diff --git a/.gitignore b/.gitignore
index 1e2b64c..329050c 100755
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,6 @@
 .idea/
 venv/
 data/
-__pycache__/
\ No newline at end of file
+__pycache__/
+*.csv
+TESTS
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 5d141a2..aa5ef62 100755
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -2,20 +2,11 @@
 <project version="4">
   <component name="ChangeListManager">
     <list default="true" id="66852614-159d-4e5f-935f-73f2ec68125e" name="Default Changelist" comment="">
-      <change afterPath="$PROJECT_DIR$/count_dataset/__init__.py" afterDir="false" />
-      <change afterPath="$PROJECT_DIR$/count_dataset/count_dataset.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/api/routes/dataset_key_route.py" beforeDir="false" afterPath="$PROJECT_DIR$/api/routes/dataset_key_route.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/api/routes/dataset_route.py" beforeDir="false" afterPath="$PROJECT_DIR$/api/routes/dataset_route.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/api/routes/outlier_route.py" beforeDir="false" afterPath="$PROJECT_DIR$/api/routes/outlier_route.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/cli/cli.py" beforeDir="false" afterPath="$PROJECT_DIR$/cli/cli.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/db/models/DataSetMap.py" beforeDir="false" afterPath="$PROJECT_DIR$/db/models/DataSetMap.py" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/outlier_detector/OutlierDetector.py" beforeDir="false" afterPath="$PROJECT_DIR$/outlier_detector/OutlierDetector.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/outlier_detector/learn_utils.py" beforeDir="false" afterPath="$PROJECT_DIR$/outlier_detector/learn_utils.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/outlier_detector/linear_regression.py" beforeDir="false" afterPath="$PROJECT_DIR$/outlier_detector/linear_regression.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/outlier_detector/sliding_window_kmean.py" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/ui/src/components/Chart/Chart.tsx" beforeDir="false" afterPath="$PROJECT_DIR$/ui/src/components/Chart/Chart.tsx" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/ui/src/typings/react-vis.d.ts" beforeDir="false" afterPath="$PROJECT_DIR$/ui/src/typings/react-vis.d.ts" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/run_all/PlotDrawer.py" beforeDir="false" afterPath="$PROJECT_DIR$/run_all/PlotDrawer.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/run_all/TestCase.py" beforeDir="false" afterPath="$PROJECT_DIR$/run_all/TestCase.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/run_all/run_all.py" beforeDir="false" afterPath="$PROJECT_DIR$/run_all/run_all.py" afterDir="false" />
     </list>
     <ignored path="$PROJECT_DIR$/out/" />
     <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
@@ -28,6 +19,8 @@
     <SUITE FILE_PATH="coverage/data_defect_detection$server.coverage" NAME="server Coverage Results" MODIFIED="1551543773003" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
     <SUITE FILE_PATH="coverage/data_defect_detection$create_data_set_map.coverage" NAME="create_data_set_map Coverage Results" MODIFIED="1551879023406" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
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+    <SUITE FILE_PATH="coverage/data_defect_detection$draw_plot.coverage" NAME="draw_plot Coverage Results" MODIFIED="1558534378824" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
+    <SUITE FILE_PATH="coverage/data_defect_detection$run_all.coverage" NAME="run_all Coverage Results" MODIFIED="1559497978472" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
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@@ -56,20 +49,77 @@
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   <component name="FileEditorManager">
     <leaf SIDE_TABS_SIZE_LIMIT_KEY="300">
-      <file pinned="false" current-in-tab="true">
-        <entry file="file://$PROJECT_DIR$/ui/src/components/App/App.tsx">
+      <file pinned="false" current-in-tab="false">
+        <entry file="file://$PROJECT_DIR$/run_all/PlotDrawer.py">
+          <provider selected="true" editor-type-id="text-editor">
+            <state relative-caret-position="289">
+              <caret line="23" column="17" selection-start-line="23" selection-start-column="17" selection-end-line="23" selection-end-column="17" />
+              <folding>
+                <element signature="e#0#11#0" expanded="true" />
+              </folding>
+            </state>
+          </provider>
+        </entry>
+      </file>
+      <file pinned="false" current-in-tab="false">
+        <entry file="file://$PROJECT_DIR$/run_all/run_all.py">
+          <provider selected="true" editor-type-id="text-editor">
+            <state relative-caret-position="2262">
+              <caret line="107" column="43" selection-start-line="107" selection-start-column="43" selection-end-line="107" selection-end-column="43" />
+              <caret line="123" column="35" selection-start-line="123" selection-start-column="35" selection-end-line="123" selection-end-column="35" />
+              <folding>
+                <element signature="e#0#16#0" expanded="true" />
+              </folding>
+            </state>
+          </provider>
+        </entry>
+      </file>
+      <file pinned="false" current-in-tab="false">
+        <entry file="file://$PROJECT_DIR$/run_all/TestCase.py">
           <provider selected="true" editor-type-id="text-editor">
             <state relative-caret-position="260">
-              <caret line="89" column="24" lean-forward="true" selection-start-line="89" selection-start-column="24" selection-end-line="89" selection-end-column="24" />
+              <caret line="67" column="61" selection-start-line="67" selection-start-column="61" selection-end-line="67" selection-end-column="61" />
             </state>
           </provider>
         </entry>
       </file>
+      <file pinned="false" current-in-tab="true">
+        <entry file="file://$PROJECT_DIR$/TESTS/53_MOGILA_LTE_1079_feature_if.csv">
+          <provider editor-type-id="csv-table-editor">
+            <state showInfoPanel="true" fixedHeaders="false" rowLines="3" />
+          </provider>
+          <provider selected="true" editor-type-id="csv-text-editor" />
+        </entry>
+      </file>
       <file pinned="false" current-in-tab="false">
-        <entry file="file://$PROJECT_DIR$/ui/src/types/DatasetMapRow.ts">
+        <entry file="file://$PROJECT_DIR$/run_all/TestComparator.py">
           <provider selected="true" editor-type-id="text-editor">
-            <state relative-caret-position="104">
-              <caret line="4" column="21" selection-start-line="4" selection-start-column="21" selection-end-line="4" selection-end-column="21" />
+            <state relative-caret-position="81">
+              <caret line="25" column="50" selection-start-line="25" selection-start-column="50" selection-end-line="25" selection-end-column="50" />
+            </state>
+          </provider>
+        </entry>
+      </file>
+      <file pinned="false" current-in-tab="false">
+        <entry file="file://$PROJECT_DIR$/outlier_detector/OutlierDetector.py">
+          <provider selected="true" editor-type-id="text-editor">
+            <state relative-caret-position="52">
+              <caret line="110" column="38" selection-start-line="110" selection-start-column="38" selection-end-line="110" selection-end-column="38" />
+              <folding>
+                <element signature="e#0#29#0" expanded="true" />
+              </folding>
+            </state>
+          </provider>
+        </entry>
+      </file>
+      <file pinned="false" current-in-tab="false">
+        <entry file="file://$PROJECT_DIR$/api/routes/outlier_route.py">
+          <provider selected="true" editor-type-id="text-editor">
+            <state relative-caret-position="470">
+              <caret line="56" column="36" selection-start-line="56" selection-start-column="36" selection-end-line="56" selection-end-column="36" />
+              <folding>
+                <element signature="e#0#11#0" expanded="true" />
+              </folding>
             </state>
           </provider>
         </entry>
@@ -103,78 +153,92 @@
       <find>K&gt;orekta</find>
       <find>n_samp</find>
       <find>1545</find>
-      <find>print</find>
       <find>XYP</find>
+      <find>allow</find>
+      <find>allow_filtering</find>
+      <find>print</find>
+      <find>sca</find>
+      <find>feature</find>
     </findStrings>
+    <replaceStrings>
+      <replace />
+    </replaceStrings>
     <dirStrings>
       <dir>$PROJECT_DIR$/ui</dir>
+      <dir>$PROJECT_DIR$/api</dir>
+      <dir>$PROJECT_DIR$</dir>
+      <dir>$PROJECT_DIR$/TESTS</dir>
     </dirStrings>
   </component>
   <component name="Git.Settings">
     <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
+    <option name="RECENT_BRANCH_BY_REPOSITORY">
+      <map>
+        <entry key="$PROJECT_DIR$" value="code_refactor" />
+      </map>
+    </option>
   </component>
   <component name="IdeDocumentHistory">
     <option name="CHANGED_PATHS">
       <list>
-        <option value="$PROJECT_DIR$/api/routes/dataset_key.py" />
-        <option value="$PROJECT_DIR$/db/models/Operator.py" />
-        <option value="$PROJECT_DIR$/db/Database.py" />
-        <option value="$PROJECT_DIR$/api/routes/operator.py" />
-        <option value="$PROJECT_DIR$/ui/index.tsx" />
-        <option value="$PROJECT_DIR$/ui/App/App.tsx" />
-        <option value="$PROJECT_DIR$/ui/.babelrc" />
-        <option value="$PROJECT_DIR$/ui/src/index.ejs" />
-        <option value="$PROJECT_DIR$/ui/src/types/DataSet.ts" />
-        <option value="$PROJECT_DIR$/ui/dist/index.html" />
-        <option value="$PROJECT_DIR$/ui/src/types/Operator.ts" />
-        <option value="$PROJECT_DIR$/ui/src/components/Select/style.ts" />
-        <option value="$PROJECT_DIR$/ui/package.json" />
-        <option value="$PROJECT_DIR$/api/routes/kpi_definitions_route.py" />
-        <option value="$PROJECT_DIR$/ui/src/types/KpiDefinition.ts" />
