Start of work on sql stuff

.idea/dataSources.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DataSourceManagerImpl" format="xml" multifile-model="true">
+    <data-source source="LOCAL" name="SlopeStability_Tests.sqlite" uuid="1bec2fa2-8163-4762-aacf-5861deb0ba9b">
+      <driver-ref>sqlite.xerial</driver-ref>
+      <synchronize>true</synchronize>
+      <remarks>DB containing information about all executed tests</remarks>
+      <jdbc-driver>org.sqlite.JDBC</jdbc-driver>
+      <jdbc-url>jdbc:sqlite:$PROJECT_DIR$/SlopeStability_Tests</jdbc-url>
+      <working-dir>$ProjectFileDir$</working-dir>
+    </data-source>
+  </component>
+</project>
\ No newline at end of file

.idea/sqldialects.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="SqlDialectMappings">
+    <file url="file://$PROJECT_DIR$/sql_scripts/create_table_executed_tests.sql" dialect="SQLite" />
+    <file url="PROJECT" dialect="SQLite" />
+  </component>
+</project>
\ No newline at end of file

notebooks_sketch/number_of_layers_estimator.ipynb

@@ -83,8 +83,8 @@
     "    y_train[idx] = test_hist[test_names_training[idx]]['n_class']\n",
     "clf.fit(x_train, y_train)\n",
     "y_pred = clf.predict(x_train)\n",
-    "training_accuray = len(np.where(y_train == y_pred)[0])/len(y_train)*100\n",
-    "print('Trained with accuracy: {accuracy:.2f}%'.format(accuracy=training_accuray))"
+    "training_accuracy = len(np.where(y_train == y_pred)[0])/len(y_train)*100\n",
+    "print('Trained with accuracy: {accuracy:.2f}%'.format(accuracy=training_accuracy))"
    ],
    "metadata": {
     "collapsed": false,
@@ -112,8 +112,8 @@
     "    x_pred[idx,:] = test_hist[test_names_prediction[idx]]['hist']\n",
     "    y_corr[idx] = test_hist[test_names_prediction[idx]]['n_class']\n",
     "y_pred = clf.predict(x_pred)\n",
-    "prediction_accuray = len(np.where(y_corr == y_pred)[0])/len(y_pred)*100\n",
-    "print('Predicted with accuracy: {accuracy:.2f}%'.format(accuracy=prediction_accuray))"
+    "prediction_accuracy = len(np.where(y_corr == y_pred)[0])/len(y_pred)*100\n",
+    "print('Predicted with accuracy: {accuracy:.2f}%'.format(accuracy=prediction_accuracy))"
    ],
    "metadata": {
     "collapsed": false,
 %% Cell type:code id: tags:
 
 ``` python
 import slopestabilitytools
 import slopestabilityML
 import settings
 import os
 import numpy as np
 import pandas as pd
 import test_definitions
 
 from sklearn import svm
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.compose import ColumnTransformer
 from sklearn.pipeline import make_pipeline
 from sklearn.metrics import accuracy_score
 
 settings.init()
 test_definitions.init()
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 print(os.path.abspath(os.path.join(os.getcwd(), os.pardir)) + '/')
 test_results = slopestabilitytools.datamanagement.import_tests(abs_path=os.path.abspath(os.path.join(os.getcwd(), os.pardir))+'/')
 test_hist = {}
 for test_name in test_results.keys():
     hist, bins = np.histogram(test_results[test_name]['RESN'])
     #hist = hist/np.sum(hist)
     test_hist[test_name] = {'hist': hist,
                             'bins': bins,
                             'n_class': test_definitions.test_definitions[test_name]['layer_n']}
 test_names_training, test_names_prediction = slopestabilityML.split_dataset(test_hist.keys(), 999)
 ```
 
