=gamma_v,cost=100).setOutputMode('REGRESSION').train(\n",
    "      features= training, \n",
    "      classProperty= 'SM', \n",
    "      inputProperties= predictionBands\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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       "<IPython.core.display.HTML object>"
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    {
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       "model_id": "c514657de6174267aaf4b41a71cb42ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[0, 0], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDataGUI(childr…"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# // Perform the RF regression on the landsat image\n",
    "svrRegressionImage = landsat7_jun_30.select(predictionBands).classify(svrRegression, 'y_hat');\n",
    "    \n",
    "# // Visualize the RF regression\n",
    "Map3=geemap.Map()\n",
    "Map3.addLayer(svrRegressionImage, {'min': 0, 'max': 0.6,'palette':['red','yellow', 'green', 'blue', 'purple']}, 'SVR regression');\n",
    "Map3.centerObject(sm_trainingTesting)\n",
    "\n",
    "Map3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need indicators of the performance of these models. We need a goodness of fit statistics. The most used is Root Mean Square Error (RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
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       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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   "source": [
    "# ////////////////////////////////////////////////////////////////////////////////\n",
    "# // Function to compute the sum of squared differences for the results from \n",
    "# // any Regression. This function uses the actual values stored in the \n",
    "# // 'Percent_Tree_Cover' property of the 'regression' feature collection, and the predicted \n",
    "# // values stored in the 'prediction' property.\n",
    "def set_sqError(feature):\n",
    "    feature = ee.Feature(feature)\n",
    "    actualValue = ee.Number(feature.get('SM'))\n",
    "    predict = ee.Number(feature.get('y_hat'))\n",
    "    sqError = (actualValue.subtract(predict)).multiply(actualValue.subtract(predict))\n",
    "    feature = feature.set('sqError',sqError)\n",
    "    return feature\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CART Regression RMSE: 0.1480531384360571\n"
     ]
    }
   ],
   "source": [
    "# // Perform the CART regression on the test set\n",
    "cartRegressionTesting = testing.classify(cartRegression, 'y_hat');\n",
    "\n",
    "# // Calculate the RMSE for CART regression and print the result\n",
    "regression=cartRegressionTesting.map(set_sqError)\n",
    "regression = ee.FeatureCollection(regression)\n",