-        <option value="$PROJECT_DIR$/ui/src/components/Select/Select.tsx" />
-        <option value="$PROJECT_DIR$/ui/src/index.tsx" />
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         <option value="$PROJECT_DIR$/ui/src/components/Chart/dads.html" />
         <option value="$PROJECT_DIR$/api/routes/kpi_definition_route.py" />
         <option value="$PROJECT_DIR$/api/api.py" />
         <option value="$PROJECT_DIR$/ui/src/types/DatasetRow.ts" />
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         <option value="$PROJECT_DIR$/outlier_detector/sliding_window_kmean.py" />
-        <option value="$PROJECT_DIR$/load_data/load_operators.py" />
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         <option value="$PROJECT_DIR$/outlier_detector/utils.py" />
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-        <option value="$PROJECT_DIR$/outlier_detector/learn_utils.py" />
-        <option value="$PROJECT_DIR$/api/routes/dataset_route.py" />
-        <option value="$PROJECT_DIR$/outlier_detector/linear_regression.py" />
-        <option value="$PROJECT_DIR$/outlier_detector/OutlierDetector.py" />
         <option value="$PROJECT_DIR$/ui/src/typings/react-vis.d.ts" />
-        <option value="$PROJECT_DIR$/ui/src/components/Chart/Chart.tsx" />
-        <option value="$PROJECT_DIR$/api/routes/outlier_route.py" />
-        <option value="$PROJECT_DIR$/outlier_detector/dist_kmean.py" />
         <option value="$PROJECT_DIR$/db/models/DataSetMap.py" />
-        <option value="$PROJECT_DIR$/cli/cli.py" />
+        <option value="$PROJECT_DIR$/ui/src/types/DatasetMapRow.ts" />
+        <option value="$PROJECT_DIR$/db/models/Operator.py" />
+        <option value="$PROJECT_DIR$/load_data/load_operators.py" />
+        <option value="$PROJECT_DIR$/load_data/load_data.py" />
+        <option value="$PROJECT_DIR$/ui/src/types/Operator.ts" />
         <option value="$PROJECT_DIR$/count_dataset/count_dataset.py" />
         <option value="$PROJECT_DIR$/api/routes/dataset_key_route.py" />
-        <option value="$PROJECT_DIR$/ui/src/types/DatasetMapRow.ts" />
+        <option value="$PROJECT_DIR$/api/routes/operator_route.py" />
+        <option value="$PROJECT_DIR$/api/json.py" />
+        <option value="$PROJECT_DIR$/api/routes/dataset_route.py" />
+        <option value="$PROJECT_DIR$/db/models/RawKpiDefinition.py" />
+        <option value="$PROJECT_DIR$/db/Database.py" />
+        <option value="$PROJECT_DIR$/db/models/Report.py" />
+        <option value="$PROJECT_DIR$/ui/src/components/Chart/style.ts" />
+        <option value="$PROJECT_DIR$/ui/src/components/Chart/Chart.tsx" />
+        <option value="$PROJECT_DIR$/outlier_detector/learn_utils.py" />
+        <option value="$PROJECT_DIR$/outlier_detector/sim_kmean.py" />
+        <option value="$PROJECT_DIR$/outlier_detector/isolation_forest.py" />
         <option value="$PROJECT_DIR$/ui/src/components/App/App.tsx" />
+        <option value="$PROJECT_DIR$/outlier_detector/random_split.py" />
+        <option value="$PROJECT_DIR$/ui/.gitignore" />
+        <option value="$PROJECT_DIR$/outlier_detector/dist_kmean.py" />
+        <option value="$PROJECT_DIR$/run_all/TEST_CASES.py" />
+        <option value="$PROJECT_DIR$/Pipfile" />
+        <option value="$PROJECT_DIR$/outlier_detector/linear_regression.py" />
+        <option value="$PROJECT_DIR$/.gitignore" />
+        <option value="$PROJECT_DIR$/api/routes/outlier_route.py" />
+        <option value="$PROJECT_DIR$/run_all/draw_plot.py" />
+        <option value="$PROJECT_DIR$/config/APPConfig.py" />
+        <option value="$PROJECT_DIR$/run_all/TestComparator.py" />
+        <option value="$PROJECT_DIR$/cli/cli.py" />
+        <option value="$PROJECT_DIR$/run_all/PlotDrawer.py" />
+        <option value="$PROJECT_DIR$/run_all/run_all.py" />
+        <option value="$PROJECT_DIR$/outlier_detector/OutlierDetector.py" />
+        <option value="$PROJECT_DIR$/run_all/TestCase.py" />
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     </option>
   </component>
   <component name="ProjectFrameBounds" extendedState="6">
-    <option name="x" value="1977" />
     <option name="y" value="27" />
-    <option name="width" value="1910" />
-    <option name="height" value="762" />
+    <option name="width" value="1920" />
+    <option name="height" value="1016" />
   </component>
   <component name="ProjectLevelVcsManager" settingsEditedManually="true">
     <ConfirmationsSetting value="2" id="Add" />
@@ -185,7 +249,6 @@
       <foldersAlwaysOnTop value="true" />
     </navigator>
     <panes>
-      <pane id="PackagesPane" />
       <pane id="ProjectPane">
         <subPane>
           <expand>
@@ -212,45 +275,28 @@
             <path>
               <item name="data_defect_detection" type="b2602c69:ProjectViewProjectNode" />
               <item name="data_defect_detection" type="462c0819:PsiDirectoryNode" />
-              <item name="count_dataset" type="462c0819:PsiDirectoryNode" />
+              <item name="config" type="462c0819:PsiDirectoryNode" />
             </path>
             <path>
               <item name="data_defect_detection" type="b2602c69:ProjectViewProjectNode" />
               <item name="data_defect_detection" type="462c0819:PsiDirectoryNode" />
-              <item name="ui" type="462c0819:PsiDirectoryNode" />
+              <item name="outlier_detector" type="462c0819:PsiDirectoryNode" />
             </path>
             <path>
               <item name="data_defect_detection" type="b2602c69:ProjectViewProjectNode" />
               <item name="data_defect_detection" type="462c0819:PsiDirectoryNode" />
-              <item name="ui" type="462c0819:PsiDirectoryNode" />
-              <item name="src" type="462c0819:PsiDirectoryNode" />
+              <item name="run_all" type="462c0819:PsiDirectoryNode" />
             </path>
             <path>
               <item name="data_defect_detection" type="b2602c69:ProjectViewProjectNode" />
               <item name="data_defect_detection" type="462c0819:PsiDirectoryNode" />
-              <item name="ui" type="462c0819:PsiDirectoryNode" />
-              <item name="src" type="462c0819:PsiDirectoryNode" />
-              <item name="components" type="462c0819:PsiDirectoryNode" />
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-            <path>
-              <item name="data_defect_detection" type="b2602c69:ProjectViewProjectNode" />
-              <item name="data_defect_detection" type="462c0819:PsiDirectoryNode" />
-              <item name="ui" type="462c0819:PsiDirectoryNode" />
-              <item name="src" type="462c0819:PsiDirectoryNode" />
-              <item name="components" type="462c0819:PsiDirectoryNode" />
-              <item name="App" type="462c0819:PsiDirectoryNode" />
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-            <path>
-              <item name="data_defect_detection" type="b2602c69:ProjectViewProjectNode" />
-              <item name="data_defect_detection" type="462c0819:PsiDirectoryNode" />
-              <item name="ui" type="462c0819:PsiDirectoryNode" />
-              <item name="src" type="462c0819:PsiDirectoryNode" />
-              <item name="types" type="462c0819:PsiDirectoryNode" />
+              <item name="TESTS" type="462c0819:PsiDirectoryNode" />
             </path>
           </expand>
           <select />
         </subPane>
       </pane>
+      <pane id="PackagesPane" />
       <pane id="Scope" />
     </panes>
   </component>
@@ -258,14 +304,15 @@
     <property name="DefaultHtmlFileTemplate" value="HTML File" />
     <property name="WebServerToolWindowFactoryState" value="false" />
     <property name="aspect.path.notification.shown" value="true" />
-    <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1553015996197" />
-    <property name="last_opened_file_path" value="$USER_HOME$/Downloads/maile PIT APP_PODATNIK (1).html" />
+    <property name="com.android.tools.idea.instantapp.provision.ProvisionBeforeRunTaskProvider.myTimeStamp" value="1559497974719" />
+    <property name="last_opened_file_path" value="$PROJECT_DIR$/../midi-controller" />
     <property name="nodejs_interpreter_path.stuck_in_default_project" value="/usr/bin/node" />
     <property name="nodejs_npm_path_reset_for_default_project" value="true" />
     <property name="nodejs_package_manager_path" value="npm" />
     <property name="project.structure.last.edited" value="SDKs" />
     <property name="project.structure.proportion" value="0.15" />
     <property name="project.structure.side.proportion" value="0.2" />
+    <property name="run.code.analysis.last.selected.profile" value="pProject Default" />
     <property name="settings.editor.selected.configurable" value="reference.settingsdialog.IDE.editor.colors.Console Font" />
     <property name="ts.external.directory.path" value="$PROJECT_DIR$/ui/node_modules/typescript/lib" />
   </component>
@@ -296,7 +343,7 @@
       </list>
     </option>
   </component>
-  <component name="RunManager" selected="Python.count dataset">
+  <component name="RunManager" selected="Python.run_all">
     <configuration name="dads.html" type="JavascriptDebugType" temporary="true" nameIsGenerated="true" uri="http://localhost:63342/data_defect_detection/ui/src/components/Chart/dads.html" useBuiltInWebServerPort="true">
       <method v="2" />
     </configuration>
@@ -369,6 +416,29 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
+    <configuration name="draw_plot" type="PythonConfigurationType" factoryName="Python">
+      <module name="data_defect_detection" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+        <env name="CQLENG_ALLOW_SCHEMA_MANAGEMENT" value="true" />
+      </envs>
+      <option name="SDK_HOME" value="$PROJECT_DIR$/venv/bin/python" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/main.py" />
+      <option name="PARAMETERS" value="draw_plot" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
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diff --git a/Pipfile b/Pipfile
index 24eb482..d4a3ad5 100644
--- a/Pipfile
+++ b/Pipfile
@@ -15,7 +15,7 @@ falcon = "*"
 gunicorn = "*"
 scikit-learn = "*"
 pandas = "*"
-learn-utils = "*"
+matplotlib = "*"
 
