Merge pull request 'stat_prediction' (#14) from stat_prediction into main

Reviewed-on: #14

frontend/pages/prediction_classification.py

@@ -1,9 +1,11 @@
 import streamlit as st
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
+from sklearn.metrics import accuracy_score,confusion_matrix
 from sklearn.preprocessing import LabelEncoder
 import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
 
 st.header("Prediction: Classification")
 
@@ -60,5 +62,18 @@ if "data" in st.session_state:
             prediction = label_encoders[target_name].inverse_transform(prediction)
         
         st.write("Prediction:", prediction[0])
+
+        if len(data_name) == 1:
+            fig = plt.figure()
+
+            y_pred = [model.predict(pd.DataFrame([pred_value[0]], columns=data_name)) for pred_value in X.values.tolist()]
+            cm = confusion_matrix(y, y_pred)
+
+            sns.heatmap(cm, annot=True, fmt="d")
+
+            plt.xlabel('Predicted')
+            plt.ylabel('True')
+
+            st.pyplot(fig, figsize=(1, 1))
 else:
     st.error("File not loaded")

frontend/pages/prediction_regression.py

@@ -1,6 +1,8 @@
 import streamlit as st
 from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score
 import pandas as pd
+import matplotlib.pyplot as plt
 
 st.header("Prediction: Regression")
 
@@ -25,5 +27,37 @@ if "data" in st.session_state:
         prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
 
         st.write("Prediction:", prediction[0])
+
+        fig = plt.figure()
+        dataframe_sorted = pd.concat([X, y], axis=1).sort_values(by=data_name)
+
+        if len(data_name) == 1:
+            y_pred = [model.predict(pd.DataFrame([pred_value[0]], columns=data_name)) for pred_value in X.values.tolist()]
+            r2 = r2_score(y, y_pred)
+            st.write('R-squared score:', r2)
+
+            X = dataframe_sorted[data_name[0]]
+            y = dataframe_sorted[target_name]
+
+            prediction_array_y = [
+                model.predict(pd.DataFrame([[dataframe_sorted[data_name[0]].iloc[i]]], columns=data_name))[0]
+                for i in range(dataframe_sorted.shape[0])
+            ]
+
+            plt.scatter(dataframe_sorted[data_name[0]], dataframe_sorted[target_name], color='b')
+            plt.plot(dataframe_sorted[data_name[0]], prediction_array_y, color='r')
+        elif len(data_name) == 2:
+            ax = fig.add_subplot(111, projection='3d')
+
+            prediction_array_y = [
+                model.predict(pd.DataFrame([[dataframe_sorted[data_name[0]].iloc[i], dataframe_sorted[data_name[1]].iloc[i]]], columns=data_name))[0]
+                for i in range(dataframe_sorted.shape[0])
+            ]
+
+            ax.scatter(dataframe_sorted[data_name[0]], dataframe_sorted[data_name[1]], dataframe_sorted[target_name], color='b')
+            ax.plot(dataframe_sorted[data_name[0]], dataframe_sorted[data_name[1]], prediction_array_y, color='r')
+
+        st.pyplot(fig)
+
 else:
     st.error("File not loaded")