diff --git a/conflict_model/__init__.py b/conflict_model/__init__.py index 56496f9..5a7f09d 100644 --- a/conflict_model/__init__.py +++ b/conflict_model/__init__.py @@ -7,4 +7,4 @@ __author__ = """Jannis M. Hoch""" __email__ = 'j.m.hoch@uu.nl' -__version__ = '0.0.1' +__version__ = '0.0.2b1' diff --git a/data/run_setting.cfg b/data/run_setting.cfg index e3214a0..eb2716e 100644 --- a/data/run_setting.cfg +++ b/data/run_setting.cfg @@ -23,4 +23,7 @@ code2class=KoeppenGeiger/classification_codes.txt [env_vars] #variable name here needs to be identical with variable name in nc-file GDP_per_capita_PPP=GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc -total_evaporation=PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc \ No newline at end of file +total_evaporation=PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc + +[machine_learning] +train_fraction=0.3 \ No newline at end of file diff --git a/example/example_notebook.html b/example/example_notebook.html index 552a3fe..41df227 100644 --- a/example/example_notebook.html +++ b/example/example_notebook.html @@ -13102,12 +13102,14 @@

Import libraries and file with import matplotlib.pyplot as plt import numpy as np import datetime +import csv import netCDF4 as nc import rasterstats as rstats import xarray as xr import rasterio as rio import seaborn as sbs from sklearn import svm, preprocessing, model_selection, metrics +from shutil import copyfile import os, sys @@ -13139,7 +13141,7 @@

Import libraries and file with 
Python version: 3.7.7 (default, Apr 15 2020, 05:09:04) [MSC v.1916 64 bit (AMD64)]
-conflict_model version: 0.0.1
+conflict_model version: 0.0.2b1
 geopandas version: 0.7.0
 xarray version: 0.15.1
 rasterio version: 1.1.0
@@ -13206,33 +13208,13 @@ 
Import libraries and file with
 

     

 
config = RawConfigParser(allow_no_value=True)
-config.read(settings_file)
+config.read(settings_file);
-
-
- - -
- -
Out[5]:
- - - - -
-
['../data/run_setting.cfg']
-
- -
- -
-
-
@@ -13269,6 +13251,19 @@

Import libraries and file with

+ +
+
+
In [7]:
+
+
+
copyfile(settings_file, os.path.join(out_dir, 'copy_of_run_setting.cfg'));
+
+ +
+
+
+
@@ -13279,7 +13274,7 @@

Applying functions
-
In [7]:
+
In [8]:
gdf = conflict_model.utils.get_geodataframe(config)
@@ -13315,7 +13310,7 @@ 
Applying functions
 

-
In [8]:

+
In [9]:

 

     

 
conflict_gdf, extent_gdf = conflict_model.selection.select(gdf, config)
@@ -13339,7 +13334,7 @@ 
Applying functionsAnalysis per year
 

-
In [9]:

+
In [10]:

 

     

 
print('simulation period from', str(config.getint('settings', 'y_start')), 'to', str(config.getint('settings', 'y_end')))
@@ -13422,7 +13417,7 @@ 
Analysis per year
-
simulation period from 2000 to 2011
+
simulation period from 2000 to 2015
 
 entering year 2000

 
@@ -14026,6 +14021,226 @@ 
Analysis per year

 
 
+

+
...DONE

+
+entering year 2011

+
+determining whether a conflict took place or not
+...DONE

+
+calculating mean GDP_per_capita_PPP per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2011
+

+

+

+
+

+
+    

+
+
+

+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+

+

+

+
+

+
+    

+
+
+

+
...DONE

+
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2011
+

+

+

+
+

+
+    

+
+
+

+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+

+

+

+
+

+
+    

+
+
+

+
...DONE

+
+entering year 2012

+
+determining whether a conflict took place or not
+...DONE

+
+calculating mean GDP_per_capita_PPP per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2012
+

+

+

+
+

+
+    

+
+
+

+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+

+

+

+
+

+
+    

+
+
+

+
...DONE

+
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2012
+

+

+

+
+

+
+    

+
+
+

+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+

+

+

+
+

+
+    

+
+
+

+
...DONE

+
+entering year 2013

+
+determining whether a conflict took place or not
+...DONE

+
+calculating mean GDP_per_capita_PPP per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2013
+

+

+

+
+

+
+    

+
+
+

+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+

+

+

+
+

+
+    

+
+
+

+
...DONE

+
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2013
+

+

+

+
+

+
+    

+
+
+

+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+

+

+

+
+

+
+    

+
+
+

+
...DONE

+
+entering year 2014

+
+determining whether a conflict took place or not
+...DONE

+
+calculating mean GDP_per_capita_PPP per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2014
+

+

+

+
+

+
+    

+
+
+

+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+

+

+

+
+

+
+    

+
+
+

+
...DONE

+
+calculating mean total_evaporation per aggregation unit from file C:\Users\hoch0001\Documents\_code\conflict_model\data\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2014
+

+

+

+
+

+
+    

+
+
+

+
C:\Users\hoch0001\AppData\Local\Continuum\anaconda3\envs\conflict_model\lib\site-packages\rasterstats\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly
+  warnings.warn("Setting nodata to -999; specify nodata explicitly")
+

+

+

+
+

+
+    

+
+
 

 
...DONE

 
@@ -14055,7 +14270,7 @@ 
Machine Learning
 

-
In [10]:

+
In [11]:

 

     

 
XY_data = list(zip(X1, X2, Y))
@@ -14079,8 +14294,8 @@ 
Machine Learning
-
4246
-4224
+
5790
+5760
 

 

 

@@ -14091,7 +14306,7 @@ 
Machine Learning
 

-
In [11]:

+
In [12]:

 

     

 
XY_data
@@ -14107,7 +14322,7 @@ 
Machine Learning
 
-    
Out[11]:

+    
Out[12]:

 
 
 
@@ -14173,38 +14388,38 @@ 
Machine LearningMachine Learning
 

-
In [12]:

+
In [13]:

 

     

 
X = XY_data[['GDP_PPP', 'ET']].to_numpy()
@@ -14241,7 +14456,7 @@ 
Machine Learning
 
-    
Out[12]:

+    
Out[13]:

 
 
 
@@ -14251,9 +14466,9 @@ 
Machine LearningMachine Learning
 

-
In [13]:

+
In [14]:

 

     

 
Y = XY_data.conflict.astype(int).to_numpy()
@@ -14281,7 +14496,7 @@ 
Machine Learning
 
-    
Out[13]:

+    
Out[14]:

 
 
 
@@ -14299,19 +14514,65 @@ 
Machine Learning

 

 

-
Before we can train and predict with the model, we need to scale the variable data and create trainings and test data for both variables and target.

+
Data preprocessing
Before we can train and predict with the model, we need to scale the variable data and create trainings and test data for both variables and target.

+
There are different scaling algorithms available. For our application, the MinMaxScaler and StandardScaler are the most obvious choices, the RobustScaler is follow-up. Depending on how incoming data looks like and is distributed, the scaling decision may need to be updated.

+
Depending on which scaler is chosen, the standardization of the data follows different approaches and may eventually influence model results. See here for some info: https://scikit-learn.org/stable/modules/preprocessing.html

 
 

 

 

 

 

-
In [14]:

+
In [15]:

 

     

-
X_train, X_test, y_train, y_test = model_selection.train_test_split(preprocessing.scale(X),
+
scaler = preprocessing.MinMaxScaler()
+# scaler = preprocessing.StandardScaler()
+# scaler = preprocessing.RobustScaler()
+

+
+    

+

+

+
+

+

+

+

+
The scaler is then used to fit the data and transform it according to scaler-specific method.

+
+

+

+

+

+

+
In [16]:

+

+    

+
X_scaled = scaler.fit_transform(X)
+

+
+    

+

+

+
+

+

+

+

+
The scaled variable data X_scaled is, together with the target data Y, split into trainings and test data. The fraction of the total data that is used for training is user-defined.