 [requires]
 python_version = "3.6"
diff --git a/Pipfile.lock b/Pipfile.lock
index 474baca..ad85c2b 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "3e56b82b4f64e08aceb7592a83c0bdbaee0af072df8fad839a16d7b60340ad29"
+            "sha256": "2b4796b2e1120adf8e0c7aa1f0d300e2a236ecee29a285a857e4347219ced1da"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -18,24 +18,24 @@
     "default": {
         "boto3": {
             "hashes": [
-                "sha256:30badbe5dab638e764e23125479e848c446a9669ef0612b8d8d305738bc442d3",
-                "sha256:c2dd70cb1ee2c49aed8bd3782fa82a766ad68921e1230620d41f4efe3b16bb75"
+                "sha256:000e266d8dfe8950972c62a4a129944d45ea5719912b197e796360064f2115e5",
+                "sha256:df865952fd3e1b8a5e4c6f94cf03ef4238a16140a77811a187497c4c664b9753"
             ],
-            "version": "==1.9.106"
+            "version": "==1.9.153"
         },
         "botocore": {
             "hashes": [
-                "sha256:60f4f62075d7b274360f74f220e9e56fb404bebd592ccb221e3f5333ceb1febb",
-                "sha256:bff336cccf1ed2b7cab54bb802e135a063cb7614271c6dacd2f49ecbc26df7da"
+                "sha256:6c3273c9881b412d665f7cab0216a5f3284bedd72937ee4d5702b265ea6e702e",
+                "sha256:6d03ec48f297fc429262603345d1e264f50064719dca7aadcff998014d935f9a"
             ],
-            "version": "==1.12.106"
+            "version": "==1.12.153"
         },
         "cassandra-driver": {
             "hashes": [
-                "sha256:6d42e11fd5879f12b1600e4c0f604e4432f472d030e80176b833c99c9cf989fc"
+                "sha256:71d79568ecbcabbd7fdbe4f488c6439e55cc6b457178ba6cb831bdbf4fb5d7f8"
             ],
             "index": "pypi",
-            "version": "==3.17.0"
+            "version": "==3.17.1"
         },
         "click": {
             "hashes": [
@@ -45,6 +45,13 @@
             "index": "pypi",
             "version": "==7.0"
         },
+        "cycler": {
+            "hashes": [
+                "sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d",
+                "sha256:cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"
+            ],
+            "version": "==0.10.0"
+        },
         "docutils": {
             "hashes": [
                 "sha256:02aec4bd92ab067f6ff27a38a38a41173bf01bed8f89157768c1573f53e474a6",
@@ -55,11 +62,23 @@
         },
         "falcon": {
             "hashes": [
-                "sha256:0a66b33458fab9c1e400a9be1a68056abda178eb02a8cb4b8f795e9df20b053b",
-                "sha256:3981f609c0358a9fcdb25b0e7fab3d9e23019356fb429c635ce4133135ae1bc4"
+                "sha256:18157af2a4fc3feedf2b5dcc6196f448639acf01c68bc33d4d5a04c3ef87f494",
+                "sha256:24adcd2b29a8ffa9d552dc79638cd21736a3fb04eda7d102c6cebafdaadb88ad",
+                "sha256:54f2cb4b687035b2a03206dbfc538055cc48b59a953187b0458aa1b574d47b53",
+                "sha256:59d1e8c993b9a37ea06df9d72cf907a46cc8063b30717cdac2f34d1658b6f936",
+                "sha256:733033ec80c896e30a43ab3e776856096836787197a44eb21022320a61311983",
+                "sha256:74cf1d18207381c665b9e6292d65100ce146d958707793174b03869dc6e614f4",
+                "sha256:95bf6ce986c1119aef12c9b348f4dee9c6dcc58391bdd0bc2b0bf353c2b15986",
+                "sha256:9712975adcf8c6e12876239085ad757b8fdeba223d46d23daef82b47658f83a9",
+                "sha256:a5ebb22a04c9cc65081938ee7651b4e3b4d2a28522ea8ec04c7bdd2b3e9e8cd8",
+                "sha256:aa184895d1ad4573fbfaaf803563d02f019ebdf4790e41cc568a330607eae439",
+                "sha256:e3782b7b92fefd46a6ad1fd8fe63fe6c6f1b7740a95ca56957f48d1aee34b357",
+                "sha256:e9efa0791b5d9f9dd9689015ea6bce0a27fcd5ecbcd30e6d940bffa4f7f03389",
+                "sha256:eea593cf466b9c126ce667f6d30503624ef24459f118c75594a69353b6c3d5fc",
+                "sha256:f93351459f110b4c1ee28556aef9a791832df6f910bea7b3f616109d534df06b"
             ],
             "index": "pypi",
-            "version": "==1.4.1"
+            "version": "==2.0.0"
         },
         "figgypy": {
             "hashes": [
@@ -95,48 +114,136 @@
             ],
             "version": "==0.9.4"
         },
+        "joblib": {
+            "hashes": [
+                "sha256:21e0c34a69ad7fde4f2b1f3402290e9ec46f545f15f1541c582edfe05d87b63a",
+                "sha256:315d6b19643ec4afd4c41c671f9f2d65ea9d787da093487a81ead7b0bac94524"
+            ],
+            "version": "==0.13.2"
+        },
+        "kiwisolver": {
+            "hashes": [
+                "sha256:05b5b061e09f60f56244adc885c4a7867da25ca387376b02c1efc29cc16bcd0f",
+                "sha256:26f4fbd6f5e1dabff70a9ba0d2c4bd30761086454aa30dddc5b52764ee4852b7",
+                "sha256:3b2378ad387f49cbb328205bda569b9f87288d6bc1bf4cd683c34523a2341efe",
+                "sha256:400599c0fe58d21522cae0e8b22318e09d9729451b17ee61ba8e1e7c0346565c",
+                "sha256:47b8cb81a7d18dbaf4fed6a61c3cecdb5adec7b4ac292bddb0d016d57e8507d5",
+                "sha256:53eaed412477c836e1b9522c19858a8557d6e595077830146182225613b11a75",
+                "sha256:58e626e1f7dfbb620d08d457325a4cdac65d1809680009f46bf41eaf74ad0187",
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-            "version": "==5.5.1"
+            "version": "==2.4.0"
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         "python-dateutil": {
             "hashes": [
@@ -153,28 +260,28 @@
             ],
             "version": "==0.4.4"
         },
-        "python-mimeparse": {
+        "pytz": {
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-            "version": "==1.6.0"
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@@ -185,70 +292,46 @@
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+                "sha256:7f11d75eb2ca5825806f2a3d5464bf9ab05a8e0b7bbfe4b6add343c9716a48fa",
+                "sha256:954db782a4227a0850af1f11c5cf033a439f4b9644daef778bd7199e0348fc85",
+                "sha256:95e71f96f40782df2a664c3d6534d6f70a65af714ec0151c0a5b64c0dc2c4fc0",
+                "sha256:a0abfcbb5e447c49d7b78e598dc4b059eb22ab25f9f0088dc862ce6bc94d7e7a",
+                "sha256:ad405a680e7617a4c470f1372a62e05bb866dc4bfdb6b50d310ad46ac18098e6",
+                "sha256:b2a84358d38530b40ddca3288f3bef642019cd9a4f89260c4078f41e9b873790",
+                "sha256:c725a5293e89f9c0fae0d81dc864442e5dabfa19a77f50fe799e33520f045f6f",
+                "sha256:cfec23b494448b6c7fe35e3782afbba234ebd4137fa22ac1be3dd77d623c6e85",
+                "sha256:d226348527c68effc84b1ce35b0d933cb397ac949f2b98eca98803c5bd95f44c",
+                "sha256:df336c2ce979793f65ce847d0e903d4f34b2ce9ac47d613a0c588d2ee5124084",
+                "sha256:fbacf86fc57581bae27a4fd5b518c6e857210435ec7911c57edb017cbedc9ed0"
             ],
             "index": "pypi",
-            "version": "==0.20.3"
+            "version": "==0.21.1"
         },
         "scipy": {
             "hashes": [
-                "sha256:014cb900c003b5ac81a53f2403294e8ecf37aedc315b59a6b9370dce0aa7627a",
-                "sha256:281a34da34a5e0de42d26aed692ab710141cad9d5d218b20643a9cb538ace976",
-                "sha256:588f9cc4bfab04c45fbd19c1354b5ade377a8124d6151d511c83730a9b6b2338",
-                "sha256:5a10661accd36b6e2e8855addcf3d675d6222006a15795420a39c040362def66",
-                "sha256:628f60be272512ca1123524969649a8cb5ae8b31cca349f7c6f8903daf9034d7",
-                "sha256:6dcc43a88e25b815c2dea1c6fac7339779fc988f5df8396e1de01610604a7c38",
-                "sha256:70e37cec0ac0fe95c85b74ca4e0620169590fd5d3f44765f3c3a532cedb0e5fd",
-                "sha256:7274735fb6fb5d67d3789ddec2cd53ed6362539b41aa6cc0d33a06c003aaa390",
-                "sha256:78e12972e144da47326958ac40c2bd1c1cca908edc8b01c26a36f9ffd3dce466",
-                "sha256:790cbd3c8d09f3a6d9c47c4558841e25bac34eb7a0864a9def8f26be0b8706af",
-                "sha256:79792c8fe8e9d06ebc50fe23266522c8c89f20aa94ac8e80472917ecdce1e5ba",
-                "sha256:865afedf35aaef6df6344bee0de391ee5e99d6e802950a237f9fb9b13e441f91",
-                "sha256:870fd401ec7b64a895cff8e206ee16569158db00254b2f7157b4c9a5db72c722",
-                "sha256:963815c226b29b0176d5e3d37fc9de46e2778ce4636a5a7af11a48122ef2577c",
-                "sha256:9726791484f08e394af0b59eb80489ad94d0a53bbb58ab1837dcad4d58489863",
-                "sha256:9de84a71bb7979aa8c089c4fb0ea0e2ed3917df3fb2a287a41aaea54bbad7f5d",
-                "sha256:b2c324ddc5d6dbd3f13680ad16a29425841876a84a1de23a984236d1afff4fa6",
-                "sha256:b86ae13c597fca087cb8c193870507c8916cefb21e52e1897da320b5a35075e5",
-                "sha256:ba0488d4dbba2af5bf9596b849873102d612e49a118c512d9d302ceafa36e01a",
-                "sha256:d78702af4102a3a4e23bb7372cec283e78f32f5573d92091aa6aaba870370fe1",
-                "sha256:def0e5d681dd3eb562b059d355ae8bebe27f5cc455ab7c2b6655586b63d3a8ea",
-                "sha256:e085d1babcb419bbe58e2e805ac61924dac4ca45a07c9fa081144739e500aa3c",
-                "sha256:e2cfcbab37c082a5087aba5ff00209999053260441caadd4f0e8f4c2d6b72088",
-                "sha256:e742f1f5dcaf222e8471c37ee3d1fd561568a16bb52e031c25674ff1cf9702d5",
-                "sha256:f06819b028b8ef9010281e74c59cb35483933583043091ed6b261bb1540f11cc",
-                "sha256:f15f2d60a11c306de7700ee9f65df7e9e463848dbea9c8051e293b704038da60",
-                "sha256:f31338ee269d201abe76083a990905473987371ff6f3fdb76a3f9073a361cf37",
-                "sha256:f6b88c8d302c3dac8dff7766955e38d670c82e0d79edfc7eae47d6bb2c186594"
+                "sha256:03b1e0775edbe6a4c64effb05fff2ce1429b76d29d754aa5ee2d848b60033351",
+                "sha256:09d008237baabf52a5d4f5a6fcf9b3c03408f3f61a69c404472a16861a73917e",
+                "sha256:10325f0ffac2400b1ec09537b7e403419dcd25d9fee602a44e8a32119af9079e",
+                "sha256:1db9f964ed9c52dc5bd6127f0dd90ac89791daa690a5665cc01eae185912e1ba",
+                "sha256:409846be9d6bdcbd78b9e5afe2f64b2da5a923dd7c1cd0615ce589489533fdbb",
+                "sha256:4907040f62b91c2e170359c3d36c000af783f0fa1516a83d6c1517cde0af5340",
+                "sha256:6c0543f2fdd38dee631fb023c0f31c284a532d205590b393d72009c14847f5b1",
+                "sha256:826b9f5fbb7f908a13aa1efd4b7321e36992f5868d5d8311c7b40cf9b11ca0e7",
+                "sha256:a7695a378c2ce402405ea37b12c7a338a8755e081869bd6b95858893ceb617ae",
+                "sha256:a84c31e8409b420c3ca57fd30c7589378d6fdc8d155d866a7f8e6e80dec6fd06",
+                "sha256:adadeeae5500de0da2b9e8dd478520d0a9945b577b2198f2462555e68f58e7ef",
+                "sha256:b283a76a83fe463c9587a2c88003f800e08c3929dfbeba833b78260f9c209785",
+                "sha256:c19a7389ab3cd712058a8c3c9ffd8d27a57f3d84b9c91a931f542682bb3d269d",
+                "sha256:c3bb4bd2aca82fb498247deeac12265921fe231502a6bc6edea3ee7fe6c40a7a",
+                "sha256:c5ea60ece0c0c1c849025bfc541b60a6751b491b6f11dd9ef37ab5b8c9041921",
+                "sha256:db61a640ca20f237317d27bc658c1fc54c7581ff7f6502d112922dc285bdabee"
             ],
-            "version": "==1.2.1"
+            "version": "==1.3.0"
         },
         "seria": {
             "hashes": [
@@ -272,11 +355,11 @@
         },
         "urllib3": {
             "hashes": [
-                "sha256:61bf29cada3fc2fbefad4fdf059ea4bd1b4a86d2b6d15e1c7c0b582b9752fe39",
-                "sha256:de9529817c93f27c8ccbfead6985011db27bd0ddfcdb2d86f3f663385c6a9c22"
+                "sha256:2393a695cd12afedd0dcb26fe5d50d0cf248e5a66f75dbd89a3d4eb333a61af4",
+                "sha256:a637e5fae88995b256e3409dc4d52c2e2e0ba32c42a6365fee8bbd2238de3cfb"
             ],
             "markers": "python_version >= '3.4'",
-            "version": "==1.24.1"
+            "version": "==1.24.3"
         },
         "xmltodict": {
             "hashes": [
diff --git a/api/api.py b/api/api.py
index b801387..a3cacd4 100644
--- a/api/api.py
+++ b/api/api.py
@@ -44,6 +44,3 @@ def server():
     api.add_route('/operators', OperatorRoute())
 
