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

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Reviewed-on: https://codefirst.iut.uca.fr/git/clement.freville2/miner/pulls/14
This commit is contained in:
Bastien OLLIER
2024-06-26 11:12:17 +02:00
parent 9da6e2d594 9bc9e21e45
commit 86bd285193
2 changed files with 50 additions and 1 deletions
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17
frontend/pages/prediction_classification.py
View File

@@ -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")

34
frontend/pages/prediction_regression.py
View File

@@ -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")