+
+

+

+

+

+

+
In [17]:

+

+    

+
X_train, X_test, y_train, y_test = model_selection.train_test_split(X_scaled,
                                                                     Y,
-                                                                    test_size=0.7)
+                                                                    test_size=1-config.getfloat('machine_learning', 'train_fraction'))
 

 
     

@@ -14322,23 +14583,25 @@ 
Machine Learning

 

 

-
The scatterplot of the (two) variables in X looks like this. Also the sample size n is provided.

+
The scatterplot of the (two) scaled variables in X_trainwe looks like this. Also the sample size n_train is provided used to train the data alongside with the total variable sample size n_tot.

 
 

 

 

 

 

-
In [15]:

+
In [18]:

 

     

-
plt.figure(figsize=(10,10))
+
plt.figure(figsize=(20,10))
 sbs.scatterplot(x=X_train[:,0],
                 y=X_train[:,1],  
                 hue=y_train)
 
-plt.title('n=' + str(len(X_train)))
-plt.savefig(os.path.join(out_dir, 'scatter_plot.png'), dpi=300)
+plt.title('training-data scaled with {0}; n_train={1}; n_tot={2}'.format(str(scaler).rsplit('(')[0], len(X_train), len(X_scaled)))
+plt.xlabel('Variable 1')
+plt.ylabel('Variable 2')
+plt.savefig(os.path.join(out_dir, 'scatter_plot_scaled_traindata_{}.png'.format(str(scaler).rsplit('(')[0])), dpi=300)
 plt.show()
 

 
@@ -14358,7 +14621,7 @@ 
Machine Learning
-
 

@@ -14368,13 +14631,21 @@ 
Machine Learning

+

+

+
A brief look at the statistics of the data of our scaled and standardized variables.

+
+

+

 

 

 

-
In [16]:

+
In [19]:

 

     

-
preprocessing.scale(X).mean(axis=0), preprocessing.scale(X).std(axis=0)
+
X_scaled.mean(axis=0), X_scaled.std(axis=0)
 

 
     

@@ -14387,13 +14658,13 @@ 
Machine Learning
 
-    
Out[16]:

+    
Out[19]:

 
 
 
 
 

-
(array([-4.71003707e-17,  6.39219317e-17]), array([1., 1.]))

+
(array([0.11131538, 0.38022174]), array([0.13922703, 0.23703751]))

 

 
 

@@ -14412,17 +14683,18 @@ 
Model

 

 

-
Create Support Vector Classification (SVC) model with balanced weight since data is unbalanced (e.g. many negative and few positive)

+
Create Support Vector Classification (SVC) model with balanced weight since data is unbalanced (e.g. many negative and few positive).

+
Note that there are many many settings in the svm.SVC class to be altered. At the moment, we stick to defaults besides the class_weight.

 
 

 

 

 

 

-
In [17]:

+
In [20]:

 

     

-
clf = svm.SVC(class_weight='balanced', C=0.9)
+
clf = svm.SVC(class_weight='balanced')
 

 
     

@@ -14440,51 +14712,28 @@ 
Model

 

-
In [18]:

+
In [21]:

 

     

-
clf.fit(X_train, y_train)
+
clf.fit(X_train, y_train);
 

 
     

 

 

 
-

-

-
-
-

-
-    
Out[18]:

-
-
-
-
-

-
SVC(C=0.9, break_ties=False, cache_size=200, class_weight='balanced', coef0=0.0,
-    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',
-    max_iter=-1, probability=False, random_state=None, shrinking=True,
-    tol=0.001, verbose=False)

-

-
-

-
-

-

-
 

 

 

 

-
Predict something with the scaled predition data

+
Predict with the scaled prediction data. We see an array with lots of 0 and 1 is produced, similar as the target data of the model, y_test.

 
 

 

 

 

 

-
In [19]:

+
In [22]:

 

     

 
y_pred = clf.predict(X_test)
@@ -14501,13 +14750,13 @@ 
Model

 
-    
Out[19]:

+    
Out[22]:

 
 
 
 
 

-
array([1, 0, 0, ..., 0, 1, 0])

+
array([0, 1, 0, ..., 1, 1, 1])

 

 
 

@@ -14526,7 +14775,7 @@ 
Model

 

-
In [20]:

+
In [23]:

 

     

 
y_score = clf.decision_function(X_test)
@@ -14543,14 +14792,14 @@ 
Model

 
-    
Out[20]:

+    
Out[23]:

 
 
 
 
 

-
array([ 0.71345701, -2.26722865, -1.34963997, ..., -1.12775605,
-        0.6524443 , -0.24453507])

+
array([-0.36311147,  0.28629907, -1.04040857, ...,  0.38083325,
+        1.1478144 ,  1.17343837])

 

 
 

@@ -14578,7 +14827,7 @@ 
Evaluation
 

 

-
In [21]:

+
In [24]:

 

     

 
print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
@@ -14600,9 +14849,9 @@ 
Evaluation
-
Accuracy: 0.669259384511329
-Precision: 0.09933142311365807
-Recall: 0.7482014388489209
+
Accuracy: 0.6661706349206349
+Precision: 0.10354609929078014
+Recall: 0.6403508771929824
 

 

 

@@ -14623,7 +14872,7 @@ 
Evaluation
 

 

-
In [22]:

+
In [25]:

 

     

 
average_precision = metrics.average_precision_score(y_test, y_score)
@@ -14645,7 +14894,7 @@ 
Evaluation
-
Average precision-recall score: 0.12
+
Average precision-recall score: 0.14
 

 

 

@@ -14656,11 +14905,13 @@ 
Evaluation
 

 

-
In [23]:

+
In [26]:

 

     

-
disp = metrics.plot_precision_recall_curve(clf, X_test, y_test)
-disp.ax_.set_title('2-class Precision-Recall curve: AP={0:0.2f}'.format(average_precision));
+
fig, ax = plt.subplots(1, 1, figsize=(20,10))
+disp = metrics.plot_precision_recall_curve(clf, X_test, y_test, ax=ax)
+disp.ax_.set_title('2-class Precision-Recall curve: AP={} with {}'.format(round(average_precision,2), str(scaler).rsplit('(')[0]))
+plt.savefig(os.path.join(out_dir, 'precision_recall_curve_{}.png'.format(str(scaler).rsplit('(')[0])), dpi=300)
 

 
     

@@ -14679,7 +14930,7 @@ 
Evaluation
-
 

@@ -14695,15 +14946,91 @@ 
Evaluation
 
Results are pretty crappy, but that is okay give we use not very sensible input data at the moment...

 
+

+

+

+

+

+

+
Documentation
Let's safe some settings used in this run to csv-files so results can be assessed in light of these settings.

+
+

+

+

+

+

+

+
First, the parameters of our SVM model.

+
+

+

+

+

+

+
In [27]:

+

+    

+
SVC_params = clf.get_params()
+
+out_fo = os.path.join(out_dir, 'SVC_params.csv')
+w = csv.writer(open(out_fo, "w"))
+for key, val in SVC_params.items():
+    w.writerow([key, val])
+

+
+    

+

+

+
+

+

+

+

+
Second, the parameters of the scaling method we used.

+
+

+

+

+

+

+
In [28]:

+

+    

+
scaler_params = scaler.get_params()
+
+out_fo = os.path.join(out_dir, '{}_params.csv'.format(str(scaler).rsplit('(')[0]))
+w = csv.writer(open(out_fo, "w"))
+for key, val in scaler_params.items():
+    w.writerow([key, val])
+

+
+    

+

+

+
+

+

+

+

+
Third, the resulting values of our objective functions.