     StandaloneApplication(application, options).run()
-
-
-
diff --git a/api/app.py b/api/app.py
index 8a1be2e..a0d6f96 100644
--- a/api/app.py
+++ b/api/app.py
@@ -21,5 +21,3 @@ def load_config(self):
 
     def load(self):
         return self.application
-
-
diff --git a/api/json.py b/api/json.py
new file mode 100644
index 0000000..a46f41f
--- /dev/null
+++ b/api/json.py
@@ -0,0 +1,20 @@
+import json
+
+from cassandra.cqlengine.query import ModelQuerySet
+
+
+class CassandraDataEncoder(json.JSONEncoder):
+    def default(self, cassandra_object: ModelQuerySet):
+        keys = []
+        for v in cassandra_object.model.__dict__['_defined_columns'].items():
+            keys.append(v[0])
+
+        result = []
+        for row in cassandra_object:
+            parsed_row = {}
+            for key in keys:
+                parsed_row[key] = row[key]
+
+            result.append(parsed_row)
+
+        return result
diff --git a/api/routes/dataset_key_route.py b/api/routes/dataset_key_route.py
index 35aff2f..3029e3d 100644
--- a/api/routes/dataset_key_route.py
+++ b/api/routes/dataset_key_route.py
@@ -1,6 +1,7 @@
 import json
 import falcon
 
+from api.json import CassandraDataEncoder
 from db.models.DataSetMap import DataSetMap
 
 
@@ -10,16 +11,8 @@ def on_get(req, resp):
         try:
             operator_id = req.params['operator_id']
             result = DataSetMap.objects.filter(operator_id=operator_id)
-            body = []
-            for data_set_map in result:
-                body.append({
-                    'operator_id': data_set_map.operator_id,
-                    'acronym': data_set_map.acronym,
-                    'kpi_name': data_set_map.kpi_name,
-                    'has_enough': data_set_map.has_enough
-                })
 
-            resp.body = json.dumps(body, ensure_ascii=False)
+            resp.body = json.dumps(result, ensure_ascii=False, cls=CassandraDataEncoder)
             resp.status = falcon.HTTP_200
         except KeyError as err:
             missing_field = str(err)
diff --git a/api/routes/dataset_route.py b/api/routes/dataset_route.py
index 805cd05..37c5fe8 100644
--- a/api/routes/dataset_route.py
+++ b/api/routes/dataset_route.py
@@ -2,6 +2,7 @@
 import falcon
 import time_uuid
 
+from api.json import CassandraDataEncoder
 from db.models.DataPoint import DataPoint
 
 
diff --git a/api/routes/kpi_definition_route.py b/api/routes/kpi_definition_route.py
index 3df6024..e0d8008 100644
--- a/api/routes/kpi_definition_route.py
+++ b/api/routes/kpi_definition_route.py
@@ -33,5 +33,3 @@ def on_get(req, resp):
             missing_field = str(err)
             resp.body = json.dumps({'error': "{} is required".format(missing_field)})
             resp.status = falcon.HTTP_400
-
-
diff --git a/api/routes/operator_route.py b/api/routes/operator_route.py
index 823934d..0cab022 100644
--- a/api/routes/operator_route.py
+++ b/api/routes/operator_route.py
@@ -1,21 +1,14 @@
 import json
 import falcon
 
+from api.json import CassandraDataEncoder
 from db.models.Operator import Operator
 
 
 class OperatorRoute(object):
     @staticmethod
-    def on_get(req, resp):
+    def on_get(_, resp):
         result = Operator.objects.all()
 
-        body = []
-        for operator_row in result:
-            body.append({
-                'operator_id': operator_row.operator_id,
-                'operator_name': "OPERATOR"
-            })
-
-        resp.body = json.dumps(body, ensure_ascii=False)
+        resp.body = json.dumps(result, ensure_ascii=False, cls=CassandraDataEncoder)
         resp.status = falcon.HTTP_200
-
diff --git a/api/routes/outlier_route.py b/api/routes/outlier_route.py
index b3058b0..bed0504 100644
--- a/api/routes/outlier_route.py
+++ b/api/routes/outlier_route.py
@@ -31,17 +31,34 @@ def on_get(req, resp):
 
             final_result = []
             if method == 'sim':
-                final_result = detector.knn_mean_sim(similarity=3)
+                final_result, _ = detector.knn_mean_sim(similarity=5)
 
-            if method == 'dist':
-                final_result = detector.knn_mean_dist()
+            if method == 'dist_f':
+                final_result, _ = detector.knn_mean_dist(variant='fixed')
+
+            if method == 'dist_c':
+                final_result, _ = detector.knn_mean_dist(variant='calculated', distance_ratio=4)
 
             if method == 'reg':
-                final_result = detector.regression_line()
+                final_result, _ = detector.regression_line(tolerance=0.5)
+
+            if method == 'isolation_forest':
+                final_result, _ = detector.isolation_forest()
+
+            if method == 'random_split':
+                final_result, _ = detector.rnd_split()
+
+            if method == 'fusion':
+                final_result = detector.fusion({
+                    'clusters': 30,
+                    'distance_ratio': 4,
+                    'similarity': 5,
+                    'precision': 0.5,
+                    'tolerance': 0.5
+                })['data']['fusion_result']
 
             resp.body = json.dumps(final_result, ensure_ascii=False)
             resp.status = falcon.HTTP_200
         except ValueError as err:
             resp.body = json.dumps({'error': "{}".format(str(err))})
             resp.status = falcon.HTTP_400
-
diff --git a/cli/cli.py b/cli/cli.py
index 5d8af8c..7641ccf 100644
--- a/cli/cli.py
+++ b/cli/cli.py
@@ -8,6 +8,7 @@
 from db.Database import Database
 from load_data.load_data import load_data
 from load_data.load_operators import load_operators
+from run_all.run_all import run_all
 
 
 @click.group()
@@ -41,9 +42,11 @@ def __count_dataset():
     count_dataset()
 
 
+@cli.command('run_all')
+def __run_all():
+    run_all()
+
+
 @cli.command('server')
 def __server():
     server()
-
-
-
diff --git a/count_dataset/count_dataset.py b/count_dataset/count_dataset.py
index 2a82037..58fc537 100644
--- a/count_dataset/count_dataset.py
+++ b/count_dataset/count_dataset.py
@@ -1,8 +1,9 @@
 from db.models.DataPoint import DataPoint
 from db.models.DataSetMap import DataSetMap
+from db.models.Operator import Operator
 