    "MSE = regression.reduceColumns(ee.Reducer.mean(),['sqError'])\n",
    "RMSE =  ee.Number(MSE.get('mean')).sqrt()\n",
    "\n",
    "print('CART Regression RMSE:', RMSE.getInfo());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF Regression RMSE: 0.13988214798776322\n"
     ]
    }
   ],
   "source": [
    "# // Perform the RF regression on the test set\n",
    "rfRegressionTesting = testing.classify(rfRegression, 'y_hat');\n",
    "\n",
    "# // Calculate the RMSE for RF regression and print the result\n",
    "regression=rfRegressionTesting.map(set_sqError)\n",
    "regression = ee.FeatureCollection(regression)\n",
    "MSE = regression.reduceColumns(ee.Reducer.mean(),['sqError'])\n",
    "RMSE =  ee.Number(MSE.get('mean')).sqrt()\n",
    "\n",
    "print('RF Regression RMSE:', RMSE.getInfo());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR Regression RMSE: 0.14362656377533617\n"
     ]
    }
   ],
   "source": [
    "# // Perform the SVR regression on the test set\n",
    "svrRegressionTesting = testing.classify(svrRegression, 'y_hat');\n",
    "\n",
    "# // Calculate the RMSE for RF regression and print the result\n",
    "regression=svrRegressionTesting.map(set_sqError)\n",
    "regression = ee.FeatureCollection(regression)\n",
    "MSE = regression.reduceColumns(ee.Reducer.mean(),['sqError'])\n",
    "RMSE =  ee.Number(MSE.get('mean')).sqrt()\n",
    "\n",
    "print('SVR Regression RMSE:', RMSE.getInfo());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Hyperparameters tuning\n",
    "\n",
    "A random forest is a collection of random trees the predictions of which are used to compute an average (regression) or vote on a label (classification).  Note that the only parameter to the classifier is the number of trees (10).  How many trees should you use?  Making that choice is best done by hyperparameter tuning.  For example, \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min RMSE = 0.11921642870261365\n",
      "Min Trees = 16\n"
     ]
    },
    {
     "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": [
    "numTrees = ee.List.sequence(1, 50, 1)\n",
    "\n",
    "\n",
    "def trees(t):\n",
    "    rfRegression = ee.Classifier.smileRandomForest(numberOfTrees=t, seed=0).setOutputMode('REGRESSION').train(\n",
    "    features= training, \n",
    "    classProperty= 'SM', \n",
    "    inputProperties= predictionBands)\n",
    "    \n",
    "    rfRegressionTesting = testing.classify(rfRegression, 'y_hat')\n",
    "\n",
    "    # // Calculate the RMSE for RF regression and print the result\n",
    "    regression=rfRegressionTesting.map(set_sqError)\n",
    "    regression = ee.FeatureCollection(regression)\n",
    "    MSE = regression.reduceColumns(ee.Reducer.mean(),['sqError'])\n",
    "    RMSE =  ee.Number(MSE.get('mean')).sqrt()        \n",