+
 

 

 

 

 

-
In [ ]:

+
In [29]:

 

     

-
 
+
evaluation = {'Accuracy': round(metrics.accuracy_score(y_test, y_pred), 2),
+              'Precision': round(metrics.precision_score(y_test, y_pred), 2),
+              'Recall': round(metrics.recall_score(y_test, y_pred), 2),
+              'Average precision-recall score': round(average_precision, 2)}
+
+out_fo = os.path.join(out_dir, 'evaluation.csv')
+w = csv.writer(open(out_fo, "w"))
+for key, val in evaluation.items():
+    w.writerow([key, val])
 

 
     

diff --git a/example/example_notebook.ipynb b/example/example_notebook.ipynb
index c45e3eb..ad07c18 100644
--- a/example/example_notebook.ipynb
+++ b/example/example_notebook.ipynb
@@ -30,12 +30,14 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import datetime\n",
+    "import csv\n",
     "import netCDF4 as nc\n",
     "import rasterstats as rstats\n",
     "import xarray as xr\n",
     "import rasterio as rio\n",
     "import seaborn as sbs\n",
     "from sklearn import svm, preprocessing, model_selection, metrics\n",
+    "from shutil import copyfile\n",
     "import os, sys"
    ]
   },
@@ -49,7 +51,7 @@
      "output_type": "stream",
      "text": [
       "Python version: 3.7.7 (default, Apr 15 2020, 05:09:04) [MSC v.1916 64 bit (AMD64)]\n",
-      "conflict_model version: 0.0.1\n",
+      "conflict_model version: 0.0.2\n",
       "geopandas version: 0.7.0\n",
       "xarray version: 0.15.1\n",
       "rasterio version: 1.1.0\n",
@@ -92,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,28 +103,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['../data/run_setting.cfg']"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "config = RawConfigParser(allow_no_value=True)\n",
-    "config.read(settings_file)"
+    "config.read(settings_file);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -142,6 +133,15 @@
     "print('for the record, saving output to folder {}'.format(out_dir) + os.linesep)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "copyfile(settings_file, os.path.join(out_dir, 'copy_of_run_setting.cfg'));"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -184,7 +184,7 @@
       "...filtering key best with lower value 5\n",
       "...filtering key type_of_violence with value 1\n",
       "...passing key country as it is empty\n",
-      "focussing on period between 2000 and 2011\n",
+      "focussing on period between 2000 and 2015\n",
       "\n",
       "reading extent and spatial aggregation level from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\waterProvinces/waterProvinces_Africa.shp\n",
       "...DONE\n",
@@ -220,21 +220,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "simulation period from 2000 to 2011\n",
+      "simulation period from 2000 to 2015\n",
       "\n",
       "entering year 2000\r\n",
       "\n",
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2000\n"
      ]
     },
     {
@@ -251,7 +251,7 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2000\n"
      ]
     },
     {
@@ -273,7 +273,7 @@
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2001\n"
      ]
     },
     {
@@ -290,7 +290,7 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2001\n"
      ]
     },
     {
@@ -312,7 +312,7 @@
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2002\n"
      ]
     },
     {
@@ -329,7 +329,7 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2002\n"
      ]
     },
     {
@@ -351,7 +351,7 @@
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2003\n"
      ]
     },
     {
@@ -368,7 +368,7 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2003\n"
      ]
     },
     {
@@ -390,7 +390,7 @@
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2004\n"
      ]
     },
     {
@@ -407,7 +407,7 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2004\n"
      ]
     },
     {
@@ -429,7 +429,7 @@
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2005\n"
      ]
     },
     {
@@ -446,7 +446,7 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2005\n"
      ]
     },
     {
@@ -468,7 +468,7 @@
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2006\n"
      ]
     },
     {
@@ -485,7 +485,7 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2006\n"
      ]
     },
     {
@@ -507,7 +507,7 @@
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2007\n"
      ]
     },
     {
@@ -524,7 +524,7 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2007\n"
      ]
     },
     {
@@ -546,7 +546,7 @@
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2008\n"
      ]
     },
     {
@@ -563,7 +563,7 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2008\n"
      ]
     },
     {
@@ -585,7 +585,7 @@
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2009\n"
      ]
     },
     {
@@ -602,7 +602,7 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2009\n"
      ]
     },
     {
@@ -624,7 +624,46 @@
       "determining whether a conflict took place or not\n",
       "...DONE\r\n",
       "\n",
-      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc\n"
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2010\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2010\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "entering year 2011\r\n",
+      "\n",
+      "determining whether a conflict took place or not\n",
+      "...DONE\r\n",
+      "\n",
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2011\n"
      ]
     },
     {
@@ -641,7 +680,124 @@
      "text": [
       "...DONE\r\n",
       "\n",
-      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc\n"
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2011\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "entering year 2012\r\n",
+      "\n",
+      "determining whether a conflict took place or not\n",
+      "...DONE\r\n",
+      "\n",
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2012\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2012\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "entering year 2013\r\n",
+      "\n",