 
-def count_dataset(limit=200):
+def count_dataset(limit=700):
     result = DataSetMap.objects.all()
 
     for data_set_map in result:
@@ -10,6 +11,10 @@ def count_dataset(limit=200):
         acronym = data_set_map.acronym
         kpi_name = data_set_map.kpi_name
         has_enough = len(DataPoint.objects.filter(operator_id=operator_id, acronym=acronym, kpi_name=kpi_name).limit(limit))
-        print(has_enough)
         DataSetMap.objects(operator_id=operator_id, acronym=acronym, kpi_name=kpi_name).update(has_enough=has_enough >= limit)
 
+        operator = Operator.objects(operator_id=operator_id).first()
+        if not operator.has_enough or operator.has_enough is None:
+            print(operator_id, has_enough >= limit)
+            Operator.objects(operator_id=operator_id).update(has_enough=has_enough >= limit)
+
diff --git a/db/Database.py b/db/Database.py
index a9b34c2..e254eea 100755
--- a/db/Database.py
+++ b/db/Database.py
@@ -8,6 +8,7 @@
 from db.models.DataSetMap import DataSetMap
 from db.models.AcronymNameMap import AcronymNameMap
 from db.models.Operator import Operator
+from db.models.Report import Report
 
 
 class Database:
@@ -27,6 +28,7 @@ def sync():
         sync_table(DataSetMap)
         sync_table(AcronymNameMap)
         sync_table(Operator)
+        sync_table(Report)
 
     def query(self, query):
         session = self.cluster.connect()
diff --git a/db/models/Operator.py b/db/models/Operator.py
index c8f8030..b459bae 100644
--- a/db/models/Operator.py
+++ b/db/models/Operator.py
@@ -4,6 +4,7 @@
 
 class Operator(Model):
     operator_id = Integer(primary_key=True)
+    has_enough = Boolean()
 
     __keyspace__ = "data_det"
     __table_name__ = "operators"
diff --git a/db/models/Report.py b/db/models/Report.py
new file mode 100644
index 0000000..8fbd9f1
--- /dev/null
+++ b/db/models/Report.py
@@ -0,0 +1,16 @@
+from cassandra.cqlengine.columns import *
+from cassandra.cqlengine.models import Model
+
+
+class Report(Model):
+    operator_id = BigInt(primary_key=True)
+    acronym = Text(primary_key=True)
+    kpi_name = Text(primary_key=True)
+    start_time = TimeUUID(primary_key=True)
+    method = Text()
+    params = Text()
+    count_outliers = BigInt()
+    end_time = TimeUUID()
+
+    __keyspace__ = "data_det"
+    __table_name__ = "reports"
diff --git a/load_data/load_data.py b/load_data/load_data.py
index 17ce1fb..35c5793 100644
--- a/load_data/load_data.py
+++ b/load_data/load_data.py
@@ -26,14 +26,14 @@ def load_kpi_definitions(skip_if_exists=True):
     result = RawKpiDefinition.objects.all()
     filtered_result = filter_complex_kpis(result)
     for raw_kpi_definition in filtered_result:
-        d_id = raw_kpi_definition.id
+        definition_id = raw_kpi_definition.id
         description = raw_kpi_definition.text
         formula = raw_kpi_definition.formula
         tags = raw_kpi_definition.tags
         technology = raw_kpi_definition.technology
         unit = raw_kpi_definition.unit
 
-        KpiDefinition.create(id=d_id, description=description, formula=formula, tags=tags, technology=technology, unit=unit)
+        KpiDefinition.create(id=definition_id, description=description, formula=formula, tags=tags, technology=technology, unit=unit)
 
 
 def load_data_points(skip_if_exists=True):
diff --git a/load_data/load_operators.py b/load_data/load_operators.py
index e4527c8..9a6562d 100644
--- a/load_data/load_operators.py
+++ b/load_data/load_operators.py
@@ -5,7 +5,7 @@
 def load_operators(skip_if_exists=True):
     print("Loading operators")
     if len(DataPoint.objects.limit(1)) is 0:
-        raise ResourceWarning("collection data_set_map is empty")
+        raise ResourceWarning("collection data_points is empty")
 
     if len(Operator.objects.limit(1)) is not 0 and skip_if_exists:
         print("Skipping due to skip_if_exists: True")
diff --git a/outlier_detector/OutlierDetector.py b/outlier_detector/OutlierDetector.py
index 36bf5ab..a90d09e 100644
--- a/outlier_detector/OutlierDetector.py
+++ b/outlier_detector/OutlierDetector.py
@@ -1,21 +1,18 @@
 from datetime import datetime
+import time
 
 from outlier_detector.dist_kmean import dist_kmean
+from outlier_detector.isolation_forest import isl_forest
+from outlier_detector.learn_utils import flatten, count_duplicates
 from outlier_detector.linear_regression import linear_regression
+from outlier_detector.random_split import random_split
 from outlier_detector.sim_kmean import sim_kmean
-import numpy as np
 
 
 class OutlierDetector:
     def __init__(self, dataset, feature):
         self.dataset = dataset
         self.dataset_with_day = self.get_dataset_with_day_feature(feature)
-        self.kmean_clusters = {
-            'weekday': 7,
-            'monthday': 12,
-            'day': 31,
-            'timestamp': 50
-        }[feature]
 
     def get_dataset_with_day_feature(self, feature):
         dataset_with_day = self.dataset
@@ -30,23 +27,105 @@ def get_dataset_with_day_feature(self, feature):
             if feature == 'weekday':
                 day = daytime_timestamp.weekday()
 
-            if feature == 'timestamp':
-                day = data['timestamp']
+            if feature == 'none':
+                day = dataset_with_day[index]['timestamp']
 
             dataset_with_day[index]['day'] = int(day)
 
         return dataset_with_day
 
-    def knn_mean_sim(self, similarity=2):
-        outlier_detect_method = lambda: sim_kmean(self.kmean_clusters, self.dataset_with_day, similarity)
-        return self.run_calculations(outlier_detect_method)
+    def knn_mean_sim(self, clusters=30, similarity=5):
+        start = time.time()
+        outlier_detect_method = lambda: sim_kmean(clusters, self.dataset_with_day, similarity)
 
-    def knn_mean_dist(self, distance_ratio=5):
-        outlier_detect_method = lambda: dist_kmean(self.kmean_clusters, self.dataset_with_day, distance_ratio)
-        return self.run_calculations(outlier_detect_method)
+        results = self.run_calculations(outlier_detect_method)
+        duration = time.time() - start
 
-    def regression_line(self):
-        return linear_regression(self.dataset_with_day)
+        return results, duration
+
+    def knn_mean_dist(self, clusters=30, variant='fixed', distance_ratio=2):
+        start = time.time()
+        outlier_detect_method = lambda: dist_kmean(clusters, self.dataset_with_day, variant, distance_ratio)
+
+        results = self.run_calculations(outlier_detect_method)
+        duration = time.time() - start
+
+        return results, duration
+
+    def regression_line(self, tolerance=0.5):
+        start = time.time()
+
+        results = linear_regression(self.dataset_with_day, tolerance)
+        duration = time.time() - start
+
+        return results, duration
+
+    def isolation_forest(self):
+        start = time.time()
+        outlier_detect_method = lambda: isl_forest(self.dataset_with_day)
+
+        results = self.run_calculations(outlier_detect_method)
+        duration = time.time() - start
+
+        return results, duration
+
+    def rnd_split(self):
+        start = time.time()
+
+        results = random_split(self.dataset_with_day)
+        duration = time.time() - start
+
+        return results, duration
+
+    def fusion(self, options, algorithms='_all_'):
+        clusters = options['clusters']
+        distance_ratio = options['distance_ratio']
+        similarity = options['similarity']
+        precision = options['precision']
+        tolerance = options['tolerance']
+
+        knn_sim_result = knn_dist_f_result = knn_dist_c_result = regression_line_result = fusion_result = if_results = None
+        durations = {
+            'fusion': 0
+        }
+
+        if 'sim' in algorithms or '_all_' in algorithms:
+            knn_sim_result, duration = self.knn_mean_sim(clusters, similarity)
+            durations['sim'] = duration
+
+        if 'dist_f' in algorithms or '_all_' in algorithms:
+            knn_dist_f_result, duration = self.knn_mean_dist(clusters, variant='fixed')
+            durations['dist_f'] = duration
+
+        if 'dist_c' in algorithms or '_all_' in algorithms:
+            knn_dist_c_result, duration = self.knn_mean_dist(clusters, variant='calculated',
+                                                             distance_ratio=distance_ratio)
+            durations['dist_c'] = duration
+
+        if 'reg' in algorithms or '_all_' in algorithms:
+            regression_line_result, duration = self.regression_line(tolerance=tolerance)
+            durations['reg'] = duration
+
+        if 'if' in algorithms:
+            if_results, duration = self.isolation_forest()
+            durations['if'] = duration
+
+        if '_all_' in algorithms:
+            fusion_result = self.get_fusion_result(
+                [knn_sim_result, knn_dist_f_result, knn_dist_c_result, regression_line_result],
+                precision=precision)
+
+        return {
+            'data': {
+                'knn_sim_result': knn_sim_result,
+                'knn_dist_f_result': knn_dist_f_result,
+                'knn_dist_c_result': knn_dist_c_result,
+                'regression_line_result': regression_line_result,
+                'if_results': if_results,
+                'fusion_result': fusion_result,
+            },
+            'durations': durations
+        }
 
     @staticmethod
     def run_calculations(method):
@@ -81,3 +160,27 @@ def run_calculations(method):
             })
 
         return final_result
+
+    @staticmethod
+    def get_fusion_result(methods, precision):
+        outliers = []
+
+        for index, method_result in enumerate(methods):
+            method_outliers = []
+            for data_point in method_result:
+                if data_point['is_outlier']:
+                    method_outliers.append(data_point['id'])
+
+            outliers.append(method_outliers)
+
+        occurrences = dict(count_duplicates(flatten(outliers)))
+        final_result = []
+        for data_point in methods[0]:
+            data_point_cp = data_point.copy()
+            if data_point_cp['id'] in occurrences:
+                is_outlier = occurrences[data_point_cp['id']] >= precision * len(methods)
+                data_point_cp['is_outlier'] = is_outlier
+
+            final_result.append(data_point_cp)
+
+        return final_result
diff --git a/outlier_detector/dist_kmean.py b/outlier_detector/dist_kmean.py
index 5b307d1..d023940 100644
--- a/outlier_detector/dist_kmean.py
+++ b/outlier_detector/dist_kmean.py
@@ -2,35 +2,93 @@
 from sklearn.cluster import KMeans
 from math import hypot
 