    "    return RMSE\n",
    "\n",
    "\n",
    "rmse_trees=numTrees.map(trees)\n",
    "value_info = rmse_trees.getInfo()\n",
    "\n",
    "# Extracting tree value for minimum RMSE\n",
    "min_rmse=min(value_info)\n",
    "print(\"Min RMSE =\", min_rmse)\n",
    "\n",
    "index=value_info.index(min(value_info))\n",
    "trees_range = numTrees.getInfo()\n",
    "min_trees =trees_range[index]\n",
    "print(\"Min Trees =\", min_trees)\n",
    "\n",
    "# print(rmse_gama.getInfo())\n",
    "\n",
    "# Plotting the interations\n",
    "\n",
    "import pandas as pd\n",
    "df =pd.DataFrame(value_info,columns=['RMSE'])\n",
    "df['numTrees'] = numTrees.getInfo() \n",
    "\n",
    "ax =df.plot.line(x='numTrees', \n",
    "             y='RMSE',\n",
    "             title= 'Impact of Number of Trees in Random Forest'\n",
    "             )\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "same for svr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min RMSE = 0.12570280092086217\n",
      "Min Gamma = 0.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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Sz7+0nesDciI4/ONfT8nIBemmaw779u1j1qxZzJs3jzvuuKM5vbCwkOnTp/PII4+wevVqfvOb3xx1/gknnMCtt97KrbfeyllnncWqVauSq+gRO/fX8lrZbpZsqmL5lirW76ymaSaoeNQgzisewelFQzh19GBOO34wJ48cSEHMV2Zxri/LieCQacOGDeP73/8+s2fP5ktf+tJRaV/96lf51Kc+xciRI486/vvf/56LL76YgoICduzYQWVlJePGjWPt2rXd3t59h+p5Y2MlfynbzWsbKimrqAFgaP98pp50HJdNGcO0k4YzdfxwjhtU2O3tcc71PA8OPWTatGmce+65LFy4kAsuuKD5+JQpU466S6nJCy+8wJ133kn//v0B+N73vseYMWNYu3Zti2sODzzwAH/7t397TO3btvcQi1du53erdrB8SxUJgwEFMc4rHsG1JeP5+KmjmDx2KHnHeB3AOdc3eHDoRjU1NUftR6eNUk0R3Xzzzdx8880APProozz66KMt8pSWlrJv374uaV9TQFi8cjtvb9kLwOSxQ5k7YyKfOHUk0046jsJ8nx5yLhd5cMgx9Y0JfrdqB//9l00s3VwFBAHh/15+BledPZYJo3LvNYvOuZY8OOSIfYfqWfjWFp74yya27auleNQg/u/lZ3Dl2WMp9oDgnEuS1cHBzHLytkmLrKtQcTDBtxat5umlWzl4uJGPnTKS78w+ixmTjvfrB865VmVtcOjfvz+VlZU5t2x30/scYgWFfPPXq3jy9UPE8jZz9bkncOsnizlr3LBMN9E51wdkbXAYP3485eXl7NoVvGiutra2+c6fbLf3MNz3/DY2V9Uy46R8vnvjpygamht9d851jawNDgUFBUetXBqPx5k2bVoGW9T9Nuyq4RvPruIvGyo5d/wwfv3ZEirLlntgcM51WNYGh1xSW9/IvD+V8cOXN9KvII8HrzmLz553ErE8ES/LdOucc32RB4c+rrzqILc9uYzV2/bz6WnjuO/KMxk9pF+mm+Wc6+M8OPRhb2ys5O9/+jb1DQn+46YSLj6zKNNNcs5lCQ8OfZCZ8eQbm/nOb9Zw0siB/OgLJZw6enCmm+WcyyIeHPqYuoZGvvnsap5aupWLJx3PY9dPZWj/gkw3yzmXZTw49CEV+2u5/SfLeHvLXuZedBr/cOnp/iCbc65bpLWqmqSZktZJKpN0T4r0SZJel1Qn6e4U6TFJyyU9Fzn2PUlrJb0r6VeShofHJ0g6JGlF+JmfXF4u2lVdx2d++Drvba/m3z73Ee6+/AwPDM65btNucJAUA+YBVwCTgRskTU7Ktge4A3i4lWLuBN5LOvYicJaZnQOsB+6NpG0ws6nh5/b2u5Hd9tfWc9Pjb1Gxv46f/N35XHn22Ew3yTmX5dIZOZwHlJnZRjM7DCwEZkczmFmFmS0B6pNPljQeuAr4cdI5L5hZQ7j7Bu28gzpX1dY3Mue/l7J+ZzX/duNHmH7ycZluknMuB6RzzWEcsDWyXw6c34E6/gX4GtDWOzpvBZ6K7BdLWg7sBx4ws1eST5A0B5gDUFRURDweb7MRNTU17ebpbRJmzFtRx7Kdjcw5px/avob49jUdKqMv9rur5Grfvd+5pbv6nU5wSDWxbSmOtTxRmgVUmNkySaWt