Miscellaneous update

Class_Mod/Miscellaneous.py

@@ -7,21 +7,6 @@ def local_css(file_name):
         st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
 local_css("style/style.css")
 
-# Cross-Validation of the model
-def CV_model(estimator, x, y, cv):
-    st.write('Cross-Validation of this model')
-    st.write("CV_scores", cross_val_score(estimator, x, y, cv=cv))
-    st.write("-- CV predict --")
-    Y_preds = cross_val_predict(estimator, x, y, cv=3)
-    st.write("MAE", mean_absolute_error(y, Y_preds))
-    st.write("MSE", mean_squared_error(y, Y_preds))
-    st.write("MAPE", mean_absolute_percentage_error(y, Y_preds))
-    st.write("R²", r2_score(y, Y_preds))
-    st.write("-- Cross Validate --")
-    cv_results = cross_validate(estimator, x, y, cv=cv, return_train_score=True, n_jobs=3)
-    for key in cv_results.keys():
-        st.write(key, cv_results[key])
-
 # predict module
 def prediction(NIRS_csv, qsep, qhdr, model):
     # hdr var correspond to column header True or False in the CSV
@@ -33,3 +18,24 @@ def prediction(NIRS_csv, qsep, qhdr, model):
     Y_preds = model.predict(X_test)
     # Y_preds = X_test
     return Y_preds
+
+
+def reg_plot( meas, pred):
+    fig, ax = plt.subplots(figsize = (12,4))
+    sns.regplot(x = meas[0] , y = pred[0], color='blue', label = 'Calib')
+    sns.regplot(x = meas[1], y = pred[1], color='red', label = 'CV')
+    sns.regplot(x = meas[2], y = pred[2], color='green', label = 'Test')
+    plt.plot([np.min(meas[0])+0.1, np.max([meas[0]])+0.1], [np.min(meas[0])+0.1, np.max([meas[0]])+0.1], color = 'black')
+    ax.set_ylabel('Predicted values')
+    ax.set_xlabel('Measured values')
+    plt.legend()
+    plt.margins(0)
+
+def resid_plot( meas, pred):
+    fig, ax = plt.subplots(figsize = (12,4))
+    sns.residplot(x = meas[0], y = pred[0], color='blue', label = 'Calib')
+    sns.residplot(x = meas[1], y = pred[1], color='red', label = 'CV')
+    sns.residplot(x = meas[2], y = pred[2], color='green', label = 'Test')
+    ax.set_ylabel('Residuals')
+    ax.set_xlabel('Predicted values')
+    plt.legend()
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