From 73374c0f04b416c6bc27c6fd928c3067ed2230ae Mon Sep 17 00:00:00 2001 From: Qiwei Han Date: Thu, 21 Sep 2017 09:22:04 -0400 Subject: [PATCH] Initial commit --- 0_install/unix_environment.yml | 295 + 0_install/win_environment.yml | 270 + 1_input/aoi_ids.csv | 124 + 1_input/labels.csv | 341 + 2_processing/features_table.csv | 11725 ++++++++++++++++++++++++ 3_output/aoi_ids.csv | 123 + 3_output/nonvegetation.dbf | Bin 0 -> 12511 bytes 3_output/nonvegetation.shp | Bin 0 -> 40764 bytes 3_output/nonvegetation.shx | Bin 0 -> 876 bytes 3_output/output_model_aoi_ids.csv | 123 + 3_output/output_model_aoi_summary.csv | 4 + 3_output/trees.dbf | Bin 0 -> 2431 bytes 3_output/trees.shp | Bin 0 -> 4540 bytes 3_output/trees.shx | Bin 0 -> 236 bytes 3_output/vegetation.dbf | Bin 0 -> 1297 bytes 3_output/vegetation.shp | Bin 0 -> 12484 bytes 3_output/vegetation.shx | Bin 0 -> 164 bytes README.md | 48 + classify.ipynb | 560 ++ classify.py | 322 + create_features.ipynb | 2458 +++++ create_features.py | 359 + 22 files changed, 16752 insertions(+) create mode 100644 0_install/unix_environment.yml create mode 100644 0_install/win_environment.yml create mode 100644 1_input/aoi_ids.csv create mode 100644 1_input/labels.csv create mode 100644 2_processing/features_table.csv create mode 100644 3_output/aoi_ids.csv create mode 100644 3_output/nonvegetation.dbf create mode 100644 3_output/nonvegetation.shp create mode 100644 3_output/nonvegetation.shx create mode 100644 3_output/output_model_aoi_ids.csv create mode 100644 3_output/output_model_aoi_summary.csv create mode 100644 3_output/trees.dbf create mode 100644 3_output/trees.shp create mode 100644 3_output/trees.shx create mode 100644 3_output/vegetation.dbf create mode 100644 3_output/vegetation.shp create mode 100644 3_output/vegetation.shx create mode 100644 README.md create mode 100644 classify.ipynb create mode 100644 classify.py create mode 100644 create_features.ipynb create mode 100644 create_features.py diff --git a/0_install/unix_environment.yml b/0_install/unix_environment.yml new file mode 100644 index 0000000..097292c --- /dev/null +++ b/0_install/unix_environment.yml @@ -0,0 +1,295 @@ +name: green-roofs +channels: +- ioam +- conda-forge +- defaults +dependencies: +- affine=2.1.0=py35_0 +- alabaster=0.7.10=py35_0 +- anaconda-client=1.6.3=py35_0 +- anaconda-project=0.7.1=py35_0 +- asn1crypto=0.22.0=py35_0 +- astroid=1.5.3=py35_0 +- astropy=2.0.1=np113py35_1 +- attrs=17.2.0=py35_0 +- babel=2.4.0=py35_0 +- beautifulsoup4=4.6.0=py35_0 +- biggus=0.15.0=py35_0 +- bitarray=0.8.1=py35_0 +- bkcharts=0.2=py35_0 +- blas=1.1=openblas +- blaze=0.11.3=py35_0 +- bleach=1.5.0=py35_0 +- bokeh=0.12.6=py35_0 +- boost=1.64.0=py35_4 +- boost-cpp=1.64.0=1 +- boto3=1.4.4=py35_0 +- botocore=1.5.92=py35_0 +- bottleneck=1.2.1=np113py35_0 +- bzip2=1.0.6=1 +- ca-certificates=2017.7.27.1=0 +- cairo=1.14.6=4 +- cartopy=0.15.1=np113py35_2 +- certifi=2017.7.27.1=py35_0 +- cf_units=1.1.3=py35_1 +- cffi=1.10.0=py35_0 +- chardet=3.0.4=py35_0 +- click=6.7=py35_0 +- click-plugins=1.0.3=py35_0 +- cligj=0.4.0=py35_0 +- cloudpickle=0.4.0=py35_0 +- clyent=1.2.2=py35_0 +- colorama=0.3.9=py35_0 +- contextlib2=0.5.5=py35_0 +- cryptography=1.9=py35_0 +- curl=7.52.1=0 +- cycler=0.10.0=py35_0 +- cython=0.26=py35_0 +- cytoolz=0.8.2=py35_0 +- dask=0.15.1=py35_0 +- datashape=0.5.4=py35_0 +- decorator=4.1.2=py35_0 +- descartes=1.1.0=py35_0 +- distributed=1.18.0=py35_0 +- docutils=0.14=py35_0 +- entrypoints=0.2.3=py35_1 +- et_xmlfile=1.0.1=py35_0 +- expat=2.1.0=3 +- fastcache=1.0.2=py35_0 +- fiona=1.7.9=py35_0 +- flask=0.12.2=py35_0 +- flask-cors=3.0.3=py35_0 +- fontconfig=2.12.1=4 +- freetype=2.7=1 +- freexl=1.0.2=2 +- gdal=2.1.4=py35_1 +- geopandas=0.2.1=py35_4 +- geos=3.5.1=1 +- gevent=1.2.2=py35_0 +- giflib=5.1.4=0 +- glib=2.51.4=0 +- gmp=6.1.2=0 +- gmpy2=2.0.8=py35_1 +- greenlet=0.4.12=py35_0 +- h5netcdf=0.3.1=py35_0 +- h5py=2.7.0=np113py35_1 +- hdf4=4.2.12=0 +- hdf5=1.8.18=1 +- heapdict=1.0.0=py35_0 +- 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- geopy==1.11.0 + - ipython-genutils==0.2.0 + - jupyter-client==5.1.0 + - jupyter-console==5.1.0 + - jupyter-core==4.3.0 + - lancet-ioam==0.9.0 + - mo-pack==0.2.0 + - nc-time-axis==1.0.2 + - prompt-toolkit==1.0.15 + - ruamel-yaml==0.11.14 + - tables==3.4.2 diff --git a/0_install/win_environment.yml b/0_install/win_environment.yml new file mode 100644 index 0000000..5e98458 --- /dev/null +++ b/0_install/win_environment.yml @@ -0,0 +1,270 @@ +name: greenroofs +channels: +- ioam +- conda-forge +- defaults +dependencies: +- affine=2.1.0=py35_0 +- alabaster=0.7.10=py35_0 +- anaconda-client=1.6.3=py35_0 +- anaconda-project=0.7.1=py35_0 +- asn1crypto=0.22.0=py35_0 +- astroid=1.5.3=py35_0 +- astropy=2.0.1=np113py35_1 +- attrs=17.2.0=py35_0 +- babel=2.4.0=py35_0 +- beautifulsoup4=4.6.0=py35_0 +- biggus=0.15.0=py35_0 +- bitarray=0.8.1=py35_0 +- bkcharts=0.2=py35_0 +- bleach=1.5.0=py35_0 +- bokeh=0.12.6=py35_0 +- boto3=1.4.4=py35_0 +- botocore=1.5.92=py35_0 +- bottleneck=1.2.1=np113py35_0 +- bzip2=1.0.6=vc14_1 +- ca-certificates=2017.7.27.1=0 +- cartopy=0.15.1=np113py35_2 +- certifi=2017.7.27.1=py35_0 +- cf_units=1.1.3=py35_1 +- cffi=1.10.0=py35_0 +- chardet=3.0.4=py35_0 +- click=6.7=py35_0 +- click-plugins=1.0.3=py35_0 +- cligj=0.4.0=py35_0 +- cloudpickle=0.4.0=py35_0 +- clyent=1.2.2=py35_0 +- colorama=0.3.9=py35_0 +- contextlib2=0.5.5=py35_0 +- cryptography=1.9=py35_0 +- cycler=0.10.0=py35_0 +- cython=0.26=py35_0 +- cytoolz=0.8.2=py35_0 +- dask=0.15.1=py35_0 +- datashape=0.5.4=py35_0 +- decorator=4.1.2=py35_0 +- descartes=1.1.0=py35_0 +- distributed=1.18.0=py35_0 +- docutils=0.14=py35_0 +- entrypoints=0.2.3=py35_1 +- et_xmlfile=1.0.1=py35_0 +- expat=2.1.0=vc14_3 +- fastcache=1.0.2=py35_0 +- fiona=1.7.9=py35_0 +- flask=0.12.2=py35_0 +- flask-cors=3.0.3=py35_0 +- freetype=2.7=vc14_1 +- freexl=1.0.2=vc14_2 +- geopandas=0.2.1=py35_4 +- geos=3.5.1=vc14_1 +- gevent=1.2.2=py35_0 +- greenlet=0.4.12=py35_0 +- h5netcdf=0.3.1=py35_0 +- h5py=2.7.1=py35_1 +- hdf4=4.2.12=vc14_0 +- 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z=&Sd|682y^`vDBKCh@WzJN4f5n_UwTFH--q>gF zi|GAN)yF=)N9z4;m%|&?KJRUxiSb{dm|^-!F#1Y?OJ{MOBM6mr}EgB z3@ab9SImBH-<5HnGG?E(_eS(KrhUCnDt}}3Imh%qnG+lQr7`!nR+4i+wfV_+JT1jb z(mR!@ef)M4OnxQQ-s{=lo2fqMi!t;2M#<^#fXbtPBZqDN^IcX$*gLJ!KJyp%{}?g% zw+fZVUwThv9QNDy0kmiDtDIMdCFeURaZa}(hwVL_@8HCIH}|c5FTj0Q?Q!2eZtv;j zo7A7a8>>`(^m6ra-`A^sAMg|354=o$y-(Wr0gP9*?+^UHPw?L_+wI(e6G4h=JB&eWL> literal 0 HcmV?d00001 diff --git a/README.md b/README.md new file mode 100644 index 0000000..ae38b6a --- /dev/null +++ b/README.md @@ -0,0 +1,48 @@ +# README + +## Identifying Green Rooftops in Rotterdam to Improve Urban Planning + +### Overview + +DSSG has partnered with the Municipality of Rotterdam to identify green rooftops in Rotterdam for a given Area of Interest (AoI). In our case, our AoI was the city center.AoI). In our case, our AoI was the city center.AoI). In our case, our AoI was the city center.AoI). In our case, our AoI was the city center. + +To achieve this goal, we build a multi-class classification model for rooftops in Rotterdam, classifying them in ‘non-vegetation’, ‘vegetation,’ and ‘trees.’ Our model is used to produce three deliverables for the Municipality of Rotterdam: + +1. a ‘Raw Table’ with the predicted classification for each rooftop in AoI, including its prediction probability and corresponding area in square meters;corresponding area in square meters; + +2. a ‘Summary Table,’ which is a ‘Raw Table’ grouped by type of classification; information is displayed in by area and by rooftop; and, + +3. a ‘Visualization’ that identifies the predicted classifications in a map of the AoI with colored shapefiles for each building footprint (i.e., grey, light green, and dark green for non-vegetation, vegetation, and trees, respectively); this file can be opened with QGIS, a free and open source geographic information system software. + +### Installation + +To set up a Python environment, go to https://conda.io/miniconda.html. Choose the Miniconda installer (Python 3.6) according to your operating system. + +Once Miniconda is installed, use the ‘conda’ command to create an environment called ‘green-roofs’ with the file ‘environment.yml’. When you create an environment or install package, conda will ask you to proceed (i.e., ‘proceed ([y]/n)?‘). Type ‘y’. + +To create the environment and install the necessary packages, open the terminal inside folder '0_install' and type: +- For Windows: ‘conda env create -f win_environment.yml -n greenroofs’; +- For Linux/macOS: ‘conda env create -f unix_environment.yml -n greenroofs’. + +To activate the environment, type ‘activate greenroofs’ (Windows) or ‘source activate greenroofs’ (macOS and Linux). To deactivate the environment, type ‘deactivate’ (Windows) or ‘source deactivate’ (macOS and Linux). + +You now have a working Python environment. + +### Instructions + +1. Open the terminal and activate the environment; + +2. Open ‘create_features.py’: + + 1. Set ‘BAG_Panden’ to the file with the building footprint polygons; + 2. Set ‘CIR’ to the file with the color-infrared imagery; + 3. Save ‘create_features.py’; + +3. Run ‘create_features.py’ (i.e., type ‘python create_features.py’ in the command line); + +4. Open ‘classify.py’ and: + + 1. Save the IDs within the selected area of interest as ‘aoi_ids.csv’ and set it as the variable ‘AoI’; + 2. Save ‘classify.py’; and, + +5. Run ‘classify.py’ (i.e., type ‘python classify.py in the command line’). diff --git a/classify.ipynb b/classify.ipynb new file mode 100644 index 0000000..0fbc3cd --- /dev/null +++ b/classify.ipynb @@ -0,0 +1,560 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "AoI_csv_dir = 'input/aoi_ids.csv'\n", + "label_csv_dir = 'input/labels.csv'\n", + "features_csv_dir = 'processing/features_table.csv'\n", + "BAG_Panden = '/home/data/citycentre/BAG_Panden.shp'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import