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test2.py

+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+
+# Load the training data
+train_data = pd.read_csv("/Users/rebecca_dxy/Downloads/Machine/TrainingDataMulti.csv")
+
+# Split the features and labels
+X_train = train_data.iloc[:, :-1]
+y_train = train_data.iloc[:, -1]
+
+# Get the column names of the training data
+feature_names = X_train.columns
+
+# Create a Random Forest classifier
+rf_classifier = RandomForestClassifier()
+
+# Train the classifier
+rf_classifier.fit(X_train, y_train)
+
+# Load the testing data
+test_data = pd.read_csv("/Users/rebecca_dxy/Downloads/Machine/TestingDataMulti.csv")
+
+# Set the column names of the testing data to match the training data
+test_data.columns = feature_names
+
+# Predict labels for the testing data
+y_pred = rf_classifier.predict(test_data)
+
+# Create a DataFrame with the computed labels for testing data
+testing_results = pd.DataFrame(y_pred, columns=["Label"])
+
+# Compute predictions on the training data
+y_train_pred = rf_classifier.predict(X_train)
+
+# Calculate training error and accuracy
+training_error = 1 - accuracy_score(y_train, y_train_pred)
+training_accuracy = accuracy_score(y_train, y_train_pred)
+
+# Print the training error and accuracy
+print("Training Error:", training_error)
+print("Training Accuracy:", training_accuracy)
+
+
+# Show the computed labels for all testing data
+print("Computed Labels for Testing Data:")
+print(testing_results)
+
+# Show the computed labels for all testing data
+for index, row in testing_results.iterrows():
+    print(f"Computed Label for Trace {index+1}: {row['Label']}")
+
+# Save the computed labels to a file
+testing_results.to_csv("TestingResultsMulti.csv", index=False)
+
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