+      "determining whether a conflict took place or not\n",
+      "...DONE\r\n",
+      "\n",
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2013\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2013\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "entering year 2014\r\n",
+      "\n",
+      "determining whether a conflict took place or not\n",
+      "...DONE\r\n",
+      "\n",
+      "calculating mean GDP_per_capita_PPP per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\GDP_HDI/GDP_per_capita_PPP_1990_2015_Africa.nc for year 2014\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hoch0001\\AppData\\Local\\Continuum\\anaconda3\\envs\\conflict_model\\lib\\site-packages\\rasterstats\\io.py:301: UserWarning: Setting nodata to -999; specify nodata explicitly\n",
+      "  warnings.warn(\"Setting nodata to -999; specify nodata explicitly\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...DONE\r\n",
+      "\n",
+      "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\conflict_model\\data\\PCRGLOBWB/totalEvap/totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2014\n"
      ]
     },
     {
@@ -718,8 +874,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4246\n",
-      "4224\n"
+      "5790\n",
+      "5760\n"
      ]
     }
    ],
@@ -800,38 +956,38 @@
        "      ...\n",
        "    \n",
        "    \n",
-       "      4241\n",
-       "      3277.156738\n",
-       "      0.060997\n",
+       "      5785\n",
+       "      3710.418701\n",
+       "      0.067678\n",
        "      0\n",
        "    \n",
        "    \n",
-       "      4242\n",
-       "      3277.156738\n",
-       "      0.068696\n",
+       "      5786\n",
+       "      3710.418701\n",
+       "      0.073053\n",
        "      0\n",
        "    \n",
        "    \n",
-       "      4243\n",
-       "      1381.966901\n",
-       "      0.048329\n",
+       "      5787\n",
+       "      1700.190578\n",
+       "      0.045663\n",
        "      0\n",
        "    \n",
        "    \n",
-       "      4244\n",
-       "      1390.211488\n",
-       "      0.052157\n",
+       "      5788\n",
+       "      1710.275059\n",
+       "      0.043818\n",
        "      0\n",
        "    \n",
        "    \n",
-       "      4245\n",
-       "      1391.689834\n",
-       "      0.052872\n",
+       "      5789\n",
+       "      1712.023575\n",
+       "      0.053515\n",
        "      0\n",
        "    \n",
        "  \n",
        "\n",
-       "
4224 rows × 3 columns
\n",
+       "
5760 rows × 3 columns
\n",
        "
"
       ],
       "text/plain": [
@@ -842,13 +998,13 @@
        "3     1275.859490  0.025305         0\n",
        "4     1182.202026  0.036308         0\n",
        "...           ...       ...       ...\n",
-       "4241  3277.156738  0.060997         0\n",
-       "4242  3277.156738  0.068696         0\n",
-       "4243  1381.966901  0.048329         0\n",
-       "4244  1390.211488  0.052157         0\n",
-       "4245  1391.689834  0.052872         0\n",
+       "5785  3710.418701  0.067678         0\n",
+       "5786  3710.418701  0.073053         0\n",
+       "5787  1700.190578  0.045663         0\n",
+       "5788  1710.275059  0.043818         0\n",
+       "5789  1712.023575  0.053515         0\n",
        "\n",
-       "[4224 rows x 3 columns]"
+       "[5760 rows x 3 columns]"
       ]
      },
      "execution_count": 13,
@@ -879,9 +1035,9 @@
        "       [3.10405169e+03, 4.05202232e-02],\n",
        "       [1.19202521e+03, 3.92765536e-02],\n",
        "       ...,\n",
-       "       [1.38196690e+03, 4.83292063e-02],\n",
-       "       [1.39021149e+03, 5.21571179e-02],\n",
-       "       [1.39168983e+03, 5.28718745e-02]])"
+       "       [1.70019058e+03, 4.56631968e-02],\n",
+       "       [1.71027506e+03, 4.38179032e-02],\n",
+       "       [1.71202357e+03, 5.35152570e-02]])"
       ]
      },
      "execution_count": 14,
@@ -919,37 +1075,77 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before we can train and predict with the model, we need to scale the variable data and create trainings and test data for both variables and target."
+    "## Data preprocessing\n",
+    "\n",
+    "Before we can train and predict with the model, we need to scale the variable data and create trainings and test data for both variables and target.\n",
+    "\n",
+    "There are different scaling algorithms available. For our application, the MinMaxScaler and StandardScaler are the most obvious choices, the RobustScaler is follow-up. Depending on how incoming data looks like and is distributed, the scaling decision may need to be updated.\n",
+    "\n",
+    "Depending on which scaler is chosen, the standardization of the data follows different approaches and may eventually influence model results. See here for some info: https://scikit-learn.org/stable/modules/preprocessing.html"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = preprocessing.MinMaxScaler()\n",
+    "# scaler = preprocessing.StandardScaler()\n",
+    "# scaler = preprocessing.RobustScaler()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The scaler is then used to fit the data and transform it according to scaler-specific method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_scaled = scaler.fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The scaled variable data X_scaled is, together with the target data Y, split into trainings and test data. The fraction of the total data that is used for training is user-defined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_train, X_test, y_train, y_test = model_selection.train_test_split(preprocessing.scale(X),\n",
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X_scaled,\n",
     "                                                                    Y,\n",
-    "                                                                    test_size=0.7)"