+from outlier_detector.learn_utils import create_points_with_labels
 
-def dist_kmean(n_clusters, dataset, distance_ratio):
-    filtered_dataset = np.array(list(map(lambda data: [data['value'], data['day']], dataset)))
+
+def dist_kmean(n_clusters, dataset, variant, distance_ratio):
+    filtered_dataset = np.array(list(map(lambda data: [data['value'], data['day'], data['timestamp']], dataset)))
     model = KMeans(n_clusters)
     clusterer = model.fit(filtered_dataset)
     labels = model.predict(filtered_dataset)
     centroids = clusterer.cluster_centers_
+    dataset_with_labels = create_points_with_labels(dataset, labels)
+
+    distances = calculate_distances(dataset_with_labels, centroids)
+    max_values_with_ratio = calculate_ratio(dataset_with_labels, distances)
+
+    # Calculate outliers based on given method
+    labeled_result = dataset_with_labels
+    for index, data_point in enumerate(dataset_with_labels):
+        if data_point['id'] in [_id for _id in max_values_with_ratio.keys()]:
+            if variant == 'fixed':
+                is_outlier = get_fixed_outliers(data_point, max_values_with_ratio)
+            elif variant == 'calculated':
+                is_outlier = get_calculated_outliers(data_point, max_values_with_ratio, distance_ratio)
+        else:
+            is_outlier = False
+
+        labeled_result[index]['is_outlier'] = is_outlier
+
+    return labeled_result
 
+
+def calculate_points_distance(centroid, data_point) -> float:
+    _x = (centroid[0] - data_point['value']) ** 2
+
+    return np.sqrt(_x)
+
+
+def calculate_distances(dataset, centroids):
     distances = {}
-    labeled_result = dataset
+    for data_point in dataset:
+        label = data_point['label']
 
-    for index, data_point in enumerate(dataset):
-        self_centroid = centroids[labels[index]]
-        dist = hypot(self_centroid[0] - data_point['value'], self_centroid[1] - data_point['day'])
-        distances[index] = dist
+        if label not in distances:
+            distances[label] = {}
 
-    distances_in_tuples = sorted(distances.items(), key=lambda kv: kv[1])
-    mid_val = distances_in_tuples[int(len(dataset) / 2)]
+        distances[label][data_point['id']] = calculate_points_distance(centroids[label], data_point)
 
-    outlier_ids = []
-    # for dist in distances_in_tuples:
-    #     if dist[1] > mid_val[1] * distance_ratio:
-    #         outlier_ids.append(dist[0])
+    return distances
 
-    for dist in distances_in_tuples[-5:]:
-        outlier_ids.append(dist[0])
 
-    for index, data_point in enumerate(labeled_result):
-        labeled_result[index]['is_outlier'] = index in outlier_ids
-        labeled_result[index]['label'] = int(labels[index])
+def calculate_ratio(dataset, distances):
+    max_values_with_ratio = {}
+    for data_point in dataset:
+        _min, _max, _mean = get_cluster_info(distances[data_point['label']])
+        dist = distances[data_point['label']][data_point['id']]
+        is_max_point = dist == _max
+        if is_max_point:
+            max_values_with_ratio[data_point['id']] = dist / _mean
 
-    return labeled_result
+    return max_values_with_ratio
+
+
+def get_cluster_info(cluster_distances):
+    values_arr = []
+    for k, v in cluster_distances.items():
+        values_arr.append(v)
+
+    _min = np.min(values_arr)
+    _max = np.max(values_arr)
+    _mean = np.mean(values_arr)
+
+    return _min, _max, _mean
+
+
+def get_fixed_outliers(data_point, ratios):
+    sorted_ratios = sorted(ratios.items(), key=lambda kv: kv[1])
+    outliers = []
+    for _tuple in sorted_ratios[-5:]:
+        outliers.append(_tuple[0])
+
+    return data_point['id'] in outliers
+
+
+def get_calculated_outliers(data_point, ratios, distance_ratio):
+    sorted_ratios = sorted(ratios.items(), key=lambda kv: kv[1])
+    outliers = []
+    for _tuple in sorted_ratios:
+        if _tuple[1] > distance_ratio:
+            outliers.append(_tuple[0])
+
+    return data_point['id'] in outliers
diff --git a/outlier_detector/isolation_forest.py b/outlier_detector/isolation_forest.py
new file mode 100644
index 0000000..2903d22
--- /dev/null
+++ b/outlier_detector/isolation_forest.py
@@ -0,0 +1,21 @@
+from sklearn.ensemble import IsolationForest
+import numpy as np
+
+from outlier_detector.learn_utils import create_points_with_labels
+
+
+def isl_forest(dataset):
+    rng = np.random.RandomState(42)
+    filtered_dataset = np.array(list(map(lambda data: [data['value'], data['day'], data['timestamp']], dataset)))
+    clf = IsolationForest(n_estimators=100, behaviour='new', max_samples=100,
+                          random_state=rng, contamination=0.01)
+
+    clf.fit(filtered_dataset)
+    prediction = clf.predict(filtered_dataset)
+
+    labeled_result = []
+    for data_point in create_points_with_labels(dataset, list(prediction)):
+        data_point['is_outlier'] = data_point['label'] == -1
+        labeled_result.append(data_point)
+
+    return labeled_result
diff --git a/outlier_detector/learn_utils.py b/outlier_detector/learn_utils.py
index 590effb..21d7d6c 100644
--- a/outlier_detector/learn_utils.py
+++ b/outlier_detector/learn_utils.py
@@ -1,4 +1,5 @@
 import numpy as np
+import collections
 
 
 def create_windows(data, window_len, slide_len):
@@ -8,3 +9,47 @@ def create_windows(data, window_len, slide_len):
         chunks.append(chunk)
 
     return chunks
+
+
+def to_clusters(dataset, labels):
+    current_label = labels[0]
+    current_cluster = []
+    clusters = []
+    clusters_dict = {}
+
+    for index, label in enumerate(labels):
+        if label != current_label:
+            clusters.append(current_cluster)
+            clusters_dict[current_label] = current_cluster
+            current_cluster = [dataset[index]]
+        else:
+            current_cluster.append(dataset[index])
+
+        current_label = label
+
+    clusters_dict[current_label] = current_cluster
+
+    return clusters[:1], clusters_dict
+
+
+def create_points_with_labels(dataset, labels):
+    dataset_with_labels = []
+    for index, data_point in enumerate(dataset):
+        point_with_label = data_point
+        point_with_label['label'] = int(labels[index])
+        dataset_with_labels.append(point_with_label)
+
+    return dataset_with_labels
+
+
+def flatten(to_flat_list):
+    flat_list = []
+    for x in to_flat_list:
+        for item in x:
+            flat_list.append(item)
+
+    return flat_list
+
+
+def count_duplicates(arg):
+    return collections.Counter(arg)
diff --git a/outlier_detector/linear_regression.py b/outlier_detector/linear_regression.py
index a593788..c5f1d6e 100644
--- a/outlier_detector/linear_regression.py
+++ b/outlier_detector/linear_regression.py
@@ -4,26 +4,20 @@
 from outlier_detector.learn_utils import create_windows
 
 
-def linear_regression(dataset, acceptance=0.5, window_len=22, slide_len=2):
-    x = np.array(list(map(lambda p: p['day'], dataset))).reshape(-1, 1)
-    y = np.array(list(map(lambda p: p['value'], dataset))).reshape(-1, 1)
-    model = LinearRegression()
-    regressor = model.fit(x, y)
-
+def linear_regression(dataset, tolerance=0.5, window_len=22, slide_len=2):
     outliers_counter = {}
     labels = {}
     labeled_result = dataset
     segments = create_windows(dataset, window_len, slide_len)
 
     for segment_num, segment in enumerate(segments):
-        x_segment = np.array(list(map(lambda p: p['day'], segment))).reshape(-1, 1)
-
-        values = regressor.predict(x_segment)
+        values = get_predicted_values(segment)
 
         for index, data_point in enumerate(segment):
             labels[data_point['id']] = segment_num
             condition_value = abs(data_point['value'] / values[index][0])
-            is_outlier = condition_value > (1 + acceptance) or condition_value < (1 - acceptance)
+            is_outlier = condition_value > (1 + tolerance) or condition_value < (1 - tolerance)
+
             if is_outlier:
                 try:
                     outliers_counter[data_point['id']] = outliers_counter[data_point['id']] + 1
@@ -32,7 +26,7 @@ def linear_regression(dataset, acceptance=0.5, window_len=22, slide_len=2):
 
     for index, data_point in enumerate(labeled_result):
         try:
-            is_outlier = outliers_counter[data_point['id']] > int(0.75 * window_len / 2)
+            is_outlier = outliers_counter[data_point['id']] > int(0.8 * window_len / 2)
         except KeyError:
             is_outlier = False
 
