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add confusion_matrix

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bastien ollier
2024-06-26 10:45:50 +02:00
parent 4054395641
commit 27e69b2af8
2 changed files with 12 additions and 16 deletions
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26
frontend/pages/prediction_classification.py
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@@ -1,11 +1,11 @@
import streamlit as st import streamlit as st
from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split 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 from sklearn.preprocessing import LabelEncoder
import pandas as pd import pandas as pd
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import seaborn as sns
st.header("Prediction: Classification") st.header("Prediction: Classification")
@@ -63,24 +63,20 @@ if "data" in st.session_state:
st.write("Prediction:", prediction[0]) 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()]
print([x[0] for x in X.values.tolist()])
cm = confusion_matrix(y, y_pred)
sns.heatmap(cm, annot=True, fmt="d")
fig = plt.figure() plt.xlabel('Predicted')
dataframe_sorted = pd.concat([X, y], axis=1).sort_values(by=data_name) plt.ylabel('True')
X = dataframe_sorted[data_name[0]] st.pyplot(fig)
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.scatter(dataframe_sorted[data_name[0]], prediction_array_y, color='r')
st.pyplot(fig)
else: else:

2
frontend/pages/prediction_regression.py
View File

@@ -2,7 +2,6 @@ import streamlit as st
from sklearn.linear_model import LinearRegression from sklearn.linear_model import LinearRegression
import pandas as pd import pandas as pd
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np
st.header("Prediction: Regression") st.header("Prediction: Regression")
@@ -31,6 +30,7 @@ if "data" in st.session_state:
fig = plt.figure() fig = plt.figure()
dataframe_sorted = pd.concat([X, y], axis=1).sort_values(by=data_name) dataframe_sorted = pd.concat([X, y], axis=1).sort_values(by=data_name)
if len(data_name) == 1: if len(data_name) == 1:
X = dataframe_sorted[data_name[0]] X = dataframe_sorted[data_name[0]]
y = dataframe_sorted[target_name] y = dataframe_sorted[target_name]