Provide data instead of csv input.

.idea/codeStyles/Project.xml

@@ -0,0 +1,6 @@
+<component name="ProjectCodeStyleConfiguration">
+  <code_scheme name="Project" version="173">
+    <option name="RIGHT_MARGIN" value="150" />
+    <option name="SOFT_MARGINS" value="88" />
+  </code_scheme>
+</component>

.idea/codeStyles/codeStyleConfig.xml

@@ -0,0 +1,5 @@
+<component name="ProjectCodeStyleConfiguration">
+  <state>
+    <option name="USE_PER_PROJECT_SETTINGS" value="true" />
+  </state>
+</component>

machine_learning/helper.py

@@ -136,14 +136,11 @@ def prepare_regression_model_input(model_input, cv_method="logo"):
     return train_x, data_y, data_groups
 
 
-def run_all_regression_models(input_csv):
-    # Prepare data
-    data_x, data_y, data_groups = prepare_regression_model_input(input_csv)
-
+def run_all_regression_models(train_x, data_y, data_groups):
     # Prepare cross validation
     logo = LeaveOneGroupOut()
     logo.get_n_splits(
-        data_x,
+        train_x,
         data_y,
         groups=data_groups,
     )
@@ -155,7 +152,7 @@ def run_all_regression_models(input_csv):
     dummy_regr = DummyRegressor(strategy="mean")
     dummy_regr_scores = cross_validate(
         dummy_regr,
-        X=data_x,
+        X=train_x,
         y=data_y,
         groups=data_groups,
         cv=logo,
@@ -173,7 +170,7 @@ def run_all_regression_models(input_csv):
     lin_reg_rapids = linear_model.LinearRegression()
     lin_reg_scores = cross_validate(
         lin_reg_rapids,
-        X=data_x,
+        X=train_x,
         y=data_y,
         groups=data_groups,
         cv=logo,
@@ -191,7 +188,7 @@ def run_all_regression_models(input_csv):
     ridge_reg = linear_model.Ridge(alpha=0.5)
     ridge_reg_scores = cross_validate(
         ridge_reg,
-        X=data_x,
+        X=train_x,
         y=data_y,
         groups=data_groups,
         cv=logo,
@@ -208,7 +205,7 @@ def run_all_regression_models(input_csv):
     lasso_reg = linear_model.Lasso(alpha=0.1)
     lasso_reg_score = cross_validate(
         lasso_reg,
-        X=data_x,
+        X=train_x,
         y=data_y,
         groups=data_groups,
         cv=logo,
@@ -225,7 +222,7 @@ def run_all_regression_models(input_csv):
     bayesian_ridge_reg = linear_model.BayesianRidge()
     bayesian_ridge_reg_score = cross_validate(
         bayesian_ridge_reg,
-        X=data_x,
+        X=train_x,
         y=data_y,
         groups=data_groups,
         cv=logo,
@@ -242,7 +239,7 @@ def run_all_regression_models(input_csv):
     ransac_reg = linear_model.RANSACRegressor()
     ransac_reg_score = cross_validate(
         ransac_reg,
-        X=data_x,
+        X=train_x,
         y=data_y,
         groups=data_groups,
         cv=logo,
@@ -258,7 +255,13 @@ def run_all_regression_models(input_csv):
 
     svr = svm.SVR()
     svr_score = cross_validate(
-        svr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
+        svr,
+        X=train_x,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring=metrics,
     )
     print("Support vector regression")
 
@@ -270,7 +273,7 @@ def run_all_regression_models(input_csv):
     kridge = kernel_ridge.KernelRidge()
     kridge_score = cross_validate(
         kridge,
-        X=data_x,
+        X=train_x,
         y=data_y,
         groups=data_groups,
         cv=logo,
@@ -286,7 +289,13 @@ def run_all_regression_models(input_csv):
 
     gpr = gaussian_process.GaussianProcessRegressor()
     gpr_score = cross_validate(
-        gpr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
+        gpr,
+        X=train_x,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring=metrics,
     )
     print("Gaussian Process Regression")
 
@@ -297,7 +306,13 @@ def run_all_regression_models(input_csv):
 
     rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
     rfr_score = cross_validate(
-        rfr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
+        rfr,
+        X=train_x,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring=metrics,
     )
     print("Random Forest Regression")
 
@@ -308,7 +323,13 @@ def run_all_regression_models(input_csv):
 
     xgb = XGBRegressor()
     xgb_score = cross_validate(
-        xgb, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
+        xgb,
+        X=train_x,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring=metrics,
     )
     print("XGBoost Regressor")
 
@@ -319,7 +340,13 @@ def run_all_regression_models(input_csv):
 
     ada = ensemble.AdaBoostRegressor()
     ada_score = cross_validate(
-        ada, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
+        ada,
+        X=train_x,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring=metrics,
     )
     print("ADA Boost Regressor")