@@ -40,3 +34,12 @@ def linear_regression(dataset, acceptance=0.5, window_len=22, slide_len=2):
         labeled_result[index]['label'] = labels[data_point['id']]
 
     return labeled_result
+
+
+def get_predicted_values(segment):
+    x_segment = np.array(list(map(lambda p: p['timestamp'], segment))).reshape(-1, 1)
+    y_segment = np.array(list(map(lambda p: p['value'], segment))).reshape(-1, 1)
+    model = LinearRegression()
+    regressor = model.fit(x_segment, y_segment)
+    prediction = regressor.predict(x_segment)
+    return prediction
diff --git a/outlier_detector/random_split.py b/outlier_detector/random_split.py
new file mode 100644
index 0000000..cedff23
--- /dev/null
+++ b/outlier_detector/random_split.py
@@ -0,0 +1,55 @@
+from random import randrange
+
+import numpy as np
+
+
+def random_split(dataset, splits_number=1):
+    filtered_dataset = np.array(list(map(lambda data: [data['value'], data['timestamp']], dataset)))
+
+    _min_v, _max_v = get_min_max(list(map(lambda dp: dp[0], filtered_dataset)))
+    labeled_result = []
+    chunks = []
+    for split in range(0, splits_number):
+        above = []
+        below = []
+        a = randrange(-10, 10) / randrange(0, 20)
+        b = randrange(int(_min_v), int(_max_v))
+
+        line = generate_line(a, b)
+
+        for index, data_point in enumerate(filtered_dataset):
+            x = data_point[1]
+            y = data_point[0]
+
+            if y < line(x):
+                below.append(data_point)
+                label = -1
+            else:
+                above.append(data_point)
+                label = 1
+
+            p = dataset[index]
+            p['label'] = label
+            p['is_outlier'] = False
+            labeled_result.append(p)
+
+        chunks.append(above)
+        chunks.append(below)
+
+    return labeled_result
+
+
+def get_min_max(values):
+    _min = np.min(values)
+    _max = np.max(values)
+
+    return _min, _max
+
+
+def generate_line(a, b):
+    print(a, b)
+
+    def y(x):
+        return a * -1 * (x / -1e9) + b
+
+    return y
diff --git a/outlier_detector/sim_kmean.py b/outlier_detector/sim_kmean.py
index 82ce2a3..f09c942 100644
--- a/outlier_detector/sim_kmean.py
+++ b/outlier_detector/sim_kmean.py
@@ -1,27 +1,39 @@
 import numpy as np
 from sklearn.cluster import KMeans
 
+from outlier_detector.learn_utils import to_clusters, create_points_with_labels
+
 
 def sim_kmean(n_clusters, dataset, similarity):
-    filtered_dataset = np.array(list(map(lambda data: [data['value'], data['day']], dataset)))
+    filtered_dataset = np.array(list(map(lambda data: [data['value'], data['day'], data['timestamp']], dataset)))
     model = KMeans(n_clusters)
-    clusterer = model.fit(filtered_dataset)
+    model.fit(filtered_dataset)
     labels = model.predict(filtered_dataset)
-    centroids = clusterer.cluster_centers_
 
-    labeled_result = dataset
-    centroids_counter = {}
+    dataset_with_labels = create_points_with_labels(dataset, labels)
 
-    for index, label in enumerate(labels):
-        key = str(centroids[label][0])
-        try:
-            centroids_counter[key] = centroids_counter[key] + 1
-        except KeyError:
-            centroids_counter[key] = 1
+    clusters, clusters_dict = to_clusters(dataset, labels)
+    _mean, _max = calculate_clusters_info(clusters)
 
-    for index, label in enumerate(labels):
-        key = str(centroids[label][0])
-        labeled_result[index]['is_outlier'] = centroids_counter[key] <= similarity
-        labeled_result[index]['label'] = int(label)
+    labeled_result = dataset_with_labels
+    for index, data_point in enumerate(dataset_with_labels):
+        labeled_result[index]['is_outlier'] = calculate_outlier_prob(_mean, _max, clusters_dict, data_point['label'],
+                                                                     similarity)
 
     return labeled_result
+
+
+def calculate_outlier_prob(_mean, _max, dict_clusters, label, similarity) -> bool:
+    num_in_cluster = len(dict_clusters[label])
+    return num_in_cluster <= int(_mean / similarity)
+
+
+def calculate_clusters_info(clusters) -> (int, int):
+    points_counter = []
+    for cluster in clusters:
+        points_counter.append(len(cluster))
+
+    _mean = int(np.mean(points_counter))
+    _max = int(np.max(points_counter))
+
+    return _mean, _max
diff --git a/run_all/PlotDrawer.py b/run_all/PlotDrawer.py
new file mode 100644
index 0000000..b9d8776
--- /dev/null
+++ b/run_all/PlotDrawer.py
@@ -0,0 +1,94 @@
+import glob
+import csv
+import math
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+class PlotDrawer:
+    def __init__(self, chart_type, parameter):
+        self._parameter = parameter
+        self._type = chart_type
+        self._reports = {}
+
+    def draw(self):
+        results = self.prepare_all_avg()
+        keys = results['keys']
+
+        fig, ax = plt.subplots()
+        for method, outliers in results['data'].items():
+            ax.plot(keys, outliers, marker='o')
+
+        ax.set(ylabel='Liczba punktów odstających', xlabel=self._parameter,
+               title='Wyniki dla zmiany parametru {}'.format(self._parameter))
+        ax.grid()
+        ax.legend(results['data'].keys())
+
+        plt.savefig("./TESTS/{}_{}.png".format(self._type, self._parameter))
+
+    def load_reports(self):
+        reports = {
+            'headers': [],
+            'data': {}
+        }
+
+        files = glob.glob("./TESTS/*{}*.csv".format(self._parameter))
+        for file in files:
+            results = []
+            dataset_key = '_'.join(file.split('_')[:4])
+
+            with open(file, 'r') as csvfile:
+                reader = csv.reader(csvfile)
+                for row in reader:
+                    results.append(row)
+
+            reports['headers'] = results[0]
+            reports['data'][dataset_key] = results[1:]
+
+        self._reports = reports
+
+    def prepare_all_avg(self):
+        self.load_reports()
+        data = self._reports['data']
+        combined_values = {}
+
+        for _, test_results in data.items():
+            for test in test_results:
+                y = test[1]
+                x = test[2:]
+                if y not in combined_values:
+                    combined_values[y] = []
+
+                combined_values[y].append(x)
+
+        avg_results = {}
+        for key, values in combined_values.items():
+            avg_results[key] = [round(sum(x) / len(values), 2) for x in zip(*PlotDrawer.to_ints(values))]
+
+        return PlotDrawer.prepare_transposed_data(self._reports['headers'][2:], avg_results)
+
+    @staticmethod
+    def to_ints(values):
+        int_values = values.copy()
+        for index, value in enumerate(values):
+            ints = list(map(int, value))
+            int_values[index] = ints
+
+        return int_values
+
+    @staticmethod
+    def prepare_transposed_data(headers, results):
+        raw_data = []
+        for k, v in results.items():
+            raw_data.append(v)
+
+        transposed_data = {
+            'keys': list(results.keys()),
+            'data': {}
+        }
+
+        transposed_values = np.array(raw_data).transpose()
+        for index, row in enumerate(transposed_values):
+            transposed_data['data'][headers[index]] = row
+
+        return transposed_data
diff --git a/run_all/TestCase.py b/run_all/TestCase.py
new file mode 100644
index 0000000..44c0182
--- /dev/null
+++ b/run_all/TestCase.py
@@ -0,0 +1,75 @@
+import datetime
+
+OPTIMAL_CONFIGURATION = {
+    'feature': 'none',
+    'clusters': 30,
+    'distance_ratio': 4,
+    'similarity': 5,
+    'precision': 0.5,
+    'tolerance': 0.5
+}
+
+
+class TestCase:
+    def __init__(self, configuration = {}):
+        self.id = datetime.datetime.now().time()
+        self._configuration = {**OPTIMAL_CONFIGURATION, **configuration}
+        self._durations = None
+
+        self._feature = self._configuration['feature']
+        self._results = {}
+        self._results['dist_c'] = {
+            'clusters': self._configuration['clusters'],
+            'distance_ratio': self._configuration['distance_ratio'],
+            'results': 0
+        }
+
+        self._results['dist_f'] = {
+            'clusters': self._configuration['clusters'],
+            'results': 0
+        }
+
+        self._results['reg'] = {
+            'tolerance': self._configuration['tolerance'],
+            'results': 0
+        }
+
+        self._results['if'] = {
+            'results': 0
+        }
+
+        self._results['sim'] = {
+            'clusters': self._configuration['clusters'],
+            'similarity': self._configuration['similarity'],
+            'results': 0
+        }
+
+        self._results['fusion'] = {
+            'precision': self._configuration['precision'],
+            'results': 0
+        }
+
+    def get_configuration(self):
+        return self._configuration
+
+    def append_results(self, result_dict):
+        self._durations = result_dict['durations']
+        outliers = {}
+        for k, v in result_dict['data'].items():
+            if v is None:
+                outliers[k] = None
+            else:
+                outliers[k] = len(list(filter(lambda dp: dp['is_outlier'], v)))
+
+        self._results['dist_c']['results'] = outliers['knn_dist_c_result']
+        self._results['dist_f']['results'] = outliers['knn_dist_f_result']
+        self._results['sim']['results'] = outliers['knn_sim_result']
+        self._results['reg']['results'] = outliers['regression_line_result']
+        self._results['if']['results'] = outliers['if_results']
+        self._results['fusion']['results'] = outliers['fusion_result']
+
+    def get_results(self):
+        return self._results
+
+    def get_durations(self):
+        return self._durations
diff --git a/run_all/TestComparator.py b/run_all/TestComparator.py
new file mode 100644
index 0000000..68d9c20
--- /dev/null
+++ b/run_all/TestComparator.py
@@ -0,0 +1,56 @@
+from run_all.PlotDrawer import PlotDrawer
+
+
+class TestComparator:
+    def __init__(self, parameter, cases, algorithms=['_all_']):
+        self._parameter = parameter
+        self._cases = cases
+        self._algorithms = algorithms
+
+        self._dataset = None
+
+    def save_dataset(self, dataset):
+        self._dataset = dataset
+
+    def get_test_cases(self):
+        return self._cases
+
+    def get_algorithms(self):
+        return self._algorithms
+
+    def get_parameter(self):
+        return self._parameter
+
+    def generate_data_summary(self):
+        results_key = []
+        for key, value in self._cases[0].get_results().items():
+            if value['results'] is not None:
+                results_key.append(key)
+
+        data = [
+            ['test_id', self._parameter, *results_key]
+        ]
+
+        for test in self._cases:
+            row_data = [test.id, test.get_configuration()[self._parameter]]
+            for key, value in test.get_results().items():
+                if value['results'] is not None:
+                    row_data.append(value['results'])
+
+            data.append(row_data)
+
+        return data
+
+    def generate_csv(self):
+        filename = '{}_{}_{}_{}_{}.csv'.format(
+            self._dataset[0],
+            self._dataset[1],
+            self._dataset[2],
+            self._parameter,
+            '_'.join(self._algorithms)
+        )
+
+        data = self.generate_data_summary()
+        with open("./TESTS/" + filename, 'w+') as file:
+            for row in data:
+                file.write(','.join(str(e) for e in row) + '\n')
diff --git a/run_all/run_all.py b/run_all/run_all.py
new file mode 100644
index 0000000..2620723
--- /dev/null
+++ b/run_all/run_all.py
@@ -0,0 +1,139 @@
+import time_uuid
+
+from db.models.DataPoint import DataPoint
+from db.models.DataSetMap import DataSetMap
+from outlier_detector.OutlierDetector import OutlierDetector
+from run_all.PlotDrawer import PlotDrawer
+from run_all.TestCase import TestCase
+from run_all.TestComparator import TestComparator
+
+TEST_DATASETS = [
+    [899, 'OYTAL', 'RNC_165'],
+    [899, 'OYTAL', 'RNC_5464'],
+    [53, 'MOGILA', 'LTE_1079'],
+    [53, 'NOGALES', 'MGW_1123'],
+    [53, 'NOGALES', 'MGW_2069'],
+    [53, 'NOGALES', 'MSC_2250'],
+    [53, 'NOGALES', 'MSC_601'],
+    [53, 'OCNITA', 'RNC_1883'],
+    [53, 'OCNITA', 'RNC_5070'],
+    [53, 'OCNITA', 'RNC_615'],
+    [534, 'AGUA_PRIETA', 'LTE_1079'],
+    [534, 'AGUA_PRIETA', 'LTE_5060'],
+    [534, 'AGUA_PRIETA', 'LTE_1079'],
+    [534, 'AGUA_PRIETA', 'LTE_5137'],
+    [534, 'AGUA_PRIETA', 'LTE_7013'],
+    [998, 'DARHAN', 'SGSN_525'],
+    [998, 'DARHAN', 'SGSN_544'],
+    [998, 'DARHAN', 'SGSN_776'],
+    [160, 'MOTUL', 'RNC_19'],
+    [160, 'MOTUL', 'RNC_5464'],
+    [160, 'MOTUL', 'RNC_1883'],
+    [283, 'VILANI', 'RNC_1883'],
+    [283, 'VILANI', 'RNC_5081'],
+    [283, 'VILANI', 'RNC_5464'],
+    [283, 'ORIZABA', 'LTE_5003'],
+    [283, 'ORIZABA', 'LTE_5058'],
+    [283, 'ORIZABA', 'LTE_5191'],
+    [283, 'ORIZABA', 'LTE_5432'],
+    [1162, 'SIBU', 'RNC_165'],
+    [1162, 'SIBU', 'RNC_1906'],
+    [1162, 'SIBU', 'RNC_5082'],
+]
+
+IF_COMPARATOR = [
+    TestComparator('feature', [
+        TestCase()
+    ], ['if'])
+]
+
+TEST_COMPARATORS = [
+    TestComparator('feature', [
+        TestCase({'feature': 'none'}),
+        TestCase({'feature': 'day'}),
+        TestCase({'feature': 'weekday'}),
+        TestCase({'feature': 'monthday'})
+    ]),
+    TestComparator('precision', [
+        TestCase({'feature': 'day', 'precision': 0.5}),
+        TestCase({'feature': 'day', 'precision': 0.75}),
+        TestCase({'feature': 'day', 'precision': 1}),
+    ]),
+    TestComparator('clusters', [
+        TestCase({'feature': 'day', 'clusters': 10}),
+        TestCase({'feature': 'day', 'clusters': 15}),
+        TestCase({'feature': 'day', 'clusters': 20}),
+        TestCase({'feature': 'day', 'clusters': 30}),
+        TestCase({'feature': 'day', 'clusters': 50}),
+        TestCase({'feature': 'day', 'clusters': 80}),
+    ], ['sim', 'dist_c', 'dist_f']),
+    TestComparator('distance_ratio', [
+        TestCase({'feature': 'day', 'distance_ratio': 1}),
+        TestCase({'feature': 'day', 'distance_ratio': 2}),
+        TestCase({'feature': 'day', 'distance_ratio': 4}),
+        TestCase({'feature': 'day', 'distance_ratio': 5}),
+        TestCase({'feature': 'day', 'distance_ratio': 7}),
+    ], ['dist_c']),
+    TestComparator('similarity', [
+        TestCase({'feature': 'day', 'similarity': 2}),
+        TestCase({'feature': 'day', 'similarity': 3}),
+        TestCase({'feature': 'day', 'similarity': 4.5}),
+        TestCase({'feature': 'day', 'similarity': 5}),
+        TestCase({'feature': 'day', 'similarity': 7}),
+    ], ['sim']),
+    TestComparator('tolerance', [
+        TestCase({'feature': 'day', 'tolerance': 0.25}),
+        TestCase({'feature': 'day', 'tolerance': 0.3}),
+        TestCase({'feature': 'day', 'tolerance': 0.5}),
+        TestCase({'feature': 'day', 'tolerance': 0.8}),
+        TestCase({'feature': 'day', 'tolerance': 0.95}),
+    ], ['reg']),
+]
+
+
+def run_all(with_calculations=True):
+    print("Calculating outliers for all defined algorithms")
+    if len(DataSetMap.objects.limit(1)) is 0:
+        raise ResourceWarning("collection data_set_map is empty")
+
+    if with_calculations:
+        for dataset in TEST_DATASETS:
+            print("Calculating dataset {}".format(dataset))
+
+            operator_id = dataset[0]
+            acronym = dataset[1]
+            kpi_name = dataset[2]
+
+            data_points = DataPoint.objects.filter(operator_id=operator_id, acronym=acronym, kpi_name=kpi_name)
+            for comparator in IF_COMPARATOR:
+                comparator.save_dataset(dataset)
+
+                for test in comparator.get_test_cases():
+                    configuration = test.get_configuration()
+                    detector = OutlierDetector(dataset=parse_result(data_points), feature=configuration['feature'])
+
+                    result_dict = detector.fusion(configuration, algorithms=comparator.get_algorithms())
+                    test.append_results(result_dict)
+
+                comparator.generate_csv()
+
+    draw_plots()
+
+
+def draw_plots():
+    for comparator in IF_COMPARATOR:
+        drawer = PlotDrawer('avg', comparator.get_parameter())
+        drawer.draw()
+
+
+def parse_result(data_set):
+    parsed_result = []
+    for index, data_point in enumerate(data_set):
+        parsed_result.append({
+            'id': index,
+            'value': data_point.value,
+            'date': str(time_uuid.TimeUUID.convert(data_point.date).get_datetime()),
+            'timestamp': time_uuid.TimeUUID.convert(data_point.date).get_timestamp()
+        })
+
+    return parsed_result
diff --git a/ui/.gitignore b/ui/.gitignore
index b7dab5e..6eaa012 100644
--- a/ui/.gitignore
+++ b/ui/.gitignore
@@ -1,2 +1,3 @@
 node_modules
-build
\ No newline at end of file
+build
+test_results
\ No newline at end of file
diff --git a/ui/src/components/App/App.tsx b/ui/src/components/App/App.tsx
index 17d5708..f1a533d 100644
--- a/ui/src/components/App/App.tsx
+++ b/ui/src/components/App/App.tsx
@@ -61,7 +61,7 @@ export class App extends React.Component<{}, AppState> {
 