 %%%% Output: stream
 
     /home/felikskrno/Documents/Studies/MasterThesis/PythonProjects/SlopeStability/
 
 %% Cell type:code id: tags:
 
 ``` python
 clf = svm.SVC(gamma=0.001, C=100, kernel='linear')
 #num_feat = ['hist']
 #num_trans = StandardScaler()
 #preprocessor = ColumnTransformer(transformers=[('num', num_trans, num_feat)])#,
 #                                                   #('cat', cat_trans, cat_feat)])
 #clf_pipeline = make_pipeline(preprocessor, clf)
 x_train = np.zeros((len(test_names_training), len(test_hist['hor_01']['hist'])))
 y_train = np.zeros((len(test_names_training)))
 for idx in range(len(test_names_training)):
     x_train[idx,:] = test_hist[test_names_training[idx]]['hist']
     y_train[idx] = test_hist[test_names_training[idx]]['n_class']
 clf.fit(x_train, y_train)
 y_pred = clf.predict(x_train)
-training_accuray = len(np.where(y_train == y_pred)[0])/len(y_train)*100
-print('Trained with accuracy: {accuracy:.2f}%'.format(accuracy=training_accuray))
+training_accuracy = len(np.where(y_train == y_pred)[0])/len(y_train)*100
+print('Trained with accuracy: {accuracy:.2f}%'.format(accuracy=training_accuracy))
 ```
 
 %%%% Output: stream
 
     Trained with accuracy: 100.00%
 
 %% Cell type:code id: tags:
 
 ``` python
 x_pred = np.zeros((len(test_names_prediction), len(test_hist['hor_01']['hist'])))
 y_corr = np.zeros((len(test_names_prediction)))
 for idx in range(len(test_names_prediction)):
     x_pred[idx,:] = test_hist[test_names_prediction[idx]]['hist']
     y_corr[idx] = test_hist[test_names_prediction[idx]]['n_class']
 y_pred = clf.predict(x_pred)
-prediction_accuray = len(np.where(y_corr == y_pred)[0])/len(y_pred)*100
-print('Predicted with accuracy: {accuracy:.2f}%'.format(accuracy=prediction_accuray))
+prediction_accuracy = len(np.where(y_corr == y_pred)[0])/len(y_pred)*100
+print('Predicted with accuracy: {accuracy:.2f}%'.format(accuracy=prediction_accuracy))
 ```
 
 %%%% Output: stream
 
     Predicted with accuracy: 50.00%

sql_scripts/create_table_executed_tests.sql

+CREATE TABLE ExecutedTests (
+    TestID INTEGER PRIMARY KEY AUTOINCREMENT ,
+    DatasetID INTEGER NOT NULL,
+    NormData   INTEGER,
+    NormDataMethod TEXT,
+    NormClass INTEGER,
+    NClass INTEGER,
+    Sensitivity INTEGER,
+    Depth INTEGER,
+    InversionID INTEGER,
+    FOREIGN KEY (InversionID)
+                           REFERENCES InversionParameters (InversionID),
+    ResultsID INTEGER,
+    'FOREIGN KEY (ResultsID)
+                           REFERENCES ClassificationResults (ClassificationID)'
+);
\ No newline at end of file

sql_scripts/create_table_inversion_parameters.sql

+CREATE TABLE InversionParameters (
+    InversionID INTEGER PRIMARY KEY AUTOINCREMENT,
+    Lambda NUMERIC,
+    ParaDX NUMERIC,
+    ParaDepthCoeff NUMERIC,
+    Quality NUMERIC,
+    Zpower NUMERIC,
+    MaxDepthCoeff NUMERIC,
+    WidthCoeff NUMERIC,
+    MaxDepth NUMERIC
+);
\ No newline at end of file

sql_scripts/create_table_results.sql

+CREATE TABLE ClassificationResults (
+
+    ClassificationID INTEGER PRIMARY KEY AUTOINCREMENT,
+
+    TrainingADA NUMERIC,
+    TrainingGBC NUMERIC,
+    TrainingKNN NUMERIC,
+    TrainingSGD NUMERIC,
+    TrainingSVM NUMERIC,
+
+    PredictionADA NUMERIC,
+    PredictionGBC NUMERIC,
+    PredictionKNN NUMERIC,
+    PredictionSGD NUMERIC,
+    PredictionSVM NUMERIC
+
+);
\ No newline at end of file