pairwise_distances_argmin\n", + "from sklearn.datasets import load_sample_image\n", + "from sklearn.utils import shuffle\n", + "from time import time\n", + "#from PIL import Image\n", + "import pandas as pd\n", + "from shutil import copyfile\n", + "import matplotlib.image as mpimg\n", + "import numpy\n", + "import geopandas as gpd\n", + "import fiona\n", + "import rasterio\n", + "import rasterio.mask\n", + "from pandas.tools.plotting import scatter_matrix\n", + "import matplotlib.pyplot as plt\n", + "from time import time\n", + "from scipy.stats import randint\n", + "from sklearn import preprocessing\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import roc_auc_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.model_selection import RandomizedSearchCV\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.linear_model import SGDClassifier\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.svm import SVC\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.mixture import GaussianMixture\n", + "from sklearn.svm import LinearSVC\n", + "from sklearn.cluster import MeanShift\n", + "from sklearn.manifold import LocallyLinearEmbedding\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import pairwise_distances_argmin\n", + "from sklearn.datasets import load_sample_image\n", + "from sklearn.utils import shuffle\n", + "from shapely.geometry import shape\n", + "from shapely.geometry import Polygon\n", + "import shapefile\n", + "import shutil\n", + "import os\n", + "import glob" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def ids_to_shp_visualization(ids_list, directory, name, shape_file=BAG_Panden):\n", + "\n", + " i=0\n", + " if not os.path.exists(directory+\"/temporary\"):\n", + " os.makedirs(directory+\"/temporary\")\n", + " \n", + " for identifica in ids_list:\n", + " for feat in fiona.open(shape_file, \"r\"):\n", + " if identifica==feat['properties']['Identifica']:\n", + " try:\n", + " \n", + " feat2=feat['properties']['Identifica']\n", + " feat1=[feat['geometry']]\n", + " area_per_roof = feat['properties']['SHAPE_Area']\n", + "\n", + " with fiona.open(shape_file, \"r\") as shapef:\n", + " meta=shapef.meta\n", + " with fiona.open(directory+'/temporary/'+str(i)+'.shp', 'w', **meta) as sink:\n", + " sink.write(feat)\n", + " \n", + " i=i+1\n", + " #if i==1:\n", + " # break\n", + " except ValueError:\n", + " continue\n", + " \n", + " files = glob.glob(directory+\"/temporary/*.shp\")\n", + " w = shapefile.Writer()\n", + " for f in files:\n", + " r = shapefile.Reader(f)\n", + " w._shapes.extend(r.shapes())\n", + " w.records.extend(r.records())\n", + " w.fields = list(r.fields)\n", + " w.save(directory+\"/\"+name+\".shp\")\n", + " shutil.rmtree(directory+\"/temporary/\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The IDs have been added.\n" + ] + } + ], + "source": [ + "aoi_list = []\n", + "[aoi_list.append(ID) for ID in pd.read_csv(AoI_csv_dir, dtype=str).ID]\n", + "print('The IDs have been added.')" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(340, 1)" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label_df = pd.read_csv(label_csv_dir, dtype={'ID':str}).set_index('ID')\n", + "label_df.label = label_df.label.replace(3,2)\n", + "label_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features_df = pd.read_csv(features_csv_dir, dtype={'ID':str}).set_index('ID')\n", + "features_df.loc(['0599100010050372'])" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "features_with_label = pd.concat([features_df, label_df], axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [], + "source": [ + "# Split-out validation dataset\n", + "\n", + "X_train = features_with_label.loc[label_df.index].drop('label', 1).dropna()\n", + "y_train = features_with_label.loc[label_df.index].dropna().label\n", + "X_test = features_with_label.loc[aoi_list].drop('label', 1).dropna()\n", + "\n", + "#print(X_train.shape)\n", + "#print(y_train.shape)\n", + "#print(X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "#y_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "seed = 0\n", + "scoring = 'accuracy'" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "models = []\n", + "models.append(('GBC', GradientBoostingClassifier()))\n", + "models.append(('LR', LogisticRegression()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('SVM', SVC()))\n", + "models.append(('SGD', SGDClassifier()))\n", + "models.append(('LSVM', LinearSVC()))" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GBC: 0.855663 (0.086274)\n", + "LR: 0.826295 (0.099712)\n", + "NB: 0.802766 (0.116554)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\n", + " warnings.warn(\"Variables are collinear.\")\n", + "/opt/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\n", + " warnings.warn(\"Variables are collinear.\")\n", + "/opt/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\n", + " warnings.warn(\"Variables are collinear.\")\n", + "/opt/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:455: UserWarning: The priors do not sum to 1. Renormalizing\n", + " UserWarning)\n", + "/opt/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\n", + " warnings.warn(\"Variables are collinear.\")\n", + "/opt/anaconda/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\n", + " warnings.warn(\"Variables are collinear.\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LDA: 0.808472 (0.103322)\n", + "KNN: 0.811326 (0.071435)\n", + "CART: 0.761062 (0.078024)\n", + "SVM: 0.606936 (0.179698)\n", + "SGD: 0.381080 (0.125629)\n", + "LSVM: 0.596971 (0.237643)\n" + ] + }, + { + "data": { + "image/png": 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n3AIAADA84RYAAIDhueZ2TlW10SUctG3btm10CQCHhNFfM7xeADAi4XYOW2EyCgCWYxmv\nGVXltQkAVjAsGQAAgOEJtwAAAAxPuAUAAGB4wi0AAADDM6EUwxl9FtLETKQAALDehFuGYhZSAABg\nNYYlAwAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4JpQCALYsM+wDm9n27duze/fuhe5j0f8Ht23bll27\ndi10H/MSbgGALckM+8Bmt3v37uH/h2ymDxENSwYAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAA\nhme2ZIAtYjPNVnggDtWvOznQ87bW7UafjRMA9ke4BdgCFh1cfN3J4vi9AsD6MCwZAACA4Qm3AAAA\nDE+4BQAAYHjCLQAAAMMzoRRb3oHMRGoW0vmMPjtvcujO0AuwTNu3b8/u3bsXvp9Fvy5t27Ytu3bt\nWug+gAMn3LLlHarBc9GW8Xs1Qy/A1rB79+4t8f98K3yoC1uZYckAAAAMT7gFAABgeMItAAAAwxNu\nAQAAGJ5wCwAAwPCEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAMT7gFAABgeMItAAAAwzt8owsAYPmq\nainbdPeatwEAOBDCLcAhSOgEALYaw5IBAAAYnnALAADA8IRbAAAAhifcAgAAMDwTSgFLY4ZeYDM7\nkP83B7Kd/1EAiyHcAkvjDR2wmfkfBTA2w5IBAAAYnnALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEJ\ntwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEJtwAAAAxP\nuAUAAGB4wi0AAADDW2q4rar7V9V7qurCqnrKXtrcu6reXlUXVNU/LLM+AAAAxnT4snZUVYclOS3J\n9yS5JMnbqurs7n7XTJsbJnlekvt394eq6uuWVR8AAADjWmbP7V2TXNjdF3X3lUnOSvKgFW0enuSV\n3f2hJOnujy+xPgAAAAa1zHB7bJKLZ+5fMl026xuTbKuqN1XVuVX1yNUeqKpOrqqdVbXz0ksvXVC5\nAAAAjGKzTSh1eJK7JPm+JPdL8itV9Y0rG3X36d29o7t3HHPMMcuuEQAAgE1madfcJvlwkuNm7t9s\numzWJUk+2d2fTfLZqnpzkjsmee9ySgQAAGBEy+y5fVuS46vqVlV17SQnJjl7RZvXJLlHVR1eVddL\ncrck715ijQAAAAxoaT233X1VVT0+yeuTHJbkjO6+oKoeO13/gu5+d1W9Lsl5Sa5J8sLuPn9ZNQIA\nADCm6u6NruGg7Nixo3fu3LnRZQAAsBdVldHfcyZb5zjYPLbC39QyjqGqzu3uHftrt9kmlAIAAIA1\nE24BAAAYnnALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4c4Xbqvr9qvrmRRcD\nAABsLlW1lB84WPP23H5rkndU1b9V1clVdcQiiwIAADaH7l7Tz4Fss2c7OBhzhdvuvnuSE5L8fZJT\nkny0ql5SVfdaZHEAAAAwj7mvue3u93T3k5Mcl+TEJDdI8oaqel9VPaWqti+qSAAAANiXww9gm2sl\nOTLJUUkOS/KhJD+e5GlVdXJ3n7mO9QEAMLg+5cjk1KM2uoyD1qccudElsMVshefGZnpe1Lzj26tq\nR5KfyKTX9nNJ/jTJC7v7/dP1P53k1O6+0YJqXdWOHTt6586dy9wlAABrUFVb4prKrXIci+b3NL+t\n8LtaxjFU1bndvWN/7ebqua2qdya5TZLXJ3l0kr/p7qtXNHtFktPWWCcAAAActHmHJb88yRnd/eG9\nNejuT8T35gIAALAB5g23z8oqwbWqrpvkmu6+cl2rAgAAgDWYt6f1FUkeu8ryx2bSqwsAAAAbZt5w\ne/ckb1hl+d8m+Y71KwcAAADWbt5we70k16yy/JokR6xfOQAAALB284bb85KctMryhyc5f/3KAQAA\ngLWbd0KpZyR5TVXdOskbp8vum+RHkzx4EYUBAADAvObque3uc5L8QJJbJHnO9OfmSX6wu/96ceUB\nAADA/s3bc5vufl2S1y2wFgAAADgg815zCwAAAJvWXD23VXXtJE/NZFKpmye51uz67j5s/UsDAGCr\nqKqNLuGgbdu2baNLAPZh3mHJv5bkYUmemeTZSX4hyS2TnJjkVxZSGQAAW0J3L3wfVbWU/QCb17zD\nkh+a5LHd/UdJrk7ymu5+QpJTknzPoooDAACAecwbbm+U5F3T25cnueH09uuSfO96FwUAAABrMW+4\n/VCSm05vX5jkftPb357k8+tdFAAAAKzFvOH2VUnuO739B0meXlXvT/LiJC9cQF0AAAAwt7kmlOru\nX5q5/RdVdXGSuyd5b3f/9aKKAwAAgHnsN9xW1bWS/HmSX+7u/0qS7v7XJP+64NoAAABgLvsdltzd\nX8xk0ihzqwMAALApzXvN7SuT/PAiCwEAAIADNdc1t5nMlvy0qrpnkp1JPju7srt/b70LAwAAgHnN\nG24fnWR3kjtMf2Z1EuEWAACADTPvbMm3WnQhAAAAcKDmveYWAAAANq25em6r6jn7Wt/dT1ifcgAA\nAGDt5r3m9vYr7l8ryW2THJbkP9a1IgAAAFijea+5vc/KZVV13SR/kuQt610UAAAArMUBX3Pb3Vck\n+Y0kT12/cgAAAGDtDnZCqaOT3GA9CgEAAIADNe+EUj+3clGSmyR5RJJz1rsoAAAAWIt5J5T6mRX3\nr0lyaZIXJXnmulYEAAAAazTvhFK3WnQhAAAAcKDmuua2qq49nR155fLrVtW1178sAAAAmN+8E0q9\nIsljV1n+2CQvX79yAAAAYO3mDbd3T/KGVZb/bZLvWL9yAAAAYO3mnVDqeplMIrXSNUmOWL9yAAAA\nDh1VtdElHJRt27ZtdAlfMm+4PS/JSUlOWbH84UnOX9eKAAAADgHdvdDHr6qF72MzmTfcPiPJa6rq\n1kneOF123yQ/muTBiygMAAAA5jXXNbfdfU6SH0hyiyTPmf7cPMkPdvdfL648AAAA2L95e27T3a9L\n8roF1gIAAAAHZN7vub1XVd1rL8u/c/3LAgAAgPnN+1VAz05y5CrLj5yuAwAAgA0z77Dk2yR55yrL\nz5+uAwAANrnt27dn9+7dC9/Por/eZtu2bdm1a9dC98F45g23n09y0yQfWLH82CRXrmdBAADAYuze\nvXtLfDXM6N8Ny2LMOyz59UmeVVVf+obeqtqe5JnTdQAAALBh5u25fVKSNyf5QFWdN112hySXJnnY\nIgoDAACAec37PbcfTXLHTELuedOfn09y+yQnLKw6AAAAmMNavuf2c0n+OEmq6tgkj8lkQqlbJjls\nEcUBAADAPOa95jZVdVhV/XBV/U0mE0s9OMkfJbn1gmoDAACAuey357aqbpPkp5I8Mslnk5yZ5H5J\nfry737XY8gAAAGD/9tlzW1VvSfLWJNuSPLS7v767n5Zk/PnDAQAA2DL213P77UlOS3J6d1+whHoA\nAABgzfZ3ze23ZhKA/7Gq/qOqnlhVN15CXQAAADC3fYbb7v6P7n5ckpsk+b0kP5jk4ul231dV2xZf\nIgAAAOzbvN9ze0V3/1l33yfJ7ZL8dpInJvlYVb12kQUCAADA/sz9VUB7dPeF3f2UJMcleWiSK9e9\nKgAAAFiD/X4V0N5099VJXjP9AQAAgA2z5p5bAAAA2GyEWwAAAIYn3AIAADA84RYAAIDhCbcAAAAM\nT7gFAABgeEsNt1V1/6p6T1VdWFVP2Ue7b62qq6rqIcusDwAAgDEtLdxW1WFJTkvygCQnJDmpqk7Y\nS7tnJXnDsmoDAABgbMvsub1rkgu7+6LuvjLJWUketEq7n0nyl0k+vsTaAAAAGNgyw+2xSS6euX/J\ndNmXVNWxSR6c5Pn7eqCqOrmqdlbVzksvvXTdCwUAAGAsm21Cqd9P8uTuvmZfjbr79O7e0d07jjnm\nmCWVBgAAwGZ1+BL39eEkx83cv9l02awdSc6qqiQ5OskDq+qq7n71ckoEAABgRMsMt29LcnxV3SqT\nUHtikofPNujuW+25XVUvTvLXgi0AAAD7s7Rw291XVdXjk7w+yWFJzujuC6rqsdP1L1hWLQAAAGwt\ny+y5TXefk+ScFctWDbXd/ehl1AQAAMD4NtuEUgAAALBmwi0AAADDE24BAAAYnnALAADA8IRbAAAA\nhifcAgAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8A7f6AIAAGClqlr4\nNt295n0Am5dwCwDApiN4Amsl3AIAwCGiTzkyOfWojS7joPUpR250CWxCwi0AABwi6umXbYle8apK\nn7rRVbDZmFAKAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABie2ZIBAOAQUlUbXcJB27Zt20aX\nwCYk3AIAwCFiGV8DVFVb4uuGGI9hyQAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAA\nhifcAgAAMDzhFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhifcAgAA\nMDzhFgAAgOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhifcAgAAMDzhFgAA\ngOEJtwAAAAxPuAUAAGB4wi0AAADDE24BAAAYnnALAADA8IRbAAAAhnf4RhcAAADA/lXVUrbp7jVv\nsxkItwAAAAMYNXQui2HJAAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAw\nPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA\n4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwvKWG26q6f1W9p6ou\nrKqnrLL+EVV1XlW9s6r+uaruuMz6AAAAGNPSwm1VHZbktCQPSHJCkpOq6oQVzd6f5F7dffskv5bk\n9GXVBwAAwLiW2XN71yQXdvdF3X1lkrOSPGi2QXf/c3fvnt59a5KbLbE+AAAABrXMcHtskotn7l8y\nXbY3P5nktautqKqTq2pnVe289NJL17FEAAAARrQpJ5SqqvtkEm6fvNr67j69u3d0945jjjlmucUB\nAACw6Ry+xH19OMlxM/dvNl32FarqDklemOQB3f3JJdUGAADAwJbZc/u2JMdX1a2q6tpJTkxy9myD\nqrp5klcm+fHufu8SawMAAGBgS+u57e6rqurxSV6f5LAkZ3T3BVX12On6FyT51SRfm+R5VZUkV3X3\njmXVCAAAfKXp+/KFb9Pda94GZtXof0Q7duzonTt3bnQZAAAALEBVnTtPp+emnFAKAAAA1kK4BQAA\nYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAA\nAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEA\nABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsA\nAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsA\nAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wC\nAAAwPOEWAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEW\nAACA4Qm3AAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3\nAAAADE+4BQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGJ9wCAAAwPOEWAACA4Qm3AAAADE+4\nBQAAYHjCLQAAAMMTbgEAABiecAsAAMDwhFsAAACGt9RwW1X3r6r3VNWFVfWUVdZXVT1nuv68qrrz\nMusDAABgTEsLt1V1WJLTkjwgyQlJTqqqE1Y0e0CS46c/Jyd5/rLqAwAAYFzL7Lm9a5ILu/ui7r4y\nyVlJHrQTU8f9AAAM0ElEQVSizYOSvKQn3prkhlV1kyXWCAAAwIAOX+K+jk1y8cz9S5LcbY42xyb5\n6Gyjqjo5k57dJLm8qt6zvqVuiKOTfGKjiyCJc7GZOBebh3OxuTgfm4dzsXk4F5uHc7F5bJVzcYt5\nGi0z3K6b7j49yekbXcd6qqqd3b1jo+vAudhMnIvNw7nYXJyPzcO52Dyci83Dudg8DrVzscxhyR9O\nctzM/ZtNl621DQAAAHyFZYbbtyU5vqpuVVXXTnJikrNXtDk7ySOnsyZ/W5JPd/dHVz4QAAAAzFra\nsOTuvqqqHp/k9UkOS3JGd19QVY+drn9BknOSPDDJhUk+l+Qxy6pvE9hSw6wH51xsHs7F5uFcbC7O\nx+bhXGwezsXm4VxsHofUuaju3ugaAAAA4KAsc1gyAAAALIRwCwAAwPCE2yWoqhtV1ZlVdVFVnVtV\n/1JVD66qe1fVp6vq7VV1XlX9XVV93cx2D6iqnVX1rqr6j6r63Y08jq2oqi5fZdmpVfXh6Xl5V1Wd\ntBG1HUqqqmf/vqvqSVV16vT27Pn4z6p6flX537WO5ngevK+qXllVJ6xoc3RVfXHP3AkcvNlzUVUP\nrKr3VtUtpufjcyteI2bb7vU5xNpU1Y2r6qyq+q/pa/Y5VfWN03X/u6quqKqjZtrPvpb/Z1X9znT5\nY6bL3l5VV1bVO6e3f3Ojjm1kVfXUqrpg+n7p7VV1SlU9c0WbO1XVu6e3P1BVb1mx/u1Vdf4y696K\nVjkXd6uqw6vqN6avF3v+7p86s83V02UXVNU7qurnvZYfmL28Zt+mqt40/R2/u6pOr6rrVdUnq+rI\nFW1fXVUPq6pHT187vntm3Q9Nlz1kGceyCP6oFqyqKsmrk7y5u7++u++SyUzRN5s2eUt336m775DJ\njNKPm273zUmem+THuvuEJDsymWiL5Xh2d98pyYOS/FFVXWujC9rivpDkh6vq6L2s33M+Tkhy+yT3\nWlplh7ZnT/8/HZ/kZUneWFXHzKz/0SRvTeIDoHVWVfdN8pwkD+juD04XfyLJz+9lk/09h5jD9DX7\nVUne1N3fMH3N/qUkN5o2OSmT1+ofXrHpW6b/o74lyfdX1d27+0XT58+dknwkyX2m95+ynKPZOqrq\n25N8f5I7T98vfXeSv0/ysBVNT0zy0pn7R1TVcdPHuN0yat3q9nIuLk7yf5LcNMntp3/z90wy+97p\n89O//29K8j1JHpDklKUWv7U9J19+zb5dkj/s7s9lMpHvg/c0mn4wd48kfzVd9M5Mnjd7nJTkHcsp\neTGE28X7riRXTmeDTpJ09we7+w9nG01fUI9Isnu66BeT/Hp3/+d0m6u7+/lLqpmp7n5fJjN3b9vo\nWra4qzKZze+J+2l37STXzZefJyxJd78syRuSPHxm8UmZhK1jq+pmq27ImlXVdyb54yTf393/NbPq\njCQPq6rtq2w273OIfbtPki+ueM1+R3e/paq+IckNkjwte/lAp7s/n+TtSY5dRrGHkJsk+UR3fyFJ\nuvsT3f3mJLur6m4z7R6arwy3L8+XA/BJK9ZxYL7qXCT5VJL/meRnuvuK6fLPdPepqz1Ad388yclJ\nHj99/8vBu0mSS/bc6e53Tm++NF8ZXh+c5PXT4Jskb0ly16q6VlXdIMmtM/kfNizhdvG+Kcm/72P9\nPavq7Uk+lMmnX2dMl39zknMXXBv7UVV3TvK+6T9iFuu0JI+YHe4344nT58lHk7y3u4f+xzuwf09y\n2ySZ9obcpLv/LV/5BpKDc51MRvv80J4PN2dcnslrxM/uZdt9PYeYz75ee09MclYmbwZvU1U3Wtmg\nqrYlOT7JmxdW4aHpDUmOmw7Tf15V7Rm986U37lX1bUl2TT+U3uMv8+Ve9h/Il3urOHCrnYtbJ/lQ\nd39m3gfp7osy+WrQr9tfW+by7ExGV722qp5YVTecLn99kjtX1ddO768c3dBJ/i7J/TIZrXj2sgpe\nFOF2yarqtOm1Bm+bLtozLPm4JC9K8lsbWB5f9sSquiDJvyb59Y0u5lDQ3ZcleUmSJ6yyes+w5K9L\ncv2qOnGVNize7CfsD8sk1CaTN/yGJq+PLyb55yQ/uZf1z0nyqKo6YuWK/TyHOHgnJTmru6/JJDT9\n6My6e1bVO5J8OJNekY9tRIFbVXdfnuQumfT2XZrkZVX16Ewul3jI9NrNlW/ak+STmfTunpjk3ZmM\nxOIgrHYuktx7ts3M9eYX7xkWzmJ194uS3C7JKzI5H2+tqut095WZBNaHTC9b+ZZMAu+sszJ5/qz2\nHBqOcLt4FyS585473f24JPdNcswqbc9O8p0z291l4dWxN8+eXhfyI0n+pKquu9EFHSJ+P5M39ddf\nbWV3fzHJ6/Ll5wnL9S2ZvEFMJm/0H11VH8jkf9cdqur4jSpsC7kmk6GVd62qX165srs/leTMTOdn\nWMU+n0Ps16qvvVV1+0x6ZP92+jd/Yr7yA523dPcdMxmt9ZNVdacl1HpImV6e9abuPiXJ45P8SHdf\nnOT9mczD8COZBK2VXpbJqIbh37RvFqucix9IcvM9H7rtud48yacz6Z39KlX19UmuTmJk3Drp7o90\n9xnd/aBMLlX55umqPSMcHpLkNdP3UrPb/Vsm85kc3d3vXWbNiyDcLt4bk1y3qn56Ztn19tL2Hkn2\nXF/120l+ub48Q+PXlBlJl667z06yM8mjNrqWQ0F378qkN3DVXqvptTl3z5efJyxJVf1Iku9N8tLp\n/6UbdPex3X3L7r5lkmdG7+26mF4L9X2ZDDFe7bnwe0n+V5LDV9l2n88h9uuNSa5TVSfvWVBVd8ik\nx/zUPX/v3X3TJDetqlvMbtzd70/ym0mevMyit7rpTLCzH57dKcmeidZemsmQzIu6+5Kv2ngyQdhv\n5at7qzgAezkX70nyJ0meu6czoKoOy2SejNUe45gkL0jy3O7uBZd8SKiq+++Z/LSqbpzkazMZSZIk\nb8rkw7nHZe8f8jwlyVd9oDqir3phZH11d1fVDyV5dlX9YiZDOD6bL7/w7bnmtjL5hOunptudV1X/\nO5M3ktfLZEz8Xy/9ALa+61XV7Ivh763S5hlJzqyqP54OR2OxfjeTT4JnPbGqfiyTmRfPS/K8pVe1\nte3tebDn9379JOcn+a7uvrSq/r9M3jDO+stMekiesfBqDwHdvauq7p/kzVV16Yp1n6iqV2Xvk0et\n9hxiDtPX7Acn+f2qenKSK5J8IJNhfj+9ovmrMukN+dcVy1+Q5ElVdcvu/sBCCz503CDJH06vI7wq\nk2+P2PMBxCsy+fDhZ1bbcHod6LOSxNxF62Jv5+LTSX4tyflV9Zkkn0/yp5nMFJ4k/2P6fvda0+3+\nLKu/52L/VnvNvlmSP6iqK6bLfmHP5RHdfU1V/UUmo4L+YbUH7O7XLrLgZSofmAAAADA6w5IBAAAY\nnnALAADA8IRbAAAAhifcAgAAMDzhFgAAgOEJtwAwuKo6uqq6qu69hm1OrarzF1gWACyVcAsAC1ZV\nL56Gzz9ZZd2zput8lzkAHAThFgCW4+IkD62q6+9ZUFWHJ3lkkg9tWFUAsEUItwCwHOcleV+Sh84s\n+74kVyR5054FVfU1VfUrVXV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cv_results_mean = []\n", + "cv_results_std = []\n", + "results = []\n", + "names = []\n", + "\n", + "for name, model in models:\n", + " kfold = KFold(n_splits=5, random_state=seed)\n", + " cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " cv_results_mean.append(cv_results.mean())\n", + " cv_results_std.append(cv_results.std())\n", + " msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n", + " print(msg)\n", + "\n", + "\n", + "fig = plt.figure(figsize=(16, 8))\n", + "fig.suptitle('Algorithm Comparison')\n", + "ax = fig.add_subplot(111)\n", + "plt.ylim([0,1])\n", + "plt.boxplot(results)\n", + "ax.set_xticklabels(names)\n", + "plt.xlabel('Model', fontsize=14)\n", + "plt.ylabel('Accuracy', fontsize=14)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "clf = LogisticRegression()\n", + "model_train = clf.fit(X_train, y_train)\n", + "y_pred = clf.predict(X_test)\n", + "y_pred_proba = clf.predict_proba(X_test).tolist()\n", + "\n", + "#print(accuracy_score(y_test, y_pred))\n", + "#print(confusion_matrix(y_test, y_pred))\n", + "#print(classification_report(y_test, y_pred))\n", + "pred_proba = clf.predict_proba(X_test)\n", + "proba = clf.fit(X_train, y_train).predict_proba(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + " model_aoi_ids = pd.DataFrame(y_pred_proba, index=X_test.index,\\\n", + " columns=['nonveg_pred_proba', \\\n", + " 'green_pred_proba', 'tree_pred_proba'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AoI IDs Table" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "model_aoi_ids['probability'] = model_aoi_ids[['nonveg_pred_proba','green_pred_proba',\\\n", + " 'tree_pred_proba']].max(axis=1)\n", + "model_aoi_ids.loc[(model_aoi_ids.probability == model_aoi_ids.nonveg_pred_proba, 'classification')] = '1'\n", + "model_aoi_ids.loc[(model_aoi_ids.probability == model_aoi_ids.green_pred_proba, 'classification')] = '2'\n", + "model_aoi_ids.loc[(model_aoi_ids.probability == model_aoi_ids.tree_pred_proba, 'classification')] = '4'\n", + "\n", + "model_aoi_ids.loc[(model_aoi_ids.classification == '1', 'category')] = 'Non-Vegetation'\n", + "model_aoi_ids.loc[(model_aoi_ids.classification == '2', 'category')] = 'Vegetation'\n", + "model_aoi_ids.loc[(model_aoi_ids.classification == '4', 'category')] = 'Trees'" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "output_model_aoi_ids = pd.concat([model_aoi_ids, features_with_label.loc[X_test.index]], axis=1, join='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "output_model_aoi_ids.loc[(output_model_aoi_ids['category'] == 'Non-Vegetation', 'area_interest')] = output_model_aoi_ids['total_area']\n", + "output_model_aoi_ids.loc[(output_model_aoi_ids['category'] == 'Vegetation', 'area_interest')] = output_model_aoi_ids['area_2_02']\n", + "output_model_aoi_ids.loc[(output_model_aoi_ids['category'] == 'Trees', 'area_interest')] = output_model_aoi_ids['total_area']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AoI Summary Table" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "sum_value = output_model_aoi_ids.loc[output_model_aoi_ids['classification'] == 2].area_2_01.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "model_aoi_summary = output_model_aoi_ids.groupby(['category'])[['category']].count()\n", + "model_aoi_summary = model_aoi_summary.rename(columns={'category':'building_count'})\n", + "model_aoi_summary['building_pc'] = model_aoi_summary.building_count/np.sum(model_aoi_summary.building_count)\n", + "model_aoi_summary['area_sum'] = output_model_aoi_ids.groupby(['category'])[['area_interest']].sum()\n", + "model_aoi_summary.set_value('Non-Vegetation', 'area_sum', model_aoi_summary.iloc[0].area_sum + sum_value)\n", + "model_aoi_summary['area_pc'] = model_aoi_summary.area_sum/np.sum(model_aoi_summary.area_sum)\n", + "output_model_aoi_summary = model_aoi_summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We now have a list of the IDS for each shapefile.\n" + ] + } + ], + "source": [ + "nonveg_id = []\n", + "veg_id = []\n", + "tree_id = []\n", + "\n", + "[nonveg_id.append(i) for i in model_aoi_ids.loc[(model_aoi_ids.classification == '1', 'category')].index]\n", + "[veg_id.append(i) for i in model_aoi_ids.loc[(model_aoi_ids.classification == '2', 'category')].index]\n", + "[tree_id.append(i) for i in model_aoi_ids.loc[(model_aoi_ids.classification == '4', 'category')].index]\n", + "print(\"We now have a list of the IDS for each shapefile.