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Improved Error Handling #381

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Oct 6, 2024
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Improved Error Handling #381

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266 changes: 135 additions & 131 deletions opensource_analysis/app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,134 +18,138 @@
if not os.path.exists(file_path):
st.error(f"File not found: {file_path}. Please ensure the file is in the correct directory.")
else:
# Load the dataset
data = pd.read_csv(file_path)

# Define the necessary columns
columns = ['Employment', 'FormalEducation', 'CompanySize', 'DevType', 'Exercise', 'Age', 'OpenSource']
data = data[columns].copy()

# Map age values to numerical values
age_mapping = {
'Under 18 years old': 0,
'18 - 24 years old': 1,
'25 - 34 years old': 2,
'35 - 44 years old': 3,
'45 - 54 years old': 4,
'55 - 64 years old': 5,
'65 years or older': 6
}
data['Age'] = data['Age'].map(age_mapping)

# Define target variable and feature columns
target_variable = 'OpenSource'
categorical_features = ['Employment', 'FormalEducation', 'CompanySize', 'DevType', 'Exercise', 'Age']
numerical_features = []

# Preprocessing for categorical data
preprocessor = ColumnTransformer(
transformers=[
('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)
]
)

# Split the data
X = data.drop(target_variable, axis=1)
y = data[target_variable]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the model
model = Pipeline(steps=[
('preprocessor', preprocessor),
('classifier', RandomForestClassifier(random_state=42))
])
model.fit(X_train, y_train)

# Evaluate the model
y_pred = model.predict(X_test)
classification_rep = classification_report(y_test, y_pred)
roc_auc = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])

# Get feature importance
importances = model.named_steps['classifier'].feature_importances_
feature_names = list(model.named_steps['preprocessor'].transformers_[0][1].get_feature_names_out())
feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances}).sort_values(by='Importance', ascending=False)

# Streamlit App
st.title('Machine Learning Model Evaluation')

# Show classification report
st.header('Classification Report')
st.text(classification_rep)

# Show ROC-AUC Score
st.header('ROC-AUC Score')
st.text(f"ROC-AUC Score: {roc_auc:.2f}")

# Plot confusion matrix
st.header('Confusion Matrix')
cm = confusion_matrix(y_test, y_pred)
fig, ax = plt.subplots()
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['No', 'Yes'], yticklabels=['No', 'Yes'], ax=ax)
plt.xlabel('Predicted')
plt.ylabel('Actual')
st.pyplot(fig)

# Plot ROC Curve
st.header('ROC Curve')
y_test_binary = y_test.map({'No': 0, 'Yes': 1})
fpr, tpr, _ = roc_curve(y_test_binary, model.predict_proba(X_test)[:, 1])
roc_auc = auc(fpr, tpr)
fig, ax = plt.subplots()
ax.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
ax.set_xlim([0.0, 1.0])
ax.set_ylim([0.0, 1.05])
ax.set_xlabel('False Positive Rate')
ax.set_ylabel('True Positive Rate')
ax.set_title('ROC Curve')
ax.legend(loc='lower right')
st.pyplot(fig)

# Plot feature importance
st.header('Feature Importance')
fig, ax = plt.subplots()
sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(20), palette='viridis', ax=ax)
ax.set_title('Top Feature Importances')
ax.set_xlabel('Importance')
ax.set_ylabel('Feature')
st.pyplot(fig)

# Section for new data input and prediction
st.header('Predict for New Data')

# Input fields for new data
employment = st.selectbox('Employment', data['Employment'].unique())
education = st.selectbox('Formal Education', data['FormalEducation'].unique())
company_size = st.selectbox('Company Size', data['CompanySize'].unique())
dev_type = st.selectbox('Dev Type', data['DevType'].unique())
exercise = st.selectbox('Exercise', data['Exercise'].unique())
age = st.selectbox('Age', list(age_mapping.keys()))

# Convert inputs to dataframe
new_data = pd.DataFrame({
'Employment': [employment],
'FormalEducation': [education],
'CompanySize': [company_size],
'DevType': [dev_type],
'Exercise': [exercise],
'Age': [age_mapping[age]]
})

# Handle any NaN values
new_data = new_data.fillna('')

# Predict the output for new data
if st.button('Predict'):
try:
prediction = model.predict(new_data)
prediction_prob = model.predict_proba(new_data)[:, 1]
st.write(f'Prediction: {"Yes" if prediction[0] == "Yes" else "No"}')
st.write(f'Prediction Probability: {prediction_prob[0]:.2f}')
except Exception as e:
st.error(f"An error occurred during prediction: {e}")
try:
# Try to load the dataset
data = pd.read_csv(file_path)