     try {
       const response = await axios.get('/api/operators');
-      const operators: Array<Operator> = response.data;
+      const operators: Array<Operator> = response.data.filter(operator => operator.has_enough);
       this.setState(state => ({
         ...state,
         operators: this.prepareOptions(operators, 'operator_name', 'operator_id')
@@ -185,8 +185,12 @@ export class App extends React.Component<{}, AppState> {
          <Select
            data={[
              {key: 'sim', value: 'sim'},
-             {key: 'dist', value: 'dist'},
+             {key: 'dist_f', value: 'dist_f'},
+             {key: 'dist_c', value: 'dist_c'},
              {key: 'reg', value: 'reg'},
+             {key: 'isolation_forest', value: 'isolation_forest'},
+             {key: 'random_split', value: 'random_split'},
+             {key: 'fusion', value: 'fusion'},
              ]}
            onChange={(selected) => this.setState(state => ({
              selected: {
@@ -202,7 +206,7 @@ export class App extends React.Component<{}, AppState> {
              {key: 'day', value: 'day'},
              {key: 'weekday', value: 'weekday'},
              {key: 'monthday', value: 'monthday'},
-             {key: 'timestamp', value: 'timestamp'}
+             {key: 'none', value: 'none'}
            ]}
            onChange={(selected) => this.setState(state => ({
              selected: {
diff --git a/ui/src/components/Chart/Chart.tsx b/ui/src/components/Chart/Chart.tsx
index 4e0e303..14c8a91 100644
--- a/ui/src/components/Chart/Chart.tsx
+++ b/ui/src/components/Chart/Chart.tsx
@@ -50,7 +50,7 @@ export class Chart extends React.Component<ChartProps, ChartState> {
     return (
       <div style={style.chart}>
         {this.renderSummary(data)}
-        <XYPlot height={window.innerHeight - 150} width={this.getChartLength() - 300} {...colorProp}>
+        <XYPlot height={window.innerHeight - 150} width={this.getChartLength() - 280} {...colorProp}>
           <XAxis tickLabelAngle={-45} tickFormat={v => v.toExponential()}/>
           <YAxis tickFormat={v => v.toExponential()}/>
           {this.state.showOutlierData
diff --git a/ui/src/components/Chart/style.ts b/ui/src/components/Chart/style.ts
index fde04f4..cfa3c8b 100644
--- a/ui/src/components/Chart/style.ts
+++ b/ui/src/components/Chart/style.ts
@@ -5,6 +5,7 @@ export const style: {[key: string]: React.CSSProperties} = {
     marginTop: 100
   },
   chart: {
+    marginLeft: 0,
     marginTop: 100
   },
   summary: {
@@ -13,7 +14,7 @@ export const style: {[key: string]: React.CSSProperties} = {
     right: 0,
     top: 70,
     bottom: 0,
-    width: 300,
+    width: 280,
     zIndex: 100
   },
   colorBlock: {
diff --git a/ui/src/types/Operator.ts b/ui/src/types/Operator.ts
index 9aa4765..8eee874 100644
--- a/ui/src/types/Operator.ts
+++ b/ui/src/types/Operator.ts
@@ -1,4 +1,5 @@
 export interface Operator {
   operator_id: number;
   operator_name: string;
+  has_enough: boolean;
 }
\ No newline at end of file