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Output" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "ids_to_shp_visualization(nonveg_id, 'output', 'nonvegetation')\n", + "ids_to_shp_visualization(veg_id, 'output', 'vegetation')\n", + "ids_to_shp_visualization(tree_id, 'output', 'trees')" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "output_model_aoi_ids.to_csv('output/output_model_aoi_ids.csv')\n", + "model_aoi_summary.to_csv('output/output_model_aoi_summary.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/classify.py b/classify.py new file mode 100644 index 0000000..8fad68e --- /dev/null +++ b/classify.py @@ -0,0 +1,322 @@ + +# coding: utf-8 + +# In[130]: + + +AoI_csv_dir = '1_input/aoi_ids.csv' +label_csv_dir = '1_input/labels.csv' +features_csv_dir = '2_processing/features_table.csv' +BAG_Panden = '/home/data/citycentre/BAG_Panden.shp' + + +# # Dependencies + +# In[131]: + + +import numpy as np +import matplotlib.pyplot as plt +from sklearn.cluster import KMeans +from sklearn.metrics import pairwise_distances_argmin +from sklearn.datasets import load_sample_image +from sklearn.utils import shuffle +from time import time +#from PIL import Image +import pandas as pd +from shutil import copyfile +import matplotlib.image as mpimg +import numpy +import geopandas as gpd +import fiona +import rasterio +import rasterio.mask +from pandas.tools.plotting import scatter_matrix +import matplotlib.pyplot as plt +from time import time +from scipy.stats import randint +from sklearn import preprocessing +from sklearn.metrics import classification_report +from sklearn.metrics import confusion_matrix +from sklearn.metrics import accuracy_score +from sklearn.metrics import roc_auc_score +from sklearn.model_selection import train_test_split +from sklearn.model_selection import cross_val_score +from sklearn.model_selection import RandomizedSearchCV +from sklearn.model_selection import GridSearchCV +from sklearn.model_selection import KFold +from sklearn.ensemble import GradientBoostingClassifier +from sklearn.linear_model import LogisticRegression +from sklearn.linear_model import SGDClassifier +from sklearn.discriminant_analysis import LinearDiscriminantAnalysis +from sklearn.neighbors import KNeighborsClassifier +from sklearn.tree import DecisionTreeClassifier +from sklearn.naive_bayes import GaussianNB +from sklearn.svm import SVC +from sklearn.ensemble import RandomForestClassifier +from sklearn.mixture import GaussianMixture +from sklearn.svm import LinearSVC +from sklearn.cluster import MeanShift +from sklearn.manifold import LocallyLinearEmbedding +import matplotlib.pyplot as plt +from sklearn.metrics import pairwise_distances_argmin +from sklearn.datasets import load_sample_image +from sklearn.utils import shuffle +from shapely.geometry import shape +from shapely.geometry import Polygon +import shapefile +import shutil +import os +import glob + + +# # Functions + +# In[132]: + + +def ids_to_shp_visualization(ids_list, directory, name, shape_file=BAG_Panden): + + i=0 + if not os.path.exists(directory+"/temporary"): + os.makedirs(directory+"/temporary") + + for identifica in ids_list: + for feat in fiona.open(shape_file, "r"): + if identifica==feat['properties']['Identifica']: + try: + + feat2=feat['properties']['Identifica'] + feat1=[feat['geometry']] + area_per_roof = feat['properties']['SHAPE_Area'] + + with fiona.open(shape_file, "r") as shapef: + meta=shapef.meta + with fiona.open(directory+'/temporary/'+str(i)+'.shp', 'w', **meta) as sink: + sink.write(feat) + + i=i+1 + #if i==1: + # break + except ValueError: + continue + + files = glob.glob(directory+"/temporary/*.shp") + w = shapefile.Writer() + for f in files: + r = shapefile.Reader(f) + w._shapes.extend(r.shapes()) + w.records.extend(r.records()) + w.fields = list(r.fields) + w.save(directory+"/"+name+".shp") + shutil.rmtree(directory+"/temporary/") + + + +# # Model + +# In[133]: + + +aoi_list = [] +[aoi_list.append(ID) for ID in pd.read_csv(AoI_csv_dir, dtype=str).ID] +print('The IDs have been added.') + + +# In[134]: + + +label_df = pd.read_csv(label_csv_dir, dtype={'ID':str}).set_index('ID') +label_df.label = label_df.label.replace(3,2) +label_df.shape + + +# In[135]: + + +features_df = pd.read_csv(features_csv_dir, dtype={'ID':str}).set_index('ID') +features_df.loc(['0599100010050372']) + + +# In[136]: + + +features_with_label = pd.concat([features_df, label_df], axis=1) + + +# In[137]: + + +# Split-out validation dataset + +X_train = features_with_label.loc[label_df.index].drop('label', 1).dropna() +y_train = features_with_label.loc[label_df.index].dropna().label +X_test = features_with_label.loc[aoi_list].drop('label', 1).dropna() + +#print(X_train.shape) +#print(y_train.shape) +#print(X_test.shape) + + +# In[138]: + + +#y_train.head() + + +# In[139]: + + +seed = 0 +scoring = 'accuracy' + + +# In[140]: + + +models = [] +models.append(('GBC', GradientBoostingClassifier())) +models.append(('LR', LogisticRegression())) +models.append(('NB', GaussianNB())) +models.append(('LDA', LinearDiscriminantAnalysis())) +models.append(('KNN', KNeighborsClassifier())) +models.append(('CART', DecisionTreeClassifier())) +models.append(('SVM', SVC())) +models.append(('SGD', SGDClassifier())) +models.append(('LSVM', LinearSVC())) + + +# In[141]: + + +cv_results_mean = [] +cv_results_std = [] +results = [] +names = [] + +for name, model in models: + kfold = KFold(n_splits=5, random_state=seed) + cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring) + results.append(cv_results) + names.append(name) + cv_results_mean.append(cv_results.mean()) + cv_results_std.append(cv_results.std()) + msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std()) + print(msg) + + +fig = plt.figure(figsize=(16, 8)) +fig.suptitle('Algorithm Comparison') +ax = fig.add_subplot(111) +plt.ylim([0,1]) +plt.boxplot(results) +ax.set_xticklabels(names) +plt.xlabel('Model', fontsize=14) +plt.ylabel('Accuracy', fontsize=14) +plt.show() + + +# In[142]: + + +clf = LogisticRegression() +model_train = clf.fit(X_train, y_train) +y_pred = clf.predict(X_test) +y_pred_proba = clf.predict_proba(X_test).tolist() + +#print(accuracy_score(y_test, y_pred)) +#print(confusion_matrix(y_test, y_pred)) +#print(classification_report(y_test, y_pred)) +pred_proba = clf.predict_proba(X_test) +proba = clf.fit(X_train, y_train).predict_proba(X_test) + + +# In[143]: + + +model_aoi_ids = pd.DataFrame(y_pred_proba, index=X_test.index, + columns=['nonveg_pred_proba', 'green_pred_proba', + 'tree_pred_proba']) + + +# # AoI IDs Table + +# In[144]: + + +model_aoi_ids['probability'] = model_aoi_ids[['nonveg_pred_proba','green_pred_proba',\ + 'tree_pred_proba']].max(axis=1) +model_aoi_ids.loc[(model_aoi_ids.probability == model_aoi_ids.nonveg_pred_proba, 'classification')] = '1' +model_aoi_ids.loc[(model_aoi_ids.probability == model_aoi_ids.green_pred_proba, 'classification')] = '2' +model_aoi_ids.loc[(model_aoi_ids.probability == model_aoi_ids.tree_pred_proba, 'classification')] = '4' + +model_aoi_ids.loc[(model_aoi_ids.classification == '1', 'category')] = 'Non-Vegetation' +model_aoi_ids.loc[(model_aoi_ids.classification == '2', 'category')] = 'Vegetation' +model_aoi_ids.loc[(model_aoi_ids.classification == '4', 'category')] = 'Trees' + + +# In[145]: + + +output_model_aoi_ids = pd.concat([model_aoi_ids, features_with_label.loc[X_test.index]], axis=1, join='inner') + + +# In[146]: + + +output_model_aoi_ids.loc[(output_model_aoi_ids['category'] == 'Non-Vegetation', 'area_interest')] = output_model_aoi_ids['total_area'] +output_model_aoi_ids.loc[(output_model_aoi_ids['category'] == 'Vegetation', 'area_interest')] = output_model_aoi_ids['area_2_02'] +output_model_aoi_ids.loc[(output_model_aoi_ids['category'] == 'Trees', 'area_interest')] = output_model_aoi_ids['total_area'] + + +# # AoI Summary Table + +# In[147]: + + +sum_value = output_model_aoi_ids.loc[output_model_aoi_ids['classification'] == 2].area_2_01.sum() + + +# In[148]: + + +model_aoi_summary = output_model_aoi_ids.groupby(['category'])[['category']].count() +model_aoi_summary = model_aoi_summary.rename(columns={'category':'building_count'}) +model_aoi_summary['building_pc'] = model_aoi_summary.building_count/np.sum(model_aoi_summary.building_count) +model_aoi_summary['area_sum'] = output_model_aoi_ids.groupby(['category'])[['area_interest']].sum() +model_aoi_summary.set_value('Non-Vegetation', 'area_sum', model_aoi_summary.iloc[0].area_sum + sum_value) +model_aoi_summary['area_pc'] = model_aoi_summary.area_sum/np.sum(model_aoi_summary.area_sum) +output_model_aoi_summary = model_aoi_summary + + +# ## Visualization + +# In[149]: + + +nonveg_id = [] +veg_id = [] +tree_id = [] + +[nonveg_id.append(i) for i in model_aoi_ids.loc[(model_aoi_ids.classification == '1', 'category')].index] +[veg_id.append(i) for i in model_aoi_ids.loc[(model_aoi_ids.classification == '2', 'category')].index] +[tree_id.append(i) for i in model_aoi_ids.loc[(model_aoi_ids.classification == '4', 'category')].index] +print("We now have a list of the IDS for each shapefile.") + + +# # Output + +# In[150]: + + +ids_to_shp_visualization(nonveg_id, 'output', 'nonvegetation') +ids_to_shp_visualization(veg_id, 'output', 'vegetation') +ids_to_shp_visualization(tree_id, 'output', 'trees') + + +# In[151]: + + +output_model_aoi_ids.to_csv('3_output/output_model_aoi_ids.csv') +model_aoi_summary.to_csv('3_output/output_model_aoi_summary.csv') + diff --git a/create_features.ipynb b/create_features.ipynb new file mode 100644 index 0000000..33ead30 --- /dev/null +++ b/create_features.ipynb @@ -0,0 +1,2458 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "BAG_Panden = '/home/data/citycentre/BAG_Panden.shp'\n", + "CIR = '/home/data/processing/CIR_2015_10.tif'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import pairwise_distances_argmin\n", + "from sklearn.datasets import load_sample_image\n", + "from sklearn.utils import shuffle\n", + "from time import time\n", + "from PIL import Image\n", + "import os, glob\n", + "import pandas as pd\n", + "from shutil import copyfile\n", + "import matplotlib.image as mpimg\n", + "import numpy\n", + "import geopandas as gpd\n", + "import fiona\n", + "import rasterio\n", + "import rasterio.mask\n", + "import os\n", + "from shapely.geometry import shape\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Files" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "shapefile=gpd.read_file(BAG_Panden)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We now ave all shapefile IDs\n" + ] + } + ], + "source": [ + "### all_ids= []\n", + "[all_ids.append(i) for i in shapefile['Identifica']]\n", + "print('We now ave all shapefile IDs')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lists" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Old\n", + "nonveg_percent_list=[]\n", + "nonveg_abs_list=[]\n", + "inten_percent_list=[]\n", + "inten_abs_list=[]\n", + "exten_percent_list=[]\n", + "exten_abs_list=[]\n", + "trees_percent_list=[]\n", + "trees_abs_list=[]\n", + "total_abs_list = []\n", + "area_per_roof_list = []\n", + "mean = []\n", + "max_value = []\n", + "min_value = []\n", + "nasa_classification_list = []\n", + "average_vegetation = []\n", + "ID_list = []" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Statistical information\n", + "\n", + "pct_05 = []\n", + "pct_10 = []\n", + "pct_15 = []\n", + "pct_20 = []\n", + "pct_25 = []\n", + "pct_30 = []\n", + "pct_35 = []\n", + "pct_40 = []\n", + "pct_45 = []\n", + "pct_50 = []\n", + "pct_55 = []\n", + "pct_60 = []\n", + "pct_65 = []\n", + "pct_70 = []\n", + "pct_75 = []\n", + "pct_80 = []\n", + "pct_85 = []\n", + "pct_90 = []\n", + "pct_95 = []\n", + "\n", + "pc_1_08 = []\n", + "pc_08_06 = []\n", + "pc_06_04 = []\n", + "pc_04_02 = []\n", + "pc_02_00 = []\n", + "pc_00_02 = []\n", + "pc_02_04 = []\n", + "pc_04_06 = []\n", + "pc_06_08 = []\n", + "pc_08_1 = []\n", + "\n", + "var = []\n", + "std = []" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Clusters\n", + "\n", + "k2_01 = []\n", + "k2_02 = []\n", + "k4_01 = []\n", + "k4_02 = []\n", + "k4_03 = []\n", + "k4_04 = []\n", + "\n", + "pc_2_01 = []\n", + "pc_2_02 = []\n", + "pc_4_01 = []\n", + "pc_4_02 = []\n", + "pc_4_03 = []\n", + "pc_4_04 = []\n", + "\n", + "area_2_01 = []\n", + "area_2_02 = []\n", + "area_4_01 = []\n", + "area_4_02 = []\n", + "area_4_03 = []\n", + "area_4_04 = []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def recreate_image(codebook, labels, w, h):\n", + " \"\"\"Recreate the (compressed) image from the code book & labels\"\"\"\n", + " d = codebook.shape[1]\n", + " image = np.zeros((w, h))\n", + " label_idx = 0\n", + " for i in range(w):\n", + " for j in range(h):\n", + " image[i][j] = codebook[labels[label_idx]]\n", + " label_idx += 1\n", + " return(image)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def cluster_per_k(imarray, n_colors):\n", + " \n", + " # Load Image and transform to a 2D numpy array.\n", + " w, h= original_shape = tuple(imarray.shape)\n", + " \n", + " d = 1\n", + " image_array = np.reshape(imarray, (w * h, d))\n", + " \n", + " \n", + " \n", + " #print(\"Fitting model on a small sub-sample of the data\")\n", + " t0 = time()\n", + " image_array_sample = shuffle(image_array, random_state=0)[:1000]\n", + " kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample)\n", + " \n", + " # Get labels for all points\n", + " #print(\"Predicting color indices on the full image (k-means)\")\n", + " t0 = time()\n", + " labels = kmeans.predict(image_array)\n", + " \n", + " \n", + " codebook_random = shuffle(image_array, random_state=0)[:n_colors + 1]\n", + " #print(\"Predicting color indices on the full image (random)\")\n", + " t0 = time()\n", + " labels_random = pairwise_distances_argmin(codebook_random, image_array, axis=0)\n", + " \n", + " \n", + " \n", + " #plt.title('Quantized image (2 colors, K-Means)')\n", + " return(recreate_image(kmeans.cluster_centers_, labels, w, h))" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_table_of_clusters_from_id(roof_id, shp_file = \"/home/data/citycentre/BAG_Panden.shp\", raster_file = \"/home/data/citycentre/lufo_2014.tif\"):\n", + " try:\n", + " ID = roof_id\n", + " shapes = fiona.open(shp_file, \"r\")\n", + " roof_idx = [str(feat['properties']['Identifica']) for feat in shapes].index(roof_id)\n", + " area_per_roof = shapes[roof_idx]['properties']['SHAPE_Area']\n", + " \n", + "\n", + " feat1 = [shapes[roof_idx]['geometry'] ]\n", + " with rasterio.open('/home/data/processing/CIR_2015_10.tif') as src:\n", + " out_image, out_transform = rasterio.mask.mask(src, feat1,crop=True, nodata=10, all_touched=True)\n", + " out_meta = src.meta.copy() \n", + " \n", + " \n", + " out_meta.update({\"driver\": \"GTiff\",\n", + " \"height\": out_image.shape[1],\n", + " \"width\": out_image.shape[2],\n", + " \"transform\": out_transform})\n", + " \n", + " NIR = np.matrix(out_image[0, :, :]).astype(float)\n", + " NIR[NIR==10] = 90\n", + " RED = np.matrix(out_image[1, :, :]).astype(float)\n", + " \n", + " ndvi = (NIR-RED)/(NIR+RED)\n", + "\n", + " ndvi_not_nan = ndvi[ndvi != .8]\n", + " ndvi[ndvi == .8] = np.nan \n", + " \n", + " k4_ndvi = cluster_per_k(np.array(ndvi_not_nan), 5)\n", + " k2_ndvi = cluster_per_k(np.array(ndvi_not_nan), 3)\n", + " \n", + " list_of_4_cluster_values = (np.unique(k4_ndvi.flatten())).tolist()[:4]\n", + " k4_01.append(list_of_4_cluster_values[0])\n", + " k4_02.append(list_of_4_cluster_values[1])\n", + " k4_03.append(list_of_4_cluster_values[2])\n", + " k4_04.append(list_of_4_cluster_values[3])\n", + " \n", + " list_of_2_cluster_values = (np.unique(k2_ndvi.flatten())).tolist()[:2]\n", + " k2_01.append(list_of_2_cluster_values[0])\n", + " k2_02.append(list_of_2_cluster_values[1])\n", + " \n", + " \n", + " total_abs = (((ndvi_not_nan < 0.088)).sum() + ((0.088 < ndvi_not_nan) & (ndvi_not_nan < 0.21)).sum()\\\n", + " + ((0.21 < ndvi_not_nan) & (ndvi_not_nan < 0.276)).sum() + ((0.276 < ndvi_not_nan) & \\\n", + " (ndvi_not_nan < 1)).sum())\n", + " \n", + " nonveg_percent = (((ndvi_not_nan < 0.088)).sum())/total_abs\n", + " exten_percent = (((0.088 < ndvi_not_nan) & (ndvi_not_nan < 0.21)).sum())/total_abs\n", + " inten_percent = (((0.21 < ndvi_not_nan) & (ndvi_not_nan < 0.276)).sum())/total_abs \n", + " trees_percent = (((0.276 < ndvi_not_nan) & (ndvi_not_nan < 1)).sum())/total_abs \n", + " nonveg_abs = area_per_roof*nonveg_percent\n", + " exten_abs = area_per_roof*exten_percent\n", + " inten_abs = area_per_roof*inten_percent\n", + " trees_abs = area_per_roof*trees_percent\n", + " mean_roof = np.nanmean(ndvi_not_nan)\n", + " max_roof = np.nanmax(ndvi_not_nan)\n", + " min_roof = np.nanmin(ndvi)\n", + " \n", + " nonveg_percent_list.append(nonveg_percent)\n", + " nonveg_abs_list.append(nonveg_abs)\n", + " inten_percent_list.append(inten_percent)\n", + " inten_abs_list.append(inten_abs)\n", + " exten_percent_list.append(exten_percent)\n", + " exten_abs_list.append(exten_abs)\n", + " trees_percent_list.append(trees_percent)\n", + " trees_abs_list.append(trees_abs) \n", + " total_abs_list.append(total_abs)\n", + " area_per_roof_list.append(area_per_roof)\n", + " ID_list.append(ID)\n", + " \n", + " min_value.append(min_roof)\n", + " pct_05.append(np.percentile(ndvi_not_nan, 5))\n", + " pct_10.append(np.percentile(ndvi_not_nan, 10))\n", + " pct_15.append(np.percentile(ndvi_not_nan, 15))\n", + " pct_20.append(np.percentile(ndvi_not_nan, 20)) \n", + " pct_25.append(np.percentile(ndvi_not_nan, 25))\n", + " pct_30.append(np.percentile(ndvi_not_nan, 30))\n", + " pct_35.append(np.percentile(ndvi_not_nan, 35))\n", + " pct_40.append(np.percentile(ndvi_not_nan, 40))\n", + " pct_45.append(np.percentile(ndvi_not_nan, 45))\n", + " pct_50.append(np.percentile(ndvi_not_nan, 50))\n", + " pct_55.append(np.percentile(ndvi_not_nan, 55))\n", + " pct_60.append(np.percentile(ndvi_not_nan, 60)) \n", + " pct_65.append(np.percentile(ndvi_not_nan, 65))\n", + " pct_70.append(np.percentile(ndvi_not_nan, 70))\n", + " pct_75.append(np.percentile(ndvi_not_nan, 75))\n", + " pct_80.append(np.percentile(ndvi_not_nan, 80))\n", + " pct_85.append(np.percentile(ndvi_not_nan, 85))\n", + " pct_90.append(np.percentile(ndvi_not_nan, 90))\n", + " pct_95.append(np.percentile(ndvi_not_nan, 95))\n", + " max_value.append(max_roof)\n", + " \n", + " pc_1_08.append((((-1 < ndvi_not_nan) & (ndvi_not_nan < -0.8)).sum())/total_abs) \n", + " pc_08_06.append((((-0.8 < ndvi_not_nan) & (ndvi_not_nan < -0.6)).sum())/total_abs)\n", + " pc_06_04.append((((-0.6 < ndvi_not_nan) & (ndvi_not_nan < -0.4)).sum())/total_abs)\n", + " pc_04_02.append((((-0.4 < ndvi_not_nan) & (ndvi_not_nan < -0.2)).sum())/total_abs)\n", + " pc_02_00.append((((-0.2 < ndvi_not_nan) & (ndvi_not_nan < 0)).sum())/total_abs)\n", + " pc_00_02.append((((0.0 < ndvi_not_nan) & (ndvi_not_nan < 0.2)).sum())/total_abs)\n", + " pc_02_04.append((((0.2 < ndvi_not_nan) & (ndvi_not_nan < 0.4)).sum())/total_abs)\n", + " pc_04_06.append((((0.4 < ndvi_not_nan) & (ndvi_not_nan < 0.6)).sum())/total_abs)\n", + " pc_06_08.append((((0.6 < ndvi_not_nan) & (ndvi_not_nan < 0.8)).sum())/total_abs)\n", + " pc_08_1.append((((0.8 < ndvi_not_nan) & (ndvi_not_nan < 1)).sum())/total_abs)\n", + " \n", + " pc_2_01.append(((k2_ndvi == list_of_2_cluster_values[0]).sum())/total_abs)\n", + " pc_2_02.append(((k2_ndvi == list_of_2_cluster_values[1]).sum())/total_abs)\n", + " pc_4_01.append(((k4_ndvi == list_of_4_cluster_values[0]).sum())/total_abs)\n", + " pc_4_02.append(((k4_ndvi == list_of_4_cluster_values[1]).sum())/total_abs)\n", + " pc_4_03.append(((k4_ndvi == list_of_4_cluster_values[2]).sum())/total_abs)\n", + " pc_4_04.append(((k4_ndvi == list_of_4_cluster_values[3]).sum())/total_abs)\n", + " \n", + " area_2_01.append((((k2_ndvi == list_of_2_cluster_values[0]).sum())/total_abs)*area_per_roof)\n", + " area_2_02.append((((k2_ndvi == list_of_2_cluster_values[1]).sum())/total_abs)*area_per_roof)\n", + " area_4_01.append((((k4_ndvi == list_of_4_cluster_values[0]).sum())/total_abs)*area_per_roof)\n", + " area_4_02.append((((k4_ndvi == list_of_4_cluster_values[1]).sum())/total_abs)*area_per_roof)\n", + " area_4_03.append((((k4_ndvi == list_of_4_cluster_values[2]).sum())/total_abs)*area_per_roof)\n", + " area_4_04.append((((k4_ndvi == list_of_4_cluster_values[3]).sum())/total_abs)*area_per_roof)\n", + " \n", + " \n", + " std.append(np.std(ndvi_not_nan))\n", + " var.append(np.var(ndvi_not_nan))\n", + " mean.append(np.nanmean(ndvi_not_nan))\n", + " \n", + " except ValueError:\n", + " print('A building partly outside the AoI was just detected.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Code" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "A building partly outside the AoI was just detected.\n", + "The wait is over.\n" + ] + } + ], + "source": [ + "[get_table_of_clusters_from_id(x) for x in all_ids]\n", + "print('The wait is over.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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11724 rows × 52 columns