# Define the necessary columns
columns = ['Employment', 'FormalEducation', 'CompanySize', 'DevType', 'Exercise', 'Age', 'OpenSource']
data = data[columns].copy()

# Map age values to numerical values
age_mapping = {
'Under 18 years old': 0,
'18 - 24 years old': 1,
'25 - 34 years old': 2,
'35 - 44 years old': 3,
'45 - 54 years old': 4,
'55 - 64 years old': 5,
'65 years or older': 6
}
data['Age'] = data['Age'].map(age_mapping)

# Define target variable and feature columns
target_variable = 'OpenSource'
categorical_features = ['Employment', 'FormalEducation', 'CompanySize', 'DevType', 'Exercise', 'Age']
numerical_features = []

# Preprocessing for categorical data
preprocessor = ColumnTransformer(
transformers=[
('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)
]
)

# Split the data
X = data.drop(target_variable, axis=1)
y = data[target_variable]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the model
model = Pipeline(steps=[
('preprocessor', preprocessor),
('classifier', RandomForestClassifier(random_state=42))
])
model.fit(X_train, y_train)

# Evaluate the model
y_pred = model.predict(X_test)
classification_rep = classification_report(y_test, y_pred)
roc_auc = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])

# Get feature importance
importances = model.named_steps['classifier'].feature_importances_
feature_names = list(model.named_steps['preprocessor'].transformers_[0][1].get_feature_names_out())
feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances}).sort_values(by='Importance', ascending=False)

# Streamlit App
st.title('Machine Learning Model Evaluation')

# Show classification report
st.header('Classification Report')
st.text(classification_rep)

# Show ROC-AUC Score
st.header('ROC-AUC Score')
st.text(f"ROC-AUC Score: {roc_auc:.2f}")

# Plot confusion matrix
st.header('Confusion Matrix')
cm = confusion_matrix(y_test, y_pred)
fig, ax = plt.subplots()
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['No', 'Yes'], yticklabels=['No', 'Yes'], ax=ax)
plt.xlabel('Predicted')
plt.ylabel('Actual')
st.pyplot(fig)

# Plot ROC Curve
st.header('ROC Curve')
y_test_binary = y_test.map({'No': 0, 'Yes': 1})
fpr, tpr, _ = roc_curve(y_test_binary, model.predict_proba(X_test)[:, 1])
roc_auc = auc(fpr, tpr)
fig, ax = plt.subplots()
ax.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
ax.set_xlim([0.0, 1.0])
ax.set_ylim([0.0, 1.05])
ax.set_xlabel('False Positive Rate')
ax.set_ylabel('True Positive Rate')
ax.set_title('ROC Curve')
ax.legend(loc='lower right')
st.pyplot(fig)

# Plot feature importance
st.header('Feature Importance')
fig, ax = plt.subplots()
sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(20), palette='viridis', ax=ax)
ax.set_title('Top Feature Importances')
ax.set_xlabel('Importance')
ax.set_ylabel('Feature')
st.pyplot(fig)

# Section for new data input and prediction
st.header('Predict for New Data')

# Input fields for new data
employment = st.selectbox('Employment', data['Employment'].unique())
education = st.selectbox('Formal Education', data['FormalEducation'].unique())
company_size = st.selectbox('Company Size', data['CompanySize'].unique())
dev_type = st.selectbox('Dev Type', data['DevType'].unique())
exercise = st.selectbox('Exercise', data['Exercise'].unique())
age = st.selectbox('Age', list(age_mapping.keys()))

# Convert inputs to dataframe
new_data = pd.DataFrame({
'Employment': [employment],
'FormalEducation': [education],
'CompanySize': [company_size],
'DevType': [dev_type],
'Exercise': [exercise],
'Age': [age_mapping[age]]
})

# Handle any NaN values
new_data = new_data.fillna('')

# Predict the output for new data
if st.button('Predict'):
try:
prediction = model.predict(new_data)
prediction_prob = model.predict_proba(new_data)[:, 1]
st.write(f'Prediction: {"Yes" if prediction[0] == "Yes" else "No"}')
st.write(f'Prediction Probability: {prediction_prob[0]:.2f}')
except Exception as e:
st.error(f"An error occurred during prediction: {e}")

except Exception as e:
st.error(f"An error occurred while loading data: {e}")