+    "                                                                    test_size=1-config.getfloat('machine_learning', 'train_fraction'))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The scatterplot of the (two) variables in X looks like this. Also the sample size n is provided."
+    "The scatterplot of the (two) scaled variables in X_trainwe looks like this. Also the sample size n_train is provided used to train the data alongside with the total variable sample size n_tot."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
      "metadata": {
@@ -959,34 +1155,43 @@
     }
    ],
    "source": [
-    "plt.figure(figsize=(10,10))\n",
+    "plt.figure(figsize=(20,10))\n",
     "sbs.scatterplot(x=X_train[:,0],\n",
     "                y=X_train[:,1],  \n",
     "                hue=y_train)\n",
     "\n",
-    "plt.title('n=' + str(len(X_train)))\n",
-    "plt.savefig(os.path.join(out_dir, 'scatter_plot.png'), dpi=300)\n",
+    "plt.title('training-data scaled with {0}; n_train={1}; n_tot={2}'.format(str(scaler).rsplit('(')[0], len(X_train), len(X_scaled)))\n",
+    "plt.xlabel('Variable 1')\n",
+    "plt.ylabel('Variable 2')\n",
+    "plt.savefig(os.path.join(out_dir, 'scatter_plot_scaled_traindata_{}.png'.format(str(scaler).rsplit('(')[0])), dpi=300)\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A brief look at the statistics of the data of our scaled and standardized variables."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([-4.71003707e-17,  6.39219317e-17]), array([1., 1.]))"
+       "(array([0.11131538, 0.38022174]), array([0.13922703, 0.23703751]))"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "preprocessing.scale(X).mean(axis=0), preprocessing.scale(X).std(axis=0)"
+    "X_scaled.mean(axis=0), X_scaled.std(axis=0)"
    ]
   },
   {
@@ -1002,16 +1207,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Create Support Vector Classification (SVC) model with balanced weight since data is unbalanced (e.g. many negative and few positive)"
+    "Create Support Vector Classification (SVC) model with balanced weight since data is unbalanced (e.g. many negative and few positive).\n",
+    "\n",
+    "Note that there are many many settings in the svm.SVC class to be altered. At the moment, we stick to defaults besides the class_weight."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "clf = svm.SVC(class_weight='balanced', C=0.9)"
+    "clf = svm.SVC(class_weight='balanced')"
    ]
   },
   {
@@ -1023,46 +1230,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "SVC(C=0.9, break_ties=False, cache_size=200, class_weight='balanced', coef0=0.0,\n",
-       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
-       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
-       "    tol=0.001, verbose=False)"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "clf.fit(X_train, y_train)"
+    "clf.fit(X_train, y_train);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Predict something with the scaled predition data"
+    "Predict with the scaled prediction data. We see an array with lots of 0 and 1 is produced, similar as the target data of the model, y_test."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 0, 0, ..., 0, 1, 0])"
+       "array([1, 1, 0, ..., 1, 1, 1])"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1081,17 +1274,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-0.60452387, -1.03416277, -0.95833466, ..., -0.6758252 ,\n",
-       "        1.07185603, -1.04955169])"
+       "array([ 0.95987488,  0.99926936, -3.2308773 , ...,  1.00562196,\n",
+       "        1.02875354,  1.09024885])"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1121,16 +1314,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 0.6604666892120392\n",
-      "Precision: 0.09620721554116558\n",
-      "Recall: 0.7938931297709924\n"
+      "Accuracy: 0.5627480158730159\n",
+      "Precision: 0.09360126916975145\n",
+      "Recall: 0.7831858407079646\n"
      ]
     }
    ],
@@ -1153,14 +1346,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average precision-recall score: 0.12\n"
+      "Average precision-recall score: 0.11\n"
      ]
     }
    ],
@@ -1172,14 +1365,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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93dpe4O6lZjYBeBlIBh5x96Vmdjew0N2nh9tGmtkygiON2+JIMCIichjFmwiGhz9zY9Y5MOJAL3L3F4AXqqz7ecxzB74XPkREpBHElQjc/ZxEByIiIo0j3rOGWpvZAxWncJrZb8ysdaKDExGRxIt3sPgRoAi4InzsAP6aqKBERKThxDtG0MfdL4tZvsvMFiUiIBERaVjxHhHsMbPTKxbM7DRgT2JCEhGRhhTvEcE3gcnhuIABW4DxiQpKREQaTrxnDS0CjjOzVuHyjoRGJSIiDeaAicDMrnb3x8zse1XWA+DuDyQwNhERaQB1HRG0DH9mJzoQERFpHAdMBO7+UPjzroYJR0REGlq8F5Tdb2atzCzVzF4zs81mdnWigxMRkcSL9/TRkeEA8UUEt47uD9yWsKhERKTBxJsIUsOfFwBT3H1LguIREZEGFu91BM+Z2UcEF5HdYmYdgb2JC0tERBpKXEcE7n47cAqQG84mtovqE9GLiEgTVNd1BCPcfYaZfTlmXWyRpxIVmIiINIy6uobOAmYAF9ewzVEiEBFp8uq6juCO8OfXGyYcERFpaPFeR/D/zKxNzHJbM/tF4sISEZGGEu/po6PdfVvFgrtvJTiVVEREmrh4E0GymaVXLJhZJpB+gPIiItJExHsdwWPAa2b2V4JB4uuAyQmLSkREGky88xHcb2ZLgPMIJqa5x91fTmhkIiLSIOI9IgD4ECh191fNrIWZZbt7UaICExGRhhHvWUM3AE8CD4WrugHPJCooERFpOPEOFn8LOA3YAeDuK4BOiQpKREQaTryJoNjd91UsmFkKwaCxiIg0cfEmgjfM7MdAppmdD/wTeC5xYYmISEOJNxH8ECgA3gduAl4AfpqooEREpOHUedaQmSUBS9x9MPCXxIckIiINqc4jAncvBxab2dENEI+IiDSweLuGugJLw4nrp1c86nqRmY0ys+Vmlmdmtx+g3OVm5maWG2/gIiJyeMR7Qdld9a3YzJKBB4HzCSa8X2Bm0919WZVy2cB3gPn1fQ8RETl0dc1QlgHcDPQlGCie5O6lcdZ9EpDn7ivDuqYSTG+5rEq5e4D7gR/UI24RETlM6uoamgzkEiSB0cBv6lF3N2BdzHJ+uK6SmR0PdHf35w9UkZndaGYLzWxhQUFBPUIQEZG61NU1NNDdhwCY2STg7XrUbTWsq7wILTwb6bfA+LoqcveJwESA3NxcXcgmInIY1XVEUFLxpB5dQhXyge4xyznAhpjlbGAw8LqZrQZOBqZrwFhEpGHVdURwnJntCJ8bwZXFO8Ln7u6tDvDaBUA/M+sFrAfGAl+t2Oju24EOFctm9jrwA3dfWO9WiIjIQatr8vrkg63Y3UvNbALwMpAMPOLuS83sbmChu9d5+qmIiCRefeYjqDd3f4HgdhSx635eS9mzExmLiIjULN4LykREpJlSIhARiTglAhGRiFMiEBGJOCUCEZGIUyIQEYm4yCSCZRuD6+KumfQ235i8gLWFuxs5IhGRI0NkEsHsFZsB+KyomFc//IwFq7c0ckQiIkeGyCSCCv87dlhjhyAickSJXCIQEZH9KRGIiEScEoGISMQpEYiIRJwSgYhIxCkRiIhEnBKBiEjEKRGIiEScEoGISMQpEYiIRJwSgYhIxCkRiIhEnBKBiEjEKRGIiEScEoGISMQpEYiIRJwSgYhIxCkRiIhEnBKBiEjEKRGIiEScEoGISMQlNBGY2SgzW25meWZ2ew3bv2dmy8xsiZm9ZmY9EhmPiIhUl7BEYGbJwIPAaGAgMM7MBlYp9h6Q6+5DgSeB+xMVj4iI1CyRRwQnAXnuvtLd9wFTgUtiC7j7THffHS7OA3ISGM9+nnovn3+9k0/+1t11FxYRacYSmQi6AetilvPDdbW5Hnixpg1mdqOZLTSzhQUFBYcU1Kfb9wIwJ6+Q7/9zMb988aNDqk9EpKlLSWDdVsM6r7Gg2dVALnBWTdvdfSIwESA3N7fGOuK1r6wcgAFdsiktd4pLyg+lOhGRJi+RRwT5QPeY5RxgQ9VCZnYe8BNgjLsXJzCe/bTPSiMtWSdNiYgk8ptwAdDPzHqZWRowFpgeW8DMjgceIkgCnyUwFhERqUXCuobcvdTMJgAvA8nAI+6+1MzuBha6+3Tgf4As4J9mBrDW3cckKqbalJaV8+HGIsrcSU9JYkCXbCbNXsWbKzYDQTfSjy84tqHDEhFpEIkcI8DdXwBeqLLu5zHPz0vk+8fr7/PWcNdzyyqXJ193ElPeXkvhrn1s213Cmx8XsGdfGZlpyXzrnL60zkxtxGhFRA4vdZIDRXtLAfjxBQMAuPaRt/mkYBfbdpdUlnlm0XomvrmSt1dtaZQYRUQSRYkgRtfWmbVum3LDyQC4H9JJSyIiRxwlghqMHtyl1m3zV21h3spCdhWXNmBEIiKJo0QQpzWFwRXIk2avYuzEefzqJV2IJiLNQ0IHi5uyId1ac2KPtqzdspsZH31WeQQwNKc1S/K387e5a9i6u4SPNxVx8XFdyUxL4arhR5ORmrxfPWsLd/OlP85hV3EpZnDHxYMYd9LRAJSVO299spnd+8pIMuPk3u3ISk+pHJvISE0mM23/+qRh7Sou5ZOCnZQ7bC4qplOrdFKSgjPLkpJqumayuu17Sigvd1KSjewMnWggRx4lglo89+3T91uetiC4W8YxnbNZkr89KLM4uD5u+X+KKred3q8DEJySunLzLhat3caWXfu4+LijeHnpJpZvKqqsc+HqLXxt0tu1xpCWksSbt51Dl9YZlevKyoMxCoO4v4ikZovWbWPDtj28u2Yr67ftYdvuEhas3kJSkrGv9MBXnF97Sg+6tM5k4/Y9ZKYmU7CzmCtzu/PKsk/p2iaTF97fSFm5s2jdtlrrOK1ve/p2zOKuSwYf7qaJ1EukE8GyjTtYtnEH768P/lkrTgtt06L6XltqioU/q/emTbvpFK54aC5l7mzdtY/12/bw21c+5rWPPr9G7vrTe/Hc4g08+tZqHn1rNQAdstIBeOCK4/jetMX71fnlE7rx1Lvr+cuslbRIS6Z/52wenr2KxTFfLP837njyt+6hc6t0pr69joVrttCrQ0vaZ6Xzt+tOqnZ00tzs2FvCvtJynl+8gU07inl9+Wd8tKmIk3u3Y97KLZWf+Yk92jJvZSHb9pRwoLH+JINyhzFDujI9TPLnHduZti1SaZeVxkNvrOTeSwfzk6c/YPLcNdVe/9S762ut+zsj+vL7GXn07tCSlZt3AcH9rubkFTJ57hoyUpP4502nMiSn9aF9KCIHIdKJoMKxXVtx0dAszuzfkW+e3Yf/OrdftTJjjuvGw7NWcfvoATwxf+1+25Jj9sy/8tBc8j7bWbn8x6tOoGV6CkO7Vf8HP7NfB7IyUrhgSFe+N20xZ/TrwMCurXhzxWbSU4Iv8UmzV1V7XUZqEntLyvn2lPeqbctKT+HtVVsoKCqme7sW8X8INfj40yLeW7sVgG5tWlQe7QB8sH575d5uj/YtOKNfx2qvL9xZzCvLPqXcYf223ZzRryOL1m2jrNwpLi2noGgvw3u159lF6zmxR1te/fAzctpmUu7OyoJdnNW/I4/PX8vlJ+bwyrJPObZrK7bv2ceyDTvo3q4FH8UcXcWatzI4xbfis3vxg02kJhspScZRbTJZU7ibK3JzmLYwn198aTBpyUl0aZ3Bmf0/b8Pvxx1frd4fjT4Wd+cnT3/Ad87tR4u0ZE7u3Z6ycueyP73FE98YzsOzV/GziwbSJjOVNi1SCS+UBOB7I48BPj/z7A8z8vjNKx9XxvLlP82hpMzJSk9hcLdWFBQV851z+5GSlMSZ/TuQkpSkrkJJCGtqp0Pm5ub6woUL6/26E+95hcJd+/jd2GH819RFnNa3PXPyCgFYfd+F9arrtPtmMHJQZy4Y0pUX39/EyEGdGTtxHgO6ZPPRpiI6ZqdTUFRcre6et/8bgLTkJPaVlR/wfR945WN+/9oKfv2V4/jBP4OjhbFf6E52RgpJScZDb6zkpxceyy/+/SGdstM5o19HWqYnY1C5t9o6M5U7xwykY1YGH27cgRkU7Cxm5MDOpCQlMbhba3bsKWH5p0W4w+59pRzVJpOfPP0+mWnJlZ9PhaNaZ7AhvHtrVX/46vHc/dwyBnRtxZsfH9odYuPRvV0m23aX8I3Te9M+K40vDgrO9CraW0K3tpn8z0vL+elFA5m2YB2n9etAtza1nxrc2D5Yv52L/m92XGW/d35/vlPDjopIXczsHXfPrWmbjggOwpzbR1Q+/0LPdpV7nhV7qAVFxSz/xSj27tu/n/npW04lp20LOmSlHbCLAuDW8/oxsGs2owZ3pXVmKsO6t6FjdtCV5O58cVAXTji6LVef3IO05KTK8YJvTA6SZJ+OLfmkYBe3/mNxtbofemNlXO084eg2nNGvI0++k8/6bXsoLd8/6AuGdOHFDzbhDhOeCI5OPiv6PAlcMuwoOrfKYMxxR3HR/83mv0cdw9PvrufKL3THHX7zynKevuU0vv7XBbz03TN49K3VnNqnA+7O3JWFfHtEP+6Y/gF3jxnMX99azfBe7Widmcr6bXs4uXf7GmOu+Ix+elEwB9IVX+heY7kjyeBurXn7J+fSKTuD4tIyks3IK9jJY/PWcOnxOVz2p7e4IjeHFz/YxNotmj9DDj8dEVD/I4KqHp61kl/8+0OuP70XXx1+NO1bptGmRdoh1Xmwtu8u4df/Wc7towcw6I6XK9enpyRx1fAePDJnFT8aPaDaPAxDurXm/fXbuf/yobzw/kZ+e8Uw2rZMq6xz3qpCvjioC0s3bOfYLq0qE8/f567mZ88uZfqE03h+yUZu++IxbN29j4zUZFrpDJnDquKI8qSe7di+p4Qbz+zNk+/kk9uzLe5wzoBOdG+byYbte0lJMlqmp9CrQ8tGjlqOFDoiSLCKvvORAzvTp2NWo8bSukUq93wpOAvl9R+czVFtMkmLGeD++cXBnvKMjz7j3ksH07dTdrU6rsjdfy+6dYvUyq6XQUftP9bxtVN68rVTegIwNKcNAJ2yM5DEeXt1MAby/bDLcO7KYIfmDzPzqpU955iO9O+SzSXHdaN3x5b7HT2KVIhcIjipVzsAbh91LBf/Ib5+2boM6NLqkI8qEqHnAfYG/3HTKQ0YiRwOFX9jyzcV8cyi9Vx2Qg7LNxVx4dCujPrfN3GHnLaZ5G/dQ/8u2Ty3eAMzlxcwc3lBZXfgKb3bM+XGkxuzGXIEilwiSE1OOiK/tEXidUyXbH44KrhBYt9OwRHoS989s1q5b4/oS//O2Zx076tkpiWzpnA3c1cW8v1pi8nOSOH20QOa/SnGEp/IJQKRqOjfOej2e/snwd3e+//kRfaVlfOvd/MBKq9nqZCZmsz/jh3Gui27GdKtNZ8WFdM5O53te0rYtruENi1Sadcyjdye7dizr4yi4hKKS8rZvqeE0nJnSf422rZIY/G6bfTvks2qzbso2ltCy7QUZnz0GaMGd2F23mY6Zafz+vICstJTGNA1mzl5hYwY0Im5nxTSuVU6qwv3HxCPPVuta+sMvnFGb64/vVeCP71oiXQiqLhQSyQKPr53NABL8rcx5g9zKi+8a5WRwo69pewpKeOmv7+TsPdfMSMYw0hNNkrKnOLSfZUnbKzavIuO2em0qmGujz6dstiwfS/HdM5m+adF3PP8Mu55Ppg/5OxjOrK7uIzvj+yPmVFaVo6ZkWRwVJtM9pWV0zIt+Jrr3Cp9v+s65HORTgTL7h7V2CGINLihOW2qdY8uyd/GrBWbOalXO+6cvpQfX3AsD87M4+xjOlJS5qQkGa0yU/nRU+9z9clH89i8tXz5hG5s311CSrIxanAXPtxYxBW5OUxfvJEz+3WgtNzp3CqDDllp7Nhbeliu5Tj+7v+wNWaekNeXB6crXzlxXlyvz0xNJqdtJo+M/wJFe0vJSE1iV3EZGalJlHtwX6iW6cm4Q6fsdAp2FlNcWk5ZuVNa5rTPSiMrPYWjjuDrUg5G5E4fXfjT8ypv7SAiTZu7M/K3b3LNKT1YumEHvTq0pLi0nGcWrefGM3pz+1PvV154WXEkcji0a5nGll37gODOBF/o2ZYffPEY0pKTjthxlwOdPqpEICKR8dYnm7nvxZJYKGUAAA5QSURBVI/46klH84t/f8idYwbxr3fy6d85i7SUJDZs28vIQZ35r6mLuGvMIObkbSa3Z1u27i5hZcFOerRvycQ3V1beRaAm157Sg06tMrjshJz9bhjZ2JQIUCIQkcT458J1PDgzj3Kn2pXf7VumMXpIF37xpSGNFN3ndEGZiEiCfCW3O18JL8Is2ltCZmoyj761mtWFu3h9eQGPzVtL7N1ZSkrLGd67PUe3a0HfTlm0a9k4dyGI1SyOCEpKSsjPz2fv3ppviAawcdseyjw4/SxZV1Ye0TIyMsjJySE1VbeokKZt4puf8Oc3VpIUnq1UVl6+32B3n44tmXzdSQC4B9c5Jao7qdl3Da1atYrs7Gzat29f6+lhyzbsoLS8nGO7tiI1WTN0HqncncLCQoqKiujVS+eKS/OT99lOPt2xlwlPvLtfUojVKiMFd9hXVk5xaTn9OmWRkpzEhHP6cuHQrgf1vs2+a2jv3r307NlT5wg3A2ZG+/btKShI/K2sRRpD305Z9O2UxdO3nMb8VYUYFkw5CMxesZm24TwWZrBh2x527yurPBOpZXpizkhqFokAUBJoRvS7lCjo2aFltfuBVb3hY0NRH4mISMQpERwm9957L4MGDWLo0KEMGzaM+fPnc+edd/KjH/1ov3KLFi3i2GOPBWDnzp3cdNNN9OnTh0GDBnHmmWcyf/78anW7OyNGjGDHjh2V655++mnMjI8++nxegdWrV5OZmcmwYcMYOHAgN998M+XlB56EvS7FxcVceeWV9O3bl+HDh7N69eoay1133XV06tSJwYP3n4j9tttuY8CAAQwdOpRLL72UbduC6S3ff/99xo8ff0ixicjhoURwGMydO5fnn3+ed999lyVLlvDqq6/SvXt3xo0bxz/+8Y/9yk6dOpWvfvWrAHzjG9+gXbt2rFixgqVLl/Loo4+yefPmavW/8MILHHfccbRq1apy3ZQpUzj99NOZOnXqfmX79OnDokWLWLJkCcuWLeOZZ545pLZNmjSJtm3bkpeXx6233soPf/jDGsuNHz+el156qdr6888/nw8++IAlS5bQv39/fvnLXwIwZMgQ8vPzWbt2bbXXiEjDajZjBBXuem4pyzbsqLZ+974y3J0W6SnUtwd64FGtuOPiQbVu37hxIx06dCA9PbhQrUOHzyd5b9OmDfPnz2f48OEATJs2jZdffplPPvmE+fPn8/jjj5OUFOTj3r1707t372r1P/7449x4442Vyzt37mTOnDnMnDmTMWPGcOedd1Z7TUpKCqeeeip5edUnK6mPZ599trL+yy+/nAkTJuDu1frxzzzzzBqPFkaOHFn5/OSTT+bJJ5+sXL744ouZOnUq//3f/31IMYrIodERwWEwcuRI1q1bR//+/bnlllt44403KreNGzeucq993rx5tG/fnn79+rF06VKGDRtGcnLdZwHMmTOHE088sXL5mWeeYdSoUfTv35927drx7rvvVnvN7t27ee211xgypPoVjWeccQbDhg2r9nj11VerlV2/fj3duwcDWCkpKbRu3ZrCwsJq5eLxyCOPMHr06Mrl3NxcZs2adVB1icjh0+yOCGrbc0/kdQRZWVm88847zJo1i5kzZ3LllVdy3333MX78eMaOHcupp57Kb37zG6ZOncq4cePqXf+WLVvIzv78dtlTpkzhu9/9LgBjx45lypQpnHDCCQB88sknDBs2DDPjkksu2e+Lt0J9vnxrus7kYM7quffee0lJSeGqq66qXNepUyc2bNhQ77pE5PBKaCIws1HA74Bk4GF3v6/K9nTgb8CJQCFwpbuvTmRMiZKcnMzZZ5/N2WefzZAhQ5g8eTLjx4+ne/fu9OzZkzfeeIN//etfzJ07F4BBgwaxePFiysvLK7uGapOSklJZrrCwkBkzZvDBBx9gZpSVlWFm3H///cDnYwQHcsYZZ1BUVP2GWb/+9a8577zz9luXk5PDunXryMnJobS0lO3bt9OuXbv6fDRMnjyZ559/ntdee22/JLJ3714yM5vX7XxFmqKEdQ2ZWTLwIDAaGAiMM7OBVYpdD2x1977Ab4FfJSqeRFq+fDkrVqyoXF60aBE9evSoXB43bhy33norffr0IScnBwi+sHNzc7njjjsq97pXrFjBs88+W63+Y445hpUrgzlnn3zySa655hrWrFnD6tWrWbduHb169WL27PjnX541axaLFi2q9qiaBADGjBnD5MmTK997xIgR9ToieOmll/jVr37F9OnTadGixX7bPv7442pnGYlIw0vkGMFJQJ67r3T3fcBU4JIqZS4BJofPnwTOtQRfTZSIynfu3Mm1117LwIEDGTp0KMuWLdtvAPcrX/kKS5cuZezYsfu97uGHH2bTpk307duXIUOGcMMNN3DUUUdVq//CCy/k9ddfB4JuoUsvvXS/7ZdddhlPPPHEYW8XwPXXX09hYSF9+/blgQce4L77goO6DRs2cMEFF1SWGzduHKeccgrLly8nJyeHSZMmATBhwgSKioo4//zzGTZsGDfffHPla2bOnMmFF2r+aJHGlrB7DZnZ5cAod/9GuPw1YLi7T4gp80FYJj9c/iQss7lKXTcCNwIcffTRJ65Zs2a/9/rwww8rz82vTdHeEtYW7mZQt9aH3LaGtnHjRq655hpeeeWVxg7lsCkuLuass85i9uzZpKRU76GM53cqIvE70L2GEnlEUNPOd9WsE08Z3H2iu+e6e27Hjh0PKpjsjNQmmQQAunbtyg033LDfBWVN3dq1a7nvvvtqTAIi0rAS+V+YD8TeOCMHqHqKSEWZfDNLAVoDWxIYU5N1xRVXNHYIh1W/fv3o169fY4chIiT2iGAB0M/MeplZGjAWmF6lzHTg2vD55cAMP8i+qqZ2O22pnX6XIg0rYYnA3UuBCcDLwIfANHdfamZ3m9mYsNgkoL2Z5QHfA24/mPfKyMigsLBQXyDNQMV8BBkZR85cryLNXbOYmCaeGcqk6dAMZSKHX7OfmCY1NVWzWYmIHCTda0hEJOKUCEREIk6JQEQk4prcYLGZFQBr6ixYsw5A9Zlfmje1ORrU5mg4lDb3cPcar8htcongUJjZwtpGzZsrtTka1OZoSFSb1TUkIhJxSgQiIhEXtUQwsbEDaARqczSozdGQkDZHaoxARESqi9oRgYiIVKFEICIScc0yEZjZKDNbbmZ5ZlbtjqZmlm5m/wi3zzezng0f5eEVR5u/Z2bLzGyJmb1mZj1qqqcpqavNMeUuNzM3syZ/qmE8bTazK8Lf9VIzS8wcpg0ojr/to81sppm9F/59X1BTPU2FmT1iZp+FMzjWtN3M7Pfh57HEzE445Dd192b1AJKBT4DeQBqwGBhYpcwtwJ/D52OBfzR23A3Q5nOAFuHzb0ahzWG5bOBNYB6Q29hxN8DvuR/wHtA2XO7U2HE3QJsnAt8Mnw8EVjd23IfY5jOBE4APatl+AfAiwQyPJwPzD/U9m+MRwUlAnruvdPd9wFTgkiplLgEmh8+fBM41s0TMa99Q6myzu890993h4jyCGeOasnh+zwD3APcDzeEe5fG0+QbgQXffCuDunzVwjIdbPG12oFX4vDXVZ0JsUtz9TQ48U+MlwN88MA9oY2ZdD+U9m2Mi6Aasi1nOD9fVWMaDCXS2A+0bJLrEiKfNsa4n2KNoyupss5kdD3R39+cbMrAEiuf33B/ob2ZzzGyemY1qsOgSI5423wlcbWb5wAvAtxsmtEZT3//3OjWL+QiqqGnPvuo5svGUaUribo+ZXQ3kAmclNKLEO2CbzSwJ+C0wvqECagDx/J5TCLqHziY46ptlZoPdfVuCY0uUeNo8DnjU3X9jZqcAfw/bXJ748BrFYf/+ao5HBPlA95jlHKofKlaWMbMUgsPJAx2KHeniaTNmdh7wE2CMuxc3UGyJUlebs4HBwOtmtpqgL3V6Ex8wjvdv+1l3L3H3VcBygsTQVMXT5uuBaQDuPhfIILg5W3MV1/97fTTHRLAA6GdmvcwsjWAweHqVMtOBa8PnlwMzPByFaaLqbHPYTfIQQRJo6v3GUEeb3X27u3dw957u3pNgXGSMuy+subomIZ6/7WcITgzAzDoQdBWtbNAoD6942rwWOBfAzI4lSAQFDRplw5oOXBOePXQysN3dNx5Khc2ua8jdS81sAvAywRkHj7j7UjO7G1jo7tOBSQSHj3kERwJjGy/iQxdnm/8HyAL+GY6Lr3X3MY0W9CGKs83NSpxtfhkYaWbLgDLgNncvbLyoD02cbf4+8Bczu5Wgi2R8U96xM7MpBF17HcJxjzuAVAB3/zPBOMgFQB6wG/j6Ib9nE/68RETkMGiOXUMiIlIPSgQiIhGnRCAiEnFKBCIiEadEICIScUoEIjUwszIzW2RmH5jZc2bW5jDXvzo8zx8z23k46xapLyUCkZrtcfdh7j6Y4FqTbzV2QCKJokQgUre5hDf1MrM+ZvaSmb1jZrPMbEC4vrOZPW1mi8PHqeH6Z8KyS83sxkZsg0itmt2VxSKHk5klE9y+YFK4aiJws7uvMLPhwB+BEcDvgTfc/dLwNVlh+evcfYuZZQILzOxfTflKX2melAhEapZpZouAnsA7wCtmlgWcyue36QBID3+OAK4BcPcyglubA3zHzC4Nn3cnuAGcEoEcUZQIRGq2x92HmVlr4HmCMYJHgW3uPiyeCszsbOA84BR3321mrxPcEE3kiKIxApEDcPftwHeAHwB7gFVm9hWonDv2uLDoawRTgGJmyWbWiuD25lvDJDCA4FbYIkccJQKROrj7ewRz5Y4FrgKuN7PFwFI+nzbxv4BzzOx9gq6kQcBLQIqZLSGYMnNeQ8cuEg/dfVREJOJ0RCAiEnFKBCIiEadEICIScUoEIiIRp0QgIhJxSgQiIhGnRCAiEnH/H9KW4F4inRhCAAAAAElFTkSuQmCC\n",
+      "image/png": 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       "text/plain": [
-       "
"
+       "
"
       ]
      },
      "metadata": {
@@ -1189,8 +1382,10 @@
     }
    ],
    "source": [
-    "disp = metrics.plot_precision_recall_curve(clf, X_test, y_test)\n",
-    "disp.ax_.set_title('2-class Precision-Recall curve: AP={0:0.2f}'.format(average_precision));"
+    "fig, ax = plt.subplots(1, 1, figsize=(20,10))\n",
+    "disp = metrics.plot_precision_recall_curve(clf, X_test, y_test, ax=ax)\n",
+    "disp.ax_.set_title('2-class Precision-Recall curve: AP={} with {}'.format(round(average_precision,2), str(scaler).rsplit('(')[0]))\n",
+    "plt.savefig(os.path.join(out_dir, 'precision_recall_curve_{}.png'.format(str(scaler).rsplit('(')[0])), dpi=300)"
    ]
   },
   {
@@ -1200,12 +1395,80 @@
     "Results are pretty crappy, but that is okay give we use not very sensible input data at the moment..."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Documentation\n",
+    "\n",
+    "Let's safe some settings used in this run to csv-files so results can be assessed in light of these settings."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, the parameters of our SVM model."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "SVC_params = clf.get_params()\n",
+    "\n",
+    "out_fo = os.path.join(out_dir, 'SVC_params.csv')\n",
+    "w = csv.writer(open(out_fo, \"w\"))\n",
+    "for key, val in SVC_params.items():\n",
+    "    w.writerow([key, val])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Second, the parameters of the scaling method we used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler_params = scaler.get_params()\n",
+    "\n",
+    "out_fo = os.path.join(out_dir, '{}_params.csv'.format(str(scaler).rsplit('(')[0]))\n",
+    "w = csv.writer(open(out_fo, \"w\"))\n",
+    "for key, val in scaler_params.items():\n",
+    "    w.writerow([key, val])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Third, the resulting values of our objective functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluation = {'Accuracy': round(metrics.accuracy_score(y_test, y_pred), 2),\n",
+    "              'Precision': round(metrics.precision_score(y_test, y_pred), 2),\n",
+    "              'Recall': round(metrics.recall_score(y_test, y_pred), 2),\n",
+    "              'Average precision-recall score': round(average_precision, 2)}\n",
+    "\n",
+    "out_fo = os.path.join(out_dir, 'evaluation.csv')\n",
+    "w = csv.writer(open(out_fo, \"w\"))\n",
+    "for key, val in evaluation.items():\n",
+    "    w.writerow([key, val])"
+   ]
   }
  ],
  "metadata": {
diff --git a/scripts/runner.py b/scripts/runner.py
index 1e63f5d..1e1c7b1 100644
--- a/scripts/runner.py
+++ b/scripts/runner.py
@@ -11,6 +11,8 @@
 import seaborn as sbs
 from sklearn import svm, preprocessing, model_selection, metrics
 import matplotlib.pyplot as plt
+from shutil import copyfile
+import csv
 import os, sys
 