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" + ], + "text/plain": [ + " nasa_nonveg_area nasa_nonveg_pc nasa_ext_area \\\n", + "ID \n", + "0599100000082215 29.105542 0.407407 32.192493 \n", + "0599100000111188 49.865211 0.654762 24.479285 \n", + "0599100000138014 46.591342 0.634146 26.431627 \n", + "0599100000215405 41.459618 0.859649 5.076688 \n", + "0599100000289238 38.475543 0.775862 7.267603 \n", + "0599100000289242 54.328643 0.748466 18.257987 \n", + "0599100000617607 37.249773 0.604317 23.946283 \n", + "0599100000617608 121.983551 0.786834 32.075356 \n", + "0599100000617609 304.233711 0.452859 346.286494 \n", + "0599100000617610 68.101024 0.839779 12.096892 \n", + "0599100000617611 49.359151 0.790210 10.920166 \n", + "0599100000617612 55.606094 0.887324 7.061091 \n", + "0599100000617613 165.969289 0.894057 19.187201 \n", + "0599100000617614 47.679956 0.741497 16.184939 \n", + "0599100000617615 23.686119 0.621053 12.043789 \n", + "0599100000617616 45.634047 0.753623 14.918823 \n", + "0599100000617617 118.834271 0.946237 6.301817 \n", + "0599100000617618 105.232177 0.975104 2.686779 \n", + "0599100000617619 719.388441 0.629732 374.461725 \n", + "0599100000617620 39.236258 0.642857 21.797921 \n", + "0599100000617621 89.421871 0.892241 10.799743 \n", + "0599100000617622 84.077089 0.608696 48.044051 \n", + "0599100000617623 38.219962 0.654135 17.133086 \n", + "0599100000617624 39.543173 0.703125 16.696007 \n", + "0599100000617625 46.958895 0.794118 11.739724 \n", + "0599100000617626 44.779403 0.719424 14.777203 \n", + "0599100000617896 41.013435 0.848214 7.339246 \n", + "0599100000617897 90.864474 0.810924 20.244417 \n", + "0599100000631878 27.736625 0.322581 36.057612 \n", + "0599100000631880 44.839619 0.495146 29.013871 \n", + "... ... ... ... \n", + "0599100000622970 97.573253 0.831325 19.797472 \n", + "0599100000622972 126.406307 0.876712 17.775887 \n", + "0599100000622975 132.760588 0.910596 13.034676 \n", + "0599100000658187 177.501961 0.934959 12.347963 \n", + "0599100000686574 3.012953 0.133333 13.558290 \n", + "0599100000686576 11.512626 0.538462 6.578644 \n", + "0599100000686577 9.420341 0.457143 6.476484 \n", + "0599100000686578 1.379025 0.065217 17.927324 \n", + "0599100000756944 190.105399 0.983051 3.277679 \n", + "0599100000756971 180.969564 0.973684 4.401962 \n", + "0599100000757176 66.552323 0.687805 21.240103 \n", + "0599100000757177 49.297715 0.512077 37.205823 \n", + "0599100000757178 45.690954 0.933333 2.797405 \n", + "0599100010034672 301.690792 0.705686 125.823648 \n", + "0599100010045360 74.390113 0.362903 125.636636 \n", + "0599100000627120 109.873887 0.902439 11.878258 \n", + "0599100000627124 116.558428 0.955556 5.421322 \n", + "0599100000627128 119.951233 0.970000 3.709832 \n", + "0599100000627132 13.414210 0.215385 42.638024 \n", + "0599100000627138 133.070037 0.820755 26.002191 \n", + "0599100000627140 140.444111 0.919094 12.363038 \n", + "0599100000690226 1157.569652 0.925177 90.454896 \n", + "0599100000757111 58.697210 0.619289 31.273104 \n", + "0599100000757172 57.012802 0.581006 29.054601 \n", + "0599100000757174 82.276669 0.829787 12.130535 \n", + "0599100000767076 35.045116 0.217666 119.864456 \n", + "0599100000767077 23.150969 0.141975 136.389403 \n", + "0599100000755931 2765.547872 1.000000 0.000000 \n", + "0599100000700214 3391.266252 0.964143 126.121472 \n", + "0599100000701988 8202.746109 1.000000 0.000000 \n", + "\n", + " nasa_ext_pc nasa_int_area nasa_int_pc nasa_tree_area \\\n", + "ID \n", + "0599100000082215 0.450617 8.378868 0.117284 1.763972 \n", + "0599100000111188 0.321429 1.359960 0.017857 0.453320 \n", + "0599100000138014 0.359756 0.447994 0.006098 0.000000 \n", + "0599100000215405 0.105263 1.269172 0.026316 0.423057 \n", + "0599100000289238 0.146552 2.137530 0.043103 1.710024 \n", + "0599100000289242 0.251534 0.000000 0.000000 0.000000 \n", + "0599100000617607 0.388489 0.443450 0.007194 0.000000 \n", + "0599100000617608 0.206897 0.485990 0.003135 0.485990 \n", + "0599100000617609 0.515456 16.094275 0.023957 5.191702 \n", + "0599100000617610 0.149171 0.896066 0.011050 0.000000 \n", + "0599100000617611 0.174825 2.184033 0.034965 0.000000 \n", + "0599100000617612 0.112676 0.000000 0.000000 0.000000 \n", + "0599100000617613 0.103359 0.479680 0.002584 0.000000 \n", + "0599100000617614 0.251701 0.437431 0.006803 0.000000 \n", + "0599100000617615 0.315789 2.007298 0.052632 0.401460 \n", + "0599100000617616 0.246377 0.000000 0.000000 0.000000 \n", + "0599100000617617 0.050179 0.450130 0.003584 0.000000 \n", + "0599100000617618 0.024896 0.000000 0.000000 0.000000 \n", + "0599100000617619 0.327793 45.357336 0.039705 3.164465 \n", + "0599100000617620 0.357143 0.000000 0.000000 0.000000 \n", + "0599100000617621 0.107759 0.000000 0.000000 0.000000 \n", + "0599100000617622 0.347826 5.543544 0.040134 0.461962 \n", + "0599100000617623 0.293233 3.075169 0.052632 0.000000 \n", + "0599100000617624 0.296875 0.000000 0.000000 0.000000 \n", + "0599100000617625 0.198529 0.434805 0.007353 0.000000 \n", 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0.310078 \n", + "0599100000617614 0.000000 64.302325 1 ... 0.476190 \n", + "0599100000617615 0.010526 38.138666 1 ... 0.189474 \n", + "0599100000617616 0.000000 60.552870 1 ... 0.413043 \n", + "0599100000617617 0.000000 125.586218 1 ... 0.336918 \n", + "0599100000617618 0.000000 107.918956 1 ... 0.095436 \n", + "0599100000617619 0.002770 1142.371966 1 ... 0.310711 \n", + "0599100000617620 0.000000 61.034179 1 ... 0.271429 \n", + "0599100000617621 0.000000 100.221614 1 ... 0.366379 \n", + "0599100000617622 0.003344 138.126647 1 ... 0.244147 \n", + "0599100000617623 0.000000 58.428218 1 ... 0.285714 \n", + "0599100000617624 0.000000 56.239180 1 ... 0.187500 \n", + "0599100000617625 0.000000 59.133423 1 ... 0.279412 \n", + "0599100000617626 0.000000 62.243371 1 ... 0.237410 \n", + "0599100000617896 0.000000 48.352681 1 ... 0.321429 \n", + "0599100000617897 0.000000 112.050492 1 ... 0.361345 \n", + "0599100000631878 0.161290 85.983536 2 ... 0.322581 \n", + "0599100000631880 0.116505 90.558447 1 ... 0.262136 \n", + "... ... ... ... ... ... \n", + "0599100000622970 0.000000 117.370725 1 ... 0.246988 \n", + "0599100000622972 0.000000 144.182194 1 ... 0.095890 \n", + "0599100000622975 0.000000 145.795264 1 ... 0.453642 \n", + "0599100000658187 0.000000 189.849924 1 ... 0.382114 \n", + "0599100000686574 0.133333 22.597150 2 ... 0.466667 \n", + "0599100000686576 0.076923 21.380591 1 ... 0.192308 \n", + "0599100000686577 0.057143 20.606996 1 ... 0.114286 \n", + "0599100000686578 0.043478 21.145048 2 ... 0.413043 \n", + "0599100000756944 0.000000 193.383078 1 ... 0.451977 \n", + "0599100000756971 0.000000 185.860633 1 ... 0.134211 \n", + "0599100000757176 0.004878 96.760470 1 ... 0.229268 \n", + "0599100000757177 0.043478 96.270066 1 ... 0.140097 \n", + "0599100000757178 0.009524 48.954593 1 ... 0.361905 \n", + "0599100010034672 0.000000 427.514440 1 ... 0.257525 \n", + "0599100010045360 0.000000 204.986090 2 ... 0.137097 \n", + "0599100000627120 0.000000 121.752145 1 ... 0.323171 \n", + "0599100000627124 0.000000 121.979750 1 ... 0.444444 \n", + "0599100000627128 0.000000 123.661065 1 ... 0.360000 \n", + "0599100000627132 0.000000 62.280260 2 ... 0.276923 \n", + "0599100000627138 0.000000 162.131310 1 ... 0.317610 \n", + "0599100000627140 0.000000 152.807149 1 ... 0.333333 \n", + "0599100000690226 0.000000 1251.187306 1 ... 0.505561 \n", + "0599100000757111 0.010152 94.781560 1 ... 0.187817 \n", + "0599100000757172 0.044693 98.127803 1 ... 0.178771 \n", + "0599100000757174 0.010638 99.153935 1 ... 0.260638 \n", + "0599100000767076 0.003155 161.004375 2 ... 0.479495 \n", + "0599100000767077 0.000000 163.063345 2 ... 0.425926 \n", + "0599100000755931 0.000000 2765.547872 1 ... 0.344111 \n", + "0599100000700214 0.000000 3517.387724 1 ... 0.262948 \n", + "0599100000701988 0.000000 8202.746109 1 ... 