5LkfaAB+Gh7aDpxkZpWSpgPPSppiZvuPaoDZAmABQElJiZWWpiy+WTwep708vYmZcd+vVrFs5xa+MWsyX/xkcfsnpdDX+t2VcrXv3u/c0l39TmdaqRw4MbI/HtiWZvmfAK6WtIlgOmqGpJ80JUq6CZgFfM7CdabNrM7MKsPtZcAG4PQ068saj724np+/tYUvlZ7a6cDgnHOdlU5wWAJMlFQsqRC4HliUTuFmdq+ZjTezCeF5fzSzGyG4Awr4OnC1mR1sOkfS6PAiOJJOASYCGzvQpz7vib9s4vt/LOPakvF87fIzMt0c51wOandaycwaJM0FngdiwONmtlrS7WH6fEljgKXAUCAh6S5gcvJUUJIfAP2AF8P3LbwR3pl0IfAdSQ1AI3C7me3pdA/7mHfL9/Kd59ZwyZlFfPfTZ+fUuyicc71HWg/BmdliYHHSsfmR7R20c7eRmcWBeGT/tFbyPQM8k067sk1dQyN3/+IdRg0u5JFrzyU/ltZjKM451+X8Cele5Psvvc/6nTX8580fZdgAXxLDOZc5/qdpL/Fu+V7mv7yRv50+nosmHZ/p5jjncpwHh16gaTpp9OB+fGNW8sPnzjnX83xaqRf41z+E00m3+HSSc6538JFDhr2zdS/zX97AZ6aP56IzfDrJOdc7eHDIoKbppOOH9OcBn05yzvUiPq2UQf/6h/d5v8Knk5xzvY+PHDKkrKKG+S9v4NoSn05yzvU+Hhwy5Icvb6AwP4+vz5yU6aY451wLHhwyYPu+Qzy74kOuKzmRkYP7Zbo5zjnXggeHDHj81Q9IGPzdBadkuinOOZeSB4cetu9gPT97cwuzzhnLiSMGZro5zjmXkgeHHvaTNzdz4HAjt114aqab4pxzrfLg0INq6xv5z9c+4FOnj2byCUMz3RznnGuVB4ce9D/Lytldc5gvlfqowTnXu3lw6CENjQkW/HkjU08czvnFIzLdHOeca5MHhx7yu1U72LLnILd/6lR/u5tzrtdLKzhImilpnaQySfekSJ8k6XVJdZLuTpEek7Rc0nORYyMkvSjp/fDncZG0e8O61km6vLOd6y3MjPkvb+CU0YO4bHJRppvjnHPtajc4SIoB84ArgMnADZKSV4nbA9wBPNxKMXcC7yUduwd4ycwmAi+F+4RlXw9MAWYC/xa2oc96tWw3q7ft57YLTyEvz0cNzrneL52Rw3lAmZltNLPDwEJgdjSDmVWY2RKgPvlkSeOBq4AfJyXNBp4It58ArokcX2hmdWb2AVAWtqHPmv/yBoqG9uOaaeMy3RTnnEtLOquyjgO2RvbLgfM7UMe/AF8DhiQdLzKz7QBmtl1S0+pz44A3kupr8a0qaQ4wB6CoqIh4PN5mI2pqatrN0x027WvktbJarjujkNdffaXH689Uv3uDXO279zu3dFe/0wkOqeZBLJ3CJc0CKsxsmaTSNNuUVn1mtgBYAFBSUmKlpW0XH4/HaS9Pd/jWotUU5m/h/htKGdq/55flzlS/e4Nc7bv3O7d0V7/TmVYqB06M7I8HtqVZ/ieAqyVtIpiOmiHpJ2HaTkljAcKfFV1QX6+SSBiLV27nojNGZyQwOOdcZ6UTHJYAEyUVSyokuFi8KJ3CzexeMxtvZhPC8/5oZjeGyYuAm8Ltm4BfR45fL6mfpGJgIvBWWr3pZZZurqKiuo6rzjkh001xzrkOaXdaycwaJM0FngdiwONmtlrS7WH6fEljgKXAUCAh6S5gspntb6PofwKelvRFYAvwmbC81ZKeBtYADcCXzayx0z3MoN++u41++XlcPMlf5uOc61vSek2omS0GFicdmx/Z3kEw/dNWGXEgHtmvBC5uJe9DwEPptK23akwYv1u1gxmTjmdQP38bq3Oub/EnpLvJ0k17qKiu48qzx2a6Kc4512EeHLrJb1dup39BHjN8Ssk51wd5cOgGjQlj8UqfUnLO9V0eHLrBWx/sYXdNHVed7XcpOef6Jg8O3eC3K7cxoCDGRZNGZ7opzjnXKR4culhDY4Lfr9rBjDOPZ2ChTyk55/omDw5dLJhSOswsv0vJOdeHeXDoYs+t3M6AghilZ/hdSs65vsuDQxdqmlK6+MzjGVDYp19B4ZzLcR4cutAbG/ew58BhZp3jU0rOub7Ng0MX+u3KbQws9Ckl51zf58Ghi9SHU0qXnFlE/wKfUnLO9W0eHLrI6xsqqTpYz1U+peScywIeHLrIC2t2MLAwxqdO9wffnHN9nweHLrJ0UxXTTz7Op5Scc1nBg0MX2HeonnU7q5l+8nGZbopzznUJDw5dYPmWKsyg5OQRmW6Kc851ibSCg6SZktZJKpN0T4r0SZJel1Qn6e7I8f6S3pL0jqTVkr4dSXtK0orws0nSivD4BEmHImnzk+vrbZZtriJPMPWk4ZluinPOdYl2V4aTFAPmAZcC5cASSYvMbE0k2x7gDuCapNPrgBlmViOpAHhV0u/M7A0zuy5SxyPAvsh5G8xsamc6lAlLN1Vx5tihDPZ3NzjnskQ6I4fzgDIz22hmh4GFwOxoBjOrMLMlQH3ScTOzmnC3IPxYNI8kAdcCP+9cFzKrvjHBiq17KfHrDc65LJLOn7rjgK2R/XLg/HQrCEcey4DTgHlm9mZSlguAnWb2fuRYsaTlwH7gATN7JUW5c4A5AEVFRcTj8TbbUVNT026ezti0r5FD9Y0MOLiDeHx3l5d/rLqr331Brvbd+51buqvf6QQHpThmKY6lZGaNwFRJw4FfSTrLzFZFstzA0aOG7cBJZlYpaTrwrKQpZrY/qdwFwAKAkpISKy0tbbMd8Xic9vJ0xn++9gGwhi9c8QlOGD6gy8s/Vt3V774gV/vu/c4t3dXvdKaVyoETI/vjgW0drcjM9gJxYGbTMUn5wN8AT0Xy1ZlZZbi9DNgAnN7R+nrK0s1VjB3Wv1cGBuec66x0gsMSYKKkYkmFwPXAonQKlzQ6HDEgaQBwCbA2kuUSYK2ZlSedEwu3TwEmAhvTqa+nmRnLwoffnHMum7Q7rWRmDZLmAs8DMeBxM1st6fYwfb6kMcBSYCiQkHQXMBkYCzwRftnnAU+b2XOR4q+n5YXoC4HvSGoAGoHbzWzPsXSyu3y49xA79tf6xWjnXNZJ695LM1sMLE46Nj+yvYNguinZu8C0Nsq9OcWxZ4Bn0mlXpi3bXAVAyQR/+M05l138CeljsGxzFQMLY0waMyTTTXHOuS7lweEYLN1UxbSThpMf839G51x28W+1Tqqpa2Dtjv1M9/WUnHNZyINDJy3fUkXC8IvRzrms5MGhk5ZuqkK+2J5zLkt5cOikZZurOKNoCEP7F2S6Kc451+U8OHRCQ2OC5VuqKJngU0rOuezkwaET1u2s5sDhRn+5j3Mua3lw6ISmh9982QznXLby4NAJSzdVUTS0H+OP88X2nHPZyYNDJyzbXEXJySMI3lPknHPZx4NDB23fd4gP9x7yKSXnXFbz4NBBSzf59QbnXPbz4NBByzZXMaAgxuQThma6Kc451208OHTQ8q17OWf8MAp8sT3nXBbzb7gOSCSM9TuqfdTgnMt6Hhw6oLzqEIfqGzmjyN/f4JzLbmkFB0kzJa2TVCbpnhTpkyS9LqlO0t2R4/0lvSXpHUmrJX07kvYtSR9KWhF+royk3RvWtU7S5cfaya6yfmc1ABM9ODjnsly7rwkN3/88D7gUKAeWSFpkZmsi2fYAdwDXJJ1eB8wwsxpJBcCrkn5nZm+E6Y+Z2cNJ9U0meLf0FOAE4A+STjezxo53r2utC4PD6UWDM9wS55zrXumMHM4Dysxso5kdBhYCs6MZzKzCzJYA9UnHzcxqwt2C8GPt1DcbWGhmdWb2AVAWtiHj1u+sZtzwAQzxlVidc1mu3ZEDMA7YGtkvB85Pt4Jw5LEMOA2YZ2ZvRpLnSvoCsBT4qplVhfW9EclTHh5LLncOMAegqKiIeDzeZjtqamrazdOetzccYmQ/HXM5Pakr+t1X5Wrfvd+5pbv6nU5wSLVGRHt//R/JGEwHTZU0HPiVpLPMbBXw78CDYVkPAo8At6Zbn5ktABYAlJSUWGlpaZvtiMfjtJenLQ2NCXa++DxXTjuZ0tIzO11OTzvWfvdludp373du6a5+pzOtVA6cGNkfD2zraEVmtheIAzPD/Z1m1mhmCeBHHJk66pL6utqmyoMcbkxwul+Mds7lgHSCwxJgoqRiSYUEF4sXpVO4pNHhiAFJA4BLgLXh/thI1k8Dq8LtRcD1kvpJKgYmAm+lU193er/5YrQHB+dc9mt3WsnMGiTNBZ4HYsDjZrZa0u1h+nxJYwiuGwwFEpLuAiYDY4EnwusOecDTZvZcWPQ/S5pKMGW0CbgtLG+1pKeBNUAD8OXecqeSBKcd73cqOeeyXzrXHDCzxcDipGPzI9s7CKZ/kr0LTGulzM+3Ud9DwEPptK2nrN9ZzckjBjKgMJbppjjnXLfzJ6TTtH5njT/85pzLGR4c0lDX0MgHuw/4shnOuZzhwSENG3cdoDFhnD7Gg4NzLjd4cEjDel82wzmXYzw4pGH9zmry88Qpozw4OOdygweHNKzbUUPxqEEU5vs/l3MuN/i3XRrer6j2h9+ccznFg0M7Dh5uYMuegx4cnHM5xYNDO8oqajCDM8b49QbnXO7w4NCO9TuD11H4A3DOuVziwaEd63dWU5ifx8kjBma6Kc4512M8OLRj3Y5qThs9mPyY/1M553KHf+O14/2d1f7wm3Mu53hwaMP+2nq27av1ZTOccznHg0Mbml7w4wvuOedyjQeHNjTdqeTPODjnco0Hhzas21HNwMIY44YPyHRTnHOuR6UVHCTNlLROUpmke1KkT5L0uqQ6SXdHjveX9JakdyStlvTtSNr3JK2V9K6kX0XeNT1B0iFJK8LP/OT6esr6ndVMPH4weXnKVBOccy4j2g0O4fuf5wFXELwX+gZJk5Oy7QHuAB5OOl4HzDCzc4GpwExJfxWmvQicZWbnAOuBeyPnbTCzqeHn9g72qcus31njU0rOuZyUzsjhPKDMzDaa2WFgITA7msHMKsxsCVCfdNzMrCbcLQg/Fqa9YGYNYdobpH4HdcZU1tSxu6aOM/xOJedcDspPI884YGtkvxw4P90KwpHHMuA0YJ6ZvZki263AU5H9YknLgf3AA2b2Sopy5wBzAIqKiojH4222o6ampt08Ue9VNgJwaMdG4vEtaZ/X23S039kkV/vu/c4t3dXvdIJDqgl3S7cCM2sEpobXFH4l6SwzW9VcuHQ/0AD8NDy0HTjJzColTQeelTTFzPYnlbsAWABQUlJipaWlbbYjHo/TXp6oLa9vAlbzmcs+yZhh/dM+r7fpaL+zSa723fudW7qr3+lMK5UDJ0b2xwPbOlqRme0F4sDMpmOSbgJmAZ8zs6bppjozqwy3lwEbgNM7Wt+xWrejmqH98yka2q+nq3bOuYxLJzgsASZKKpZUCFwPLEqncEmjI3chDQAuAdaG+zOBrwNXm9nBpHNi4fYpwERgY9o96iLvhxejJb9TyTmXe9qdVjKzBklzgeeBGPC4ma2WdHuYPl/SGGApMBRISLqL4M6mscAT4Zd9HvC0mT0XFv0DoB/wYvgF/EZ4Z9KFwHckNQCNwO1mtqfLepwGM2PdzmquOmdsT1brnHO9RjrXHDCzxcDipGPzI9s7SH230bvAtFbKPK2V488Az6TTru5SU9fAvkP1vky3cy5n+RPSKeyqrgPgeL/e4JzLUR4cUmgKDqMH9927lJxz7lh4cEihwkcOzrkc58EhhSMjBw8Ozrnc5MEhhYrqOgpiYvjAgkw3xTnnMsKDQwq7qusYPbifP+PgnMtZHhxSqKiuZfRQvxjtnMtdHhxSaBo5OOdcrvLgkMKu6jq/U8k5l9M8OCSpb0yw5+BhHzk453KaB4cklTWHMYPRQzw4OOdylweHJM1LZ3hwcM7lMA8OSSqqawEfOTjncpsHhyRHFt3zW1mdc7nLg0OSpnWVRg0uzHBLnHMuczw4JNlVXcfwgQX0y49luinOOZcxaQUHSTMlrZNUJumeFOmTJL0uqU7S3ZHj/SW9JekdSaslfTuSNkLSi5LeD38eF0m7N6xrnaTLj7WTHVFRXeu3sTrncl67wSF8xec84AqCV3/eIGlyUrY9wB3Aw0nH64AZZnYuMBWYKemvwrR7gJfMbCLwUrhPWPb1wBRgJvBvTe+U7gn+AJxzzqU3cjgPKDOzjWZ2GFgIzI5mMLMKM1sC1CcdNzOrCXcLwo+F+7OBJ8LtJ4BrIscXmlmdmX0AlIVt6BEVvnSGc86lFRzGAVsj++XhsbRIiklaAVQAL5rZm2FSkZltBwh/Ht8V9R0LMwtHDn6nknMut+WnkSfVutWW4lhKZtYITJU0HPiVpLPMbNWx1idpDjAHoKioiHg83mY7ampq2s1zoN6oa0iwb2c58fjONvP2Fen0O1vlat+937mlu/qdTnAoB06M7I8HtnW0IjPbKylOcB1hFbBT0lgz2y5pLMHIIu36zGwBsACgpKTESktL26w/Ho/TXp6yihp46WU+Pm0ypVN7ZLDS7dLpd7bK1b57v3NLd/U7nWmlJcBEScWSCgkuFi9Kp3BJo8MRA5IGAJcAa8PkRcBN4fZNwK8jx6+X1E9SMTAReCud+o6Vvx7UOecC7Y4czKxB0lzgeSAGPG5mqyXdHqbPlzQGWAoMBRKS7iK4s2ks8ER4t1Ee8LSZPRcW/U/A05K+CGwBPhOWt1rS08AaoAH4cjg11e2als7wu5Wcc7kunWklzGwxsDjp2PzI9g6C6Z9k7wLTWimzEri4lbSHgIfSaVtXOjJy8AvSzrnc5k9IR+yqrqMwP4+hA9KKmc45l7U8OEQ0vR5USnXDlHPO5Q4PDhEV/nS0c84BHhyOssufjnbOOcCDw1Eqqmt95OCcc3hwaHa4IUHVwXq/U8k55/Dg0Gx3TdMb4Hzk4JxzHhxC/nS0c84d4cEhVFHtIwfnnGviwSHUPHIY4sHBOec8OISa1lUa5dNKzjnnwaHJruo6RgwqpCDm/yTOOeffhCF/Pahzzh3hwSG0y5fOcM65Zh4cQr50hnPOHeHBATCzIDj4yME55wAPDgDsO1TP4caEjxyccy6UVnCQNFPSOkllku5JkT5J0uuS6iTdHTl+oqQ/SXpP0mpJd0bSnpK0IvxskrQiPD5B0qFI2vzk+rraruYH4HxdJeecgzReExq+/3kecClQDiyRtMjM1kSy7QHuAK5JOr0B+KqZvS1pCLBM0otmtsbMrovU8QiwL3LeBjOb2pkOdUaFL53hnHNHSWfkcB5QZmYbzewwsBCYHc1gZhVmtgSoTzq+3czeDrergfeAcdE8Cl67di3w80734hjt8qUznHPuKOm8LHkcsDWyXw6c39GKJE0ApgFvJiVdAOw0s/cjx4olLQf2Aw+Y2SspypsDzAEoKioiHo+3WX9NTU2reV7/IIhp61YsYWtBdr0itK1+Z7tc7bv3O7d0V7/TCQ6pvi2tI5VIGgw8A9xlZvuTkm/g6FHDduAkM6uUNB14VtKU5PPMbAGwAKCkpMRKS0vbbEM8Hqe1PK8dWEP/jZu54pLSrHt/dFv9zna52nfvd27prn6nM61UDpwY2R8PbEu3AkkFBIHhp2b2y6S0fOBvgKeajplZnZlVhtvLgA3A6enW1xm7qusYPaRf1gUG55zrrHSCwxJgoqRiSYXA9cCidAoPryf8B/CemT2aIsslwFozK4+cMzq8CI6kU4CJwMZ06uusiuo6jh/idyo551yTdqeVzKxB0lzgeSAGPG5mqyXdHqbPlzQGWAoMBRKS7gImA+cAnwdWNt2qCtxnZovD7etpeSH6QuA7khqARuB2M9tzDH1s167qOk4dPbg7q3DOuT4lnWsOhF/mi5OOzY9s7yCYbkr2KqmvWTSdd3OKY88QTEP1mIrqOj526sierNI553q1nH9Cuq6hkX2H6v0ZB+eci8j54ODPODjnXEseHPz1oM4510LOB4empTP8biXnnDsi54ODjxycc66lnA8OFdV1SDByUGGmm+Kcc71GzgeHXdV1jBxUSH4s5/8pnHOuWc5/I+6qrmW0X29wzrmjeHAI11Vyzjl3RM4Hh2BdJQ8OzjkXldPBIZEwdtf4yME555LldHDYe6ie+kbzkYNzziXJ6eDgzzg451xqOR0cCmLiqrPHcsooX67bOeei0lqyO1udMnow8z73kUw3wznnep2cHjk455xLzYODc865FtIKDpJmSlonqUzSPSnSJ0l6XVKdpLsjx0+U9CdJ70laLenOSNq3JH0oaUX4uTKSdm9Y1zpJlx9rJ51zznVMu9ccJMWAecClQDmwRNIiM1sTybYHuAO4Jun0BuCrZva2pCHAMkkvRs59zMweTqpvMsG7pacAJwB/kHS6mTV2vHvOOec6I52Rw3lAmZltNLPDwEJgdjSDmVWY2RKgPun4djN7O9yuBt4DxrVT32xgoZnVmdkHQFnYBueccz0knbuVxgFbI/vlwPkdrUjSBGAa8Gbk8FxJXwCWEowwqsL63kiqr0VAkTQHmANQVFREPB5vs/6ampp282SjXO035G7fvd+5pbv6nU5wUIpj1pFKJA0GngHuMrP94eF/Bx4My3oQeAS4Nd36zGwBsACgpKTESktL22xDPB6nvTzZKFf7Dbnbd+93bumufqczrVQOnBjZHw9sS7cCSQUEgeGnZvbLpuNmttPMGs0sAfyII1NHx1Sfc865Y5fOyGEJMFFSMfAhwcXiz6ZTuCQB/wG8Z2aPJqWNNbPt4e6ngVXh9iLgZ5IeJbggPRF4q616li1btlvS5naaMwrYnU67s0yu9htyt+/e79xyLP0+ubUEmbU/QxTeZvovQAx43MweknQ7gJnNlzSG4LrBUCAB1ACTgXOAV4CV4XGA+8xssaQngakEU0abgNuagoWk+wmmmBoIpqJ+17H+puzDUjMrOdZy+ppc7Tfkbt+937mlu/qdVnDIBv4/Tu7J1b57v3NLd/Xbn5B2zjnXQi4FhwWZbkCG5Gq/IXf77v3OLd3S75yZVnLOOZe+XBo5OOecS5MHB+eccy3kRHBob1XZbCTpcUkVkla1nzt7tLUScDaT1F/SW5LeCfv97Uy3qSdJiklaLum5TLelJ0naJGlluLL10i4tO9uvOYSryq4nsqoscEPSqrJZR9KFBM+b/LeZnZXp9vQUSWOBsdGVgIFrcuC/t4BBZlYTrkrwKnCnmb3RzqlZQdI/ACXAUDOblen29BRJm4ASM+vyh/9yYeTQ7qqy2cjM/kywlHpO6eRKwH2eBWrC3YLwk91/+YUkjQeuAn6c6bZkk1wIDqlWlc36LwvX6krAWSucWlkBVAAvmllO9Jtg9YavcWQVhlxiwAuSloUrVXeZXAgOx7yqrOt7WlkJOKuFC1lOJVis8jxJWT+dKGkWUGFmyzLdlgz5hJl9BLgC+HI4ndwlciE4+CqvOaa1lYBzhZntBeLAzMy2pEd8Arg6nHtfCMyQ9JPMNqnnmNm28GcF8Cu68MVouRAcmleVlVRIsKrsogy3yXWTtlYCzmaSRksaHm4PAC4B1ma0UT3AzO41s/FmNoHgd/uPZnZjhpvVIyQNCm+6QNIg4DKOrG59zLI+OJhZAzAXeJ7g4uTTZrY6s63qfpJ+DrwOnCGpXNIXM92mHvIJ4PMEf0GuCD9XZrpRPWAs8CdJ7xL8QfSimeXUbZ05qAh4VdI7BK81+K2Z/b6rCs/6W1mdc851XNaPHJxzznWcBwfnnHMteHBwzjnXggcH55xzLXhwcM4514IHB+daIalI0s8kbQyXJ3hd0qcz3S7neoIHB+dSCB+mexb4s5mdYmbTCR6yGp/RhjnXQ/w5B+dSkHQx8E0z+1SKtAnAk8Cg8NBcM/uLpFLg28BOYCrwS2AlcCcwgGDp8A2S/gs4BEwCTgZuAW4CPga8aWY3h/X8O/DR8Nz/MbN/7PqeOpdafqYb4FwvNQV4u5W0CuBSM6uVNBH4OcG7BADOBc4kWC59I/BjMzsvfOnQ/wHuCvMdB8wArgZ+Q/Bk998BSyRNNbMVwP1mtid8J8lLks4xs3e7uJ/OpeTTSs6lQdK88C1rSwjelfAjSSuBXwCTI1mXhO+UqAM2AC+Ex1cCEyL5fmPBsH0lsNPMVppZAlgdyXetpLeB5QTBKlqPc93KRw7OpbYa+F9NO2b2ZUmjgKXAVwimjs4l+AOrNnJeXWQ7EdlPcPTvW12KPM35JBUDdwMfNbOqcCqq/zH2ybm0+cjBudT+CPSX9KXIsYHhz2HA9vAv/c8DsW6ofyhwANgnqYhgvX7neoyPHJxLwcxM0jXAY5K+Buwi+LL+OsG1iGckfQb4U3i8q+t/R9JyghHMRuC1rq7Dubb43UrOOeda8Gkl55xzLXhwcM4514IHB+eccy14cHDOOdeCBwfnnHMteHBwzjnXggcH55xzLfx/V4A8bbKVLbMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma_vals = ee.List.sequence(0.1, 5, 0.1)\n",
    "\n",
    "\n",
    "def gammas(t):\n",
    "    svrRegression = ee.Classifier.libsvm(svmType ='EPSILON_SVR',kernelType='RBF',gamma=t,cost=100).setOutputMode('REGRESSION').train(\n",
    "      features= training, \n",
    "      classProperty= 'SM', \n",
    "      inputProperties= predictionBands\n",
    "    )\n",
    "    \n",
    "    svrRegressionTesting = testing.classify(svrRegression, 'y_hat');\n",
    "\n",
    "    # // Calculate the RMSE for SVM regression and print the result\n",
    "    regression=svrRegressionTesting.map(set_sqError)\n",
    "    regression = ee.FeatureCollection(regression)\n",
    "    MSE = regression.reduceColumns(ee.Reducer.mean(),['sqError'])\n",