 
@@ -20,9 +22,10 @@ def cli():
 
 @click.command()
 @click.argument('cfg',)
-@click.option('-so', '--safe-output', default=False, help='whether or not to save output', type=click.BOOL)
+@click.option('-so', '--safe-output', default=True, help='save output yes/no', type=click.BOOL)
+@click.option('-v', '--verbose', default=False, help='verbose model yes/no', is_flag=True, type=click.BOOL)
 
-def main(cfg, safe_output=False):
+def main(cfg, safe_output=True, verbose=False):
     """
     Runs the conflict_model from command line with several options and the settings cfg-file as argument.
 
@@ -31,6 +34,9 @@ def main(cfg, safe_output=False):
     print('')
     print('#### LETS GET STARTED PEOPLZ! ####' + os.linesep)
 
+    print('safe output: {}'.format(safe_output))
+    print('verbose mode on: {}'.format(verbose) + os.linesep)
+
     if gpd.__version__ < '0.7.0':
         sys.exit('please upgrade geopandas to version 0.7.0, your current version is {}'.format(gpd.__version__))
 
@@ -45,6 +51,8 @@ def main(cfg, safe_output=False):
     else:
         print('not saving output' + os.linesep)
 
+    if verbose: copyfile(cfg, os.path.join(out_dir, 'copy_of_run_setting.cfg'))
+
     gdf = conflict_model.utils.get_geodataframe(config)
 
     conflict_gdf, extent_gdf = conflict_model.selection.select(gdf, config)
@@ -84,23 +92,43 @@ def main(cfg, safe_output=False):
     X = XY_data[['GDP_PPP', 'ET']].to_numpy()
     Y = XY_data.conflict.astype(int).to_numpy()
 
-    print('scaling data and splitting into trainings and test samples' + os.linesep)
-    X_train, X_test, y_train, y_test = model_selection.train_test_split(preprocessing.scale(X),
+    scaler = preprocessing.MinMaxScaler()
+
+    if verbose:
+        scaler_params = scaler.get_params()
+        out_fo = os.path.join(out_dir, '{}_params.csv'.format(str(scaler).rsplit('(')[0]))
+        w = csv.writer(open(out_fo, "w"))
+        for key, val in scaler_params.items():
+            w.writerow([key, val])
+
+    print('scaling data with {}'.format(str(scaler).rsplit('(')[0]))
+    X_scaled = scaler.fit_transform(X)
+
+    print('splitting into trainings and test samples' + os.linesep)
+    X_train, X_test, y_train, y_test = model_selection.train_test_split(X_scaled,
                                                                         Y,
-                                                                        test_size=0.7)
+                                                                        test_size=1-config.getfloat('machine_learning', 'train_fraction'))
 
     plt.figure(figsize=(10,10))
     sbs.scatterplot(x=X_train[:,0],
                     y=X_train[:,1],  
                     hue=y_train)
 
-    plt.title('n=' + str(len(X_train)))
-    if safe_output:
-        plt.savefig(os.path.join(out_dir, 'scatter_plot.png'), dpi=300)
+    plt.title('training-data scaled with {0}; n_train={1}; n_tot={2}'.format(str(scaler).rsplit('(')[0], len(X_train), len(X_scaled)))
+    plt.xlabel('Variable 1')
+    plt.ylabel('Variable 2')
+    if safe_output: plt.savefig(os.path.join(out_dir, 'scatter_plot_scaled_traindata_{}.png'.format(str(scaler).rsplit('(')[0])), dpi=300)
 
     print('initializing Support Vector Classification model' + os.linesep)
     clf = svm.SVC(class_weight='balanced')
 
+    if verbose:
+        SVC_params = clf.get_params()
+        out_fo = os.path.join(out_dir, 'SVC_params.csv')
+        w = csv.writer(open(out_fo, "w"))
+        for key, val in SVC_params.items():
+            w.writerow([key, val])
+
     print('fitting model with trainings data' + os.linesep)
     clf.fit(X_train, y_train)
 
@@ -115,8 +143,21 @@ def main(cfg, safe_output=False):
     print("...Recall:", metrics.recall_score(y_test, y_pred))
     print('...Average precision-recall score: {0:0.2f}'.format(metrics.average_precision_score(y_test, y_score)))
 
-    disp = metrics.plot_precision_recall_curve(clf, X_test, y_test)
-    disp.ax_.set_title('2-class Precision-Recall curve: AP={0:0.2f}'.format(metrics.average_precision_score(y_test, y_score)))
+    fig, ax = plt.subplots(1, 1, figsize=(20,10))
+    disp = metrics.plot_precision_recall_curve(clf, X_test, y_test, ax=ax)
+    disp.ax_.set_title('2-class Precision-Recall curve: AP={} with {}'.format(round(metrics.average_precision_score(y_test, y_score),2), str(scaler).rsplit('(')[0]))
+    if safe_output: plt.savefig(os.path.join(out_dir, 'precision_recall_curve_{}.png'.format(str(scaler).rsplit('(')[0])), dpi=300)
+
+    if safe_output:
+        evaluation = {'Accuracy': round(metrics.accuracy_score(y_test, y_pred), 2),
+                    'Precision': round(metrics.precision_score(y_test, y_pred), 2),
+                    'Recall': round(metrics.recall_score(y_test, y_pred), 2),
+                    'Average precision-recall score': round(metrics.average_precision_score(y_test, y_score), 2)}
+
+        out_fo = os.path.join(out_dir, 'evaluation.csv')
+        w = csv.writer(open(out_fo, "w"))
+        for key, val in evaluation.items():
+            w.writerow([key, val])
 
 if __name__ == '__main__':
     main()
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
index 9d6f5cd..64b6a62 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.0.1
+current_version = 0.0.2b1
 commit = True
 tag = True
 
diff --git a/setup.py b/setup.py
index c1c3395..aa03563 100644
--- a/setup.py
+++ b/setup.py
@@ -45,6 +45,6 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/JannisHoch/conflict_model',
-    version='0.0.1',
+    version='0.0.2b1',
     zip_safe=False,
 )