0.166667 \n", + "\n", + " pc_4_03 pc_4_04 area_2_01 area_2_02 area_4_01 \\\n", + "ID \n", + "0599100000082215 0.228395 0.209877 25.577597 23.372632 23.813625 \n", + "0599100000111188 0.255952 0.184524 43.518729 32.185727 12.239643 \n", + "0599100000138014 0.115854 0.298780 28.671595 18.815734 18.367741 \n", + "0599100000215405 0.087719 0.043860 36.805987 6.345860 20.306752 \n", + "0599100000289238 0.155172 0.086207 34.200483 10.260145 22.230314 \n", + "0599100000289242 0.184049 0.159509 37.851923 23.601788 14.695453 \n", + "0599100000617607 0.151079 0.179856 34.145625 13.746940 22.172484 \n", + "0599100000617608 0.225705 0.094044 94.282107 41.795161 16.037678 \n", + "0599100000617609 0.222566 0.180062 232.069060 239.337442 229.992379 \n", + "0599100000617610 0.270718 0.082873 64.068726 12.544925 8.064595 \n", + "0599100000617611 0.489510 0.076923 8.736133 46.738311 7.425713 \n", + "0599100000617612 0.507042 0.352113 7.502409 53.840821 3.971864 \n", + "0599100000617613 0.183463 0.108527 79.147204 95.456325 65.236484 \n", + "0599100000617614 0.251701 0.115646 5.249169 46.805094 5.249169 \n", + "0599100000617615 0.126316 0.052632 25.693417 10.036491 23.686119 \n", + "0599100000617616 0.347826 0.072464 12.286089 42.562524 6.581834 \n", + "0599100000617617 0.182796 0.039427 56.266227 63.468304 54.915837 \n", + "0599100000617618 0.323651 0.132780 49.705411 41.645074 42.540667 \n", + "0599100000617619 0.442752 0.090489 350.200824 650.825026 90.187261 \n", + "0599100000617620 0.335714 0.271429 26.157505 30.081131 3.923626 \n", + "0599100000617621 0.150862 0.077586 38.879074 49.678817 36.719126 \n", + "0599100000617622 0.227425 0.147157 55.435444 52.201709 35.109114 \n", + "0599100000617623 0.142857 0.105263 26.358594 22.404805 23.722735 \n", + "0599100000617624 0.171875 0.187500 29.437696 13.620426 19.332218 \n", + "0599100000617625 0.102941 0.080882 43.480458 9.565701 30.436321 \n", + "0599100000617626 0.179856 0.071942 37.614699 16.568379 27.763230 \n", + "0599100000617896 0.312500 0.116071 11.224730 33.242468 10.361289 \n", + "0599100000617897 0.424370 0.088235 39.076432 62.616451 9.886808 \n", + "0599100000631878 0.225806 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32.555095 46.042205 \n", + "0599100000757178 0.076190 0.019048 34.967567 13.054558 26.109117 \n", + "0599100010034672 0.187291 0.137124 190.165286 151.560303 152.990117 \n", + "0599100010045360 0.270161 0.254032 65.297988 84.308795 46.287182 \n", + "0599100000627120 0.262195 0.341463 4.454347 64.588028 4.454347 \n", + "0599100000627124 0.194444 0.033333 88.774151 28.461942 36.593925 \n", + "0599100000627128 0.140000 0.025000 80.379692 38.953235 57.502395 \n", + "0599100000627132 0.300000 0.200000 1.916316 36.889077 1.916316 \n", + "0599100000627138 0.182390 0.062893 90.242899 53.533923 61.181626 \n", + "0599100000627140 0.190939 0.058252 93.959088 46.979544 60.826147 \n", + "0599100000690226 0.143579 0.054601 986.780687 204.946758 328.294344 \n", + "0599100000757111 0.182741 0.162437 52.442589 24.056234 42.820096 \n", + "0599100000757172 0.139665 0.089385 58.109202 27.410001 54.271802 \n", + "0599100000757174 0.058511 0.085106 75.947695 11.075706 57.488186 \n", + "0599100000767076 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"0599100000757176 22.184108 9.912048 9.912048 0.347826 \n", + "0599100000757177 13.487111 24.183785 8.371310 0.373134 \n", + "0599100000757178 17.716900 3.729874 0.932468 0.304348 \n", + "0599100010034672 110.095692 80.069594 58.622381 0.203390 \n", + "0599100010045360 28.102932 55.379307 52.073079 0.275000 \n", + "0599100000627120 39.346730 31.922819 41.573903 0.208791 \n", + "0599100000627124 54.213222 23.718285 4.065992 0.163636 \n", + "0599100000627128 44.517983 17.312549 3.091527 0.166667 \n", + "0599100000627132 17.246841 18.684078 12.456052 0.275000 \n", + "0599100000627138 51.494536 29.571119 10.196938 0.243902 \n", + "0599100000627140 50.935716 29.176770 8.901387 0.205128 \n", + "0599100000690226 632.551722 179.644689 68.315586 0.253731 \n", + "0599100000757111 17.801613 17.320488 15.395990 0.368421 \n", + "0599100000757172 17.542401 13.705000 8.771200 0.358025 \n", + "0599100000757174 25.843313 5.801560 8.438633 0.304348 \n", + "0599100000767076 77.200836 55.869026 13.713306 0.277778 \n", + "0599100000767077 69.452906 62.406959 13.085330 0.263158 \n", + "0599100000755931 951.655042 760.046644 530.116567 0.085714 \n", + "0599100000700214 924.890796 462.445398 84.080981 0.180723 \n", + "0599100000701988 1367.124352 2460.823833 3281.098444 0.057143 \n", + "\n", + "[11724 rows x 52 columns]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_df = pd.DataFrame({'ID': pd.Series(ID_list),\\\n", + " 'nasa_nonveg_area': pd.Series(nonveg_abs_list), \\\n", + " 'nasa_nonveg_pc': pd.Series(nonveg_percent_list), \\\n", + " 'nasa_ext_area': pd.Series(exten_abs_list),\\\n", + " 'nasa_ext_pc': pd.Series(exten_percent_list),\\\n", + " 'nasa_int_area': pd.Series(inten_abs_list),\\\n", + " 'nasa_int_pc': pd.Series(inten_percent_list),\\\n", + " 'nasa_tree_area': pd.Series(trees_abs_list),\\\n", + " 'nasa_tree_pc': pd.Series(trees_percent_list),\\\n", + " 'total_area': pd.Series(area_per_roof_list),\\\n", + " 'k2_01': pd.Series(k2_01),\\\n", + " 'k2_02': pd.Series(k2_02),\\\n", + " 'k4_01': pd.Series(k4_01),\\\n", + " 'k4_02': pd.Series(k4_02),\\\n", + " 'k4_03': pd.Series(k4_03),\\\n", + " 'k4_04': pd.Series(k4_04),\\\n", + " 'std': pd.Series(std),\\\n", + " 'var': pd.Series(var),\\\n", + " 'mean': pd.Series(mean),\\\n", + " 'min': pd.Series(min_value),\\\n", + " 'pct_05': pd.Series(pct_05),\\\n", + " 'pct_10': pd.Series(pct_10),\\\n", + " 'pct_15': pd.Series(pct_15),\\\n", + " 'pct_20': pd.Series(pct_20),\\\n", + " 'pct_25': pd.Series(pct_25),\\\n", + " 'pct_30': pd.Series(pct_30),\\\n", + " 'pct_35': pd.Series(pct_35),\\\n", + " 'pct_40': pd.Series(pct_40),\\\n", + " 'pct_45': pd.Series(pct_45),\\\n", + " 'pct_50': pd.Series(pct_50),\\\n", + " 'pct_55': pd.Series(pct_55),\\\n", + " 'pct_60': pd.Series(pct_60),\\\n", + " 'pct_65': pd.Series(pct_65),\\\n", + " 'pct_70': pd.Series(pct_70),\\\n", + " 'pct_75': pd.Series(pct_75),\\\n", + " 'pct_80': pd.Series(pct_80),\\\n", + " 'pct_85': pd.Series(pct_85),\\\n", + " 'pct_90': pd.Series(pct_90),\\\n", + " 'pct_95': pd.Series(pct_95),\\\n", + " 'pc_2_01': pd.Series(pc_2_01),\\\n", + " 'pc_2_02': pd.Series(pc_2_02),\\\n", + " 'pc_4_01': pd.Series(pc_4_01),\\\n", + " 'pc_4_02': pd.Series(pc_4_02),\\\n", + " 'pc_4_03': pd.Series(pc_4_03),\\\n", + " 'pc_4_04': pd.Series(pc_4_04),\\\n", + " 'area_2_01': pd.Series(area_2_01),\\\n", + " 'area_2_02': pd.Series(area_2_02),\\\n", + " 'area_4_01': pd.Series(area_4_01),\\\n", + " 'area_4_02': pd.Series(area_4_02),\\\n", + " 'area_4_03': pd.Series(area_4_03),\\\n", + " 'area_4_04': pd.Series(area_4_04),\\\n", + " 'max': pd.Series(max_value),})\n", + "\n", + "\n", + "raw_df['nasa_max_pc'] = raw_df[['nasa_nonveg_pc','nasa_ext_pc','nasa_int_pc', 'nasa_tree_pc']].max(axis=1)\n", + "raw_df.loc[(raw_df['nasa_nonveg_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '1'\n", + "raw_df.loc[(raw_df['nasa_ext_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '2'\n", + "raw_df.loc[(raw_df['nasa_int_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '3'\n", + "raw_df.loc[(raw_df['nasa_tree_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '4'\n", + "\n", + "\n", + "raw_df.set_index('ID', inplace=True)\n", + "\n", + "raw_df = raw_df[['nasa_nonveg_area', 'nasa_nonveg_pc', 'nasa_ext_area', 'nasa_ext_pc',\\\n", + " 'nasa_int_area', 'nasa_int_pc', 'nasa_tree_area', 'nasa_tree_pc', 'total_area', 'naive_label', \\\n", + " 'k2_01', 'k2_01', 'k4_01', 'k4_02', 'k4_03', 'k4_04', 'std', 'var', 'mean', \\\n", + " 'min', 'pct_05', 'pct_10', 'pct_15', 'pct_20', 'pct_25', 'pct_30', 'pct_35', 'pct_40', 'pct_45', \\\n", + " 'pct_50', 'pct_55', 'pct_60', 'pct_65', 'pct_70','pct_75', 'pct_80', 'pct_85', 'pct_90', 'pct_95',\\\n", + " 'pc_2_01', 'pc_2_02', 'pc_4_01', 'pc_4_02', 'pc_4_03', 'pc_4_04', 'area_2_01', 'area_2_02',\\\n", + " 'area_4_01', 'area_4_02', 'area_4_03', 'area_4_04', 'max']].dropna()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Output" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "raw_df.to_csv('processing/features_table.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/create_features.py b/create_features.py new file mode 100644 index 0000000..8e9dc3c --- /dev/null +++ b/create_features.py @@ -0,0 +1,359 @@ + +# coding: utf-8 + +# In[4]: + + +BAG_Panden = '/home/data/citycentre/BAG_Panden.shp' +CIR = '/home/data/processing/CIR_2015_10.tif' + + +# # Dependencies + +# In[5]: + + +import numpy as np +import matplotlib.pyplot as plt +from sklearn.cluster import KMeans +from sklearn.metrics import pairwise_distances_argmin +from sklearn.datasets import load_sample_image +from sklearn.utils import shuffle +from time import time +from PIL import Image +import os, glob +import pandas as pd +from shutil import copyfile +import matplotlib.image as mpimg +import numpy +import geopandas as gpd +import fiona +import rasterio +import rasterio.mask +import os +from shapely.geometry import shape +#get_ipython().magic('matplotlib inline') + + +# # Files + +# In[6]: + + +shapefile=gpd.read_file(BAG_Panden) + + +# In[9]: + + +### all_ids= [] +[all_ids.append(i) for i in shapefile['Identifica']] +print('We now ave all shapefile IDs') + + +# # Lists + +# In[79]: + + +# Old +nonveg_percent_list=[] +nonveg_abs_list=[] +inten_percent_list=[] +inten_abs_list=[] +exten_percent_list=[] +exten_abs_list=[] +trees_percent_list=[] +trees_abs_list=[] +total_abs_list = [] +area_per_roof_list = [] +mean = [] +max_value = [] +min_value = [] +nasa_classification_list = [] +average_vegetation = [] +ID_list = [] + + +# In[80]: + + +# Statistical information + +pct_05 = [] +pct_10 = [] +pct_15 = [] +pct_20 = [] +pct_25 = [] +pct_30 = [] +pct_35 = [] +pct_40 = [] +pct_45 = [] +pct_50 = [] +pct_55 = [] +pct_60 = [] +pct_65 = [] +pct_70 = [] +pct_75 = [] +pct_80 = [] +pct_85 = [] +pct_90 = [] +pct_95 = [] + +pc_1_08 = [] +pc_08_06 = [] +pc_06_04 = [] +pc_04_02 = [] +pc_02_00 = [] +pc_00_02 = [] +pc_02_04 = [] +pc_04_06 = [] +pc_06_08 = [] +pc_08_1 = [] + +var = [] +std = [] + + +# In[81]: + + +# Clusters + +k2_01 = [] +k2_02 = [] +k4_01 = [] +k4_02 = [] +k4_03 = [] +k4_04 = [] + +pc_2_01 = [] +pc_2_02 = [] +pc_4_01 = [] +pc_4_02 = [] +pc_4_03 = [] +pc_4_04 = [] + +area_2_01 = [] +area_2_02 = [] +area_4_01 = [] +area_4_02 = [] +area_4_03 = [] +area_4_04 = [] + + +# # Functions + +# In[82]: + + +def recreate_image(codebook, labels, w, h): + """Recreate the (compressed) image from the code book & labels""" + d = codebook.shape[1] + image = np.zeros((w, h)) + label_idx = 0 + for i in range(w): + for j in range(h): + image[i][j] = codebook[labels[label_idx]] + label_idx += 1 + return(image) + + +# In[83]: + + +def cluster_per_k(imarray, n_colors): + + # Load Image and transform to a 2D numpy array. + w, h= original_shape = tuple(imarray.shape) + + d = 1 + image_array = np.reshape(imarray, (w * h, d)) + + + + #print("Fitting model on a small sub-sample of the data") + t0 = time() + image_array_sample = shuffle(image_array, random_state=0)[:1000] + kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample) + + # Get labels for all points + #print("Predicting color indices on the full image (k-means)") + t0 = time() + labels = kmeans.predict(image_array) + + + codebook_random = shuffle(image_array, random_state=0)[:n_colors + 1] + #print("Predicting color indices on the full image (random)") + t0 = time() + labels_random = pairwise_distances_argmin(codebook_random, image_array, axis=0) + + + + #plt.title('Quantized image (2 colors, K-Means)') + return(recreate_image(kmeans.cluster_centers_, labels, w, h)) + + +# In[84]: + + +def get_table_of_clusters_from_id(roof_id, shp_file = "/home/data/citycentre/BAG_Panden.shp", raster_file = "/home/data/citycentre/lufo_2014.tif"): + try: + ID = roof_id + shapes = fiona.open(shp_file, "r") + roof_idx = [str(feat['properties']['Identifica']) for feat in shapes].index(roof_id) + area_per_roof = shapes[roof_idx]['properties']['SHAPE_Area'] + + + feat1 = [shapes[roof_idx]['geometry'] ] + with rasterio.open('/home/data/processing/CIR_2015_10.tif') as src: + out_image, out_transform = rasterio.mask.mask(src, feat1,crop=True, nodata=10, all_touched=True) + out_meta = src.meta.copy() + + + out_meta.update({"driver": "GTiff", + "height": out_image.shape[1], + "width": out_image.shape[2], + "transform": out_transform}) + + NIR = np.matrix(out_image[0, :, :]).astype(float) + NIR[NIR==10] = 90 + RED = np.matrix(out_image[1, :, :]).astype(float) + + ndvi = (NIR-RED)/(NIR+RED) + + ndvi_not_nan = ndvi[ndvi != .8] + ndvi[ndvi == .8] = np.nan + + k4_ndvi = cluster_per_k(np.array(ndvi_not_nan), 5) + k2_ndvi = cluster_per_k(np.array(ndvi_not_nan), 3) + + list_of_4_cluster_values = (np.unique(k4_ndvi.flatten())).tolist()[:4] + k4_01.append(list_of_4_cluster_values[0]) + k4_02.append(list_of_4_cluster_values[1]) + k4_03.append(list_of_4_cluster_values[2]) + k4_04.append(list_of_4_cluster_values[3]) + + list_of_2_cluster_values = (np.unique(k2_ndvi.flatten())).tolist()[:2] + k2_01.append(list_of_2_cluster_values[0]) + k2_02.append(list_of_2_cluster_values[1]) + + + total_abs = (((ndvi_not_nan < 0.088)).sum() + ((0.088 < ndvi_not_nan) & (ndvi_not_nan < 0.21)).sum() + ((0.21 < ndvi_not_nan) & (ndvi_not_nan < 0.276)).sum() + ((0.276 < ndvi_not_nan) & (ndvi_not_nan < 1)).sum()) + + nonveg_percent = (((ndvi_not_nan < 0.088)).sum())/total_abs + exten_percent = (((0.088 < ndvi_not_nan) & (ndvi_not_nan < 0.21)).sum())/total_abs + inten_percent = (((0.21 < ndvi_not_nan) & (ndvi_not_nan < 0.276)).sum())/total_abs + trees_percent = (((0.276 < ndvi_not_nan) & (ndvi_not_nan < 1)).sum())/total_abs + nonveg_abs = area_per_roof*nonveg_percent + exten_abs = area_per_roof*exten_percent + inten_abs = area_per_roof*inten_percent + trees_abs = area_per_roof*trees_percent + mean_roof = np.nanmean(ndvi_not_nan) + max_roof = np.nanmax(ndvi_not_nan) + min_roof = np.nanmin(ndvi) + + nonveg_percent_list.append(nonveg_percent) + nonveg_abs_list.append(nonveg_abs) + inten_percent_list.append(inten_percent) + inten_abs_list.append(inten_abs) + exten_percent_list.append(exten_percent) + exten_abs_list.append(exten_abs) + trees_percent_list.append(trees_percent) + trees_abs_list.append(trees_abs) + total_abs_list.append(total_abs) + area_per_roof_list.append(area_per_roof) + ID_list.append(ID) + + min_value.append(min_roof) + pct_05.append(np.percentile(ndvi_not_nan, 5)) + pct_10.append(np.percentile(ndvi_not_nan, 10)) + pct_15.append(np.percentile(ndvi_not_nan, 15)) + pct_20.append(np.percentile(ndvi_not_nan, 20)) + pct_25.append(np.percentile(ndvi_not_nan, 25)) + pct_30.append(np.percentile(ndvi_not_nan, 30)) + pct_35.append(np.percentile(ndvi_not_nan, 35)) + pct_40.append(np.percentile(ndvi_not_nan, 40)) + pct_45.append(np.percentile(ndvi_not_nan, 45)) + pct_50.append(np.percentile(ndvi_not_nan, 50)) + pct_55.append(np.percentile(ndvi_not_nan, 55)) + pct_60.append(np.percentile(ndvi_not_nan, 60)) + pct_65.append(np.percentile(ndvi_not_nan, 65)) + pct_70.append(np.percentile(ndvi_not_nan, 70)) + pct_75.append(np.percentile(ndvi_not_nan, 75)) + pct_80.append(np.percentile(ndvi_not_nan, 80)) + pct_85.append(np.percentile(ndvi_not_nan, 85)) + pct_90.append(np.percentile(ndvi_not_nan, 90)) + pct_95.append(np.percentile(ndvi_not_nan, 95)) + max_value.append(max_roof) + + pc_1_08.append((((-1 < ndvi_not_nan) & (ndvi_not_nan < -0.8)).sum())/total_abs) + pc_08_06.append((((-0.8 < ndvi_not_nan) & (ndvi_not_nan < -0.6)).sum())/total_abs) + pc_06_04.append((((-0.6 < ndvi_not_nan) & (ndvi_not_nan < -0.4)).sum())/total_abs) + pc_04_02.append((((-0.4 < ndvi_not_nan) & (ndvi_not_nan < -0.2)).sum())/total_abs) + pc_02_00.append((((-0.2 < ndvi_not_nan) & (ndvi_not_nan < 0)).sum())/total_abs) + pc_00_02.append((((0.0 < ndvi_not_nan) & (ndvi_not_nan < 0.2)).sum())/total_abs) + pc_02_04.append((((0.2 < ndvi_not_nan) & (ndvi_not_nan < 0.4)).sum())/total_abs) + pc_04_06.append((((0.4 < ndvi_not_nan) & (ndvi_not_nan < 0.6)).sum())/total_abs) + pc_06_08.append((((0.6 < ndvi_not_nan) & (ndvi_not_nan < 0.8)).sum())/total_abs) + pc_08_1.append((((0.8 < ndvi_not_nan) & (ndvi_not_nan < 1)).sum())/total_abs) + + pc_2_01.append(((k2_ndvi == list_of_2_cluster_values[0]).sum())/total_abs) + pc_2_02.append(((k2_ndvi == list_of_2_cluster_values[1]).sum())/total_abs) + pc_4_01.append(((k4_ndvi == list_of_4_cluster_values[0]).sum())/total_abs) + pc_4_02.append(((k4_ndvi == list_of_4_cluster_values[1]).sum())/total_abs) + pc_4_03.append(((k4_ndvi == list_of_4_cluster_values[2]).sum())/total_abs) + pc_4_04.append(((k4_ndvi == list_of_4_cluster_values[3]).sum())/total_abs) + + area_2_01.append((((k2_ndvi == list_of_2_cluster_values[0]).sum())/total_abs)*area_per_roof) + area_2_02.append((((k2_ndvi == list_of_2_cluster_values[1]).sum())/total_abs)*area_per_roof) + area_4_01.append((((k4_ndvi == list_of_4_cluster_values[0]).sum())/total_abs)*area_per_roof) + area_4_02.append((((k4_ndvi == list_of_4_cluster_values[1]).sum())/total_abs)*area_per_roof) + area_4_03.append((((k4_ndvi == list_of_4_cluster_values[2]).sum())/total_abs)*area_per_roof) + area_4_04.append((((k4_ndvi == list_of_4_cluster_values[3]).sum())/total_abs)*area_per_roof) + + + std.append(np.std(ndvi_not_nan)) + var.append(np.var(ndvi_not_nan)) + mean.append(np.nanmean(ndvi_not_nan)) + + except ValueError: + print('A building partly outside the AoI was just detected.') + + +# # Code + +# In[85]: + + +[get_table_of_clusters_from_id(x) for x in all_ids] +print('The wait is over.') + + +# # Dataframe + +# In[86]: + + +raw_df = pd.DataFrame({'ID': pd.Series(ID_list), 'nasa_nonveg_area': pd.Series(nonveg_abs_list), 'nasa_nonveg_pc': pd.Series(nonveg_percent_list), 'nasa_ext_area': pd.Series(exten_abs_list), 'nasa_ext_pc': pd.Series(exten_percent_list), 'nasa_int_area': pd.Series(inten_abs_list), 'nasa_int_pc': pd.Series(inten_percent_list), 'nasa_tree_area': pd.Series(trees_abs_list), 'nasa_tree_pc': pd.Series(trees_percent_list), 'total_area': pd.Series(area_per_roof_list), 'k2_01': pd.Series(k2_01), 'k2_02': pd.Series(k2_02), 'k4_01': pd.Series(k4_01), 'k4_02': pd.Series(k4_02), 'k4_03': pd.Series(k4_03), 'k4_04': pd.Series(k4_04), 'std': pd.Series(std), 'var': pd.Series(var), 'mean': pd.Series(mean), 'min': pd.Series(min_value), 'pct_05': pd.Series(pct_05), 'pct_10': pd.Series(pct_10), 'pct_15': pd.Series(pct_15), 'pct_20': pd.Series(pct_20), 'pct_25': pd.Series(pct_25), 'pct_30': pd.Series(pct_30), 'pct_35': pd.Series(pct_35), 'pct_40': pd.Series(pct_40), 'pct_45': pd.Series(pct_45), 'pct_50': pd.Series(pct_50), 'pct_55': pd.Series(pct_55), 'pct_60': pd.Series(pct_60), 'pct_65': pd.Series(pct_65), 'pct_70': pd.Series(pct_70), 'pct_75': pd.Series(pct_75), 'pct_80': pd.Series(pct_80), 'pct_85': pd.Series(pct_85), 'pct_90': pd.Series(pct_90), 'pct_95': pd.Series(pct_95), 'pc_2_01': pd.Series(pc_2_01), 'pc_2_02': pd.Series(pc_2_02), 'pc_4_01': pd.Series(pc_4_01), 'pc_4_02': pd.Series(pc_4_02), 'pc_4_03': pd.Series(pc_4_03), 'pc_4_04': pd.Series(pc_4_04), 'area_2_01': pd.Series(area_2_01), 'area_2_02': pd.Series(area_2_02), 'area_4_01': pd.Series(area_4_01), 'area_4_02': pd.Series(area_4_02), 'area_4_03': pd.Series(area_4_03), 'area_4_04': pd.Series(area_4_04), 'max': pd.Series(max_value),}) + + +raw_df['nasa_max_pc'] = raw_df[['nasa_nonveg_pc','nasa_ext_pc','nasa_int_pc', 'nasa_tree_pc']].max(axis=1) +raw_df.loc[(raw_df['nasa_nonveg_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '1' +raw_df.loc[(raw_df['nasa_ext_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '2' +raw_df.loc[(raw_df['nasa_int_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '3' +raw_df.loc[(raw_df['nasa_tree_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '4' + + +raw_df.set_index('ID', inplace=True) + +raw_df = raw_df[['nasa_nonveg_area', 'nasa_nonveg_pc', 'nasa_ext_area', 'nasa_ext_pc', 'nasa_int_area', 'nasa_int_pc', 'nasa_tree_area', 'nasa_tree_pc', 'total_area', 'naive_label', 'k2_01', 'k2_01', 'k4_01', 'k4_02', 'k4_03', 'k4_04', 'std', 'var', 'mean', 'min', 'pct_05', 'pct_10', 'pct_15', 'pct_20', 'pct_25', 'pct_30', 'pct_35', 'pct_40', 'pct_45', 'pct_50', 'pct_55', 'pct_60', 'pct_65', 'pct_70','pct_75', 'pct_80', 'pct_85', 'pct_90', 'pct_95', 'pc_2_01', 'pc_2_02', 'pc_4_01', 'pc_4_02', 'pc_4_03', 'pc_4_04', 'area_2_01', 'area_2_02', 'area_4_01', 'area_4_02', 'area_4_03', 'area_4_04', 'max']].dropna() + + +# # Output + +# In[87]: + + +raw_df.to_csv('2_processing/features_table.csv') +