    "    RMSE =  ee.Number(MSE.get('mean')).sqrt()        \n",
    "    return RMSE\n",
    "\n",
    "\n",
    "rmse_gama=gamma_vals.map(gammas)\n",
    "value_info = rmse_gama.getInfo()\n",
    "\n",
    "# Extracting gamma value for minimum RMSE\n",
    "min_rmse=min(value_info)\n",
    "print(\"Min RMSE =\", min_rmse)\n",
    "\n",
    "index=value_info.index(min(value_info))\n",
    "gamma_range = gamma_vals.getInfo()\n",
    "min_gamma =gamma_range[index]\n",
    "print(\"Min Gamma =\", min_gamma)\n",
    "\n",
    "# print(rmse_gama.getInfo())\n",
    "\n",
    "# Plotting the interations\n",
    "import pandas as pd\n",
    "df =pd.DataFrame(value_info,columns=['RMSE'])\n",
    "df['Gamma'] = gamma_vals.getInfo() \n",
    "\n",
    "ax =df.plot.line(x='Gamma', \n",
    "             y='RMSE',\n",
    "             title= 'Impact of Gamma in SVR'\n",
    "             )\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "same for cart regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min RMSE = 0.10805697357978562\n",
      "Min Leaf = 11\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "leaf_vals = ee.List.sequence(1, 150, 5)\n",
    "\n",
    "def leafs(t):\n",
    "    cartRegression = ee.Classifier.smileCart(minLeafPopulation=t).setOutputMode('REGRESSION').train(\n",
    "    features= training, \n",
    "    classProperty= 'SM', \n",
    "    inputProperties= predictionBands\n",
    "    )\n",
    "\n",
    "    cartRegressionTesting = testing.classify(cartRegression, 'y_hat');\n",
    "\n",
    "    # // Calculate the RMSE for CART regression and print the result\n",
    "    regression=cartRegressionTesting.map(set_sqError)\n",
    "    regression = ee.FeatureCollection(regression)\n",
    "    MSE = regression.reduceColumns(ee.Reducer.mean(),['sqError'])\n",
    "    RMSE =  ee.Number(MSE.get('mean')).sqrt()        \n",
    "    return RMSE\n",
    "\n",
    "\n",
    "rmse_leaf=leaf_vals.map(leafs)\n",
    "value_info = rmse_leaf.getInfo()\n",
    "\n",
    "# Extracting gamma value for minimum RMSE\n",
    "min_rmse=min(value_info)\n",
    "print(\"Min RMSE =\", min_rmse)\n",
    "\n",
    "index=value_info.index(min(value_info))\n",
    "leaf_range = leaf_vals.getInfo()\n",
    "min_leaf =leaf_range[index]\n",
    "print(\"Min Leaf =\", min_leaf)\n",
    "\n",
    "# print(rmse_gama.getInfo())\n",
    "\n",
    "# Plotting the interations\n",
    "import pandas as pd\n",
    "df =pd.DataFrame(value_info,columns=['RMSE'])\n",
    "df['Leaf'] = leaf_vals.getInfo() \n",
    "\n",
    "ax =df.plot.line(x='Leaf', \n",
    "             y='RMSE',\n",
    "             title= 'Impact of Leaf numbers in Cart'\n",
    "             )\n",
    "ax.grid()"
   ]
  },
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