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ChatAttachmentAnalysisWithValidation.py

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+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+
+# Read your data file
+datafile_path = "data/chat_transcripts_with_embeddings_and_scores.csv"
+
+df = pd.read_csv(datafile_path)
+
+# Convert embeddings to numpy arrays
+df['embedding'] = df['embedding'].apply(lambda x: [float(num) for num in x.strip('[]').split(',')])
+
+# Split the data into features (X) and labels (y)
+X = list(df.embedding.values)
+y = df[['avoide', 'avoida', 'avoidb', 'avoidc', 'avoidd', 'anxietye', 'anxietya', 'anxietyb', 'anxietyc', 'anxietyd']].values
+
+# Split data into training, validation, and testing sets
+X_train, X_val_test, y_train, y_val_test = train_test_split(X, y, test_size=0.3, random_state=42)
+X_val, X_test, y_val, y_test = train_test_split(X_val_test, y_val_test, test_size=0.5, random_state=42)
+
+# Train your regression model
+rfr = RandomForestRegressor(n_estimators=100)
+rfr.fit(X_train, y_train)
+
+# Make predictions on the validation data and adjust your model parameters accordingly
+val_preds = rfr.predict(X_val)
+val_mse = mean_squared_error(y_val, val_preds)
+val_mae = mean_absolute_error(y_val, val_preds)
+print(f"Validation MSE: {val_mse:.2f}, Validation MAE: {val_mae:.2f}")
+
+# After adjusting your model parameters, make predictions on the test data
+test_preds = rfr.predict(X_test)
+
+# Evaluate your model
+test_mse = mean_squared_error(y_test, test_preds)
+test_mae = mean_absolute_error(y_test, test_preds)
+print(f"Test MSE: {test_mse:.2f}, Test MAE: {test_mae:.2f}")

ChatAttachmentAnalysisXGWithValidation.py

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+import pandas as pd
+import numpy as np
+import xgboost as xgb
+from sklearn.multioutput import MultiOutputRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+
+# Read your data file
+datafile_path = "data/chat_transcripts_with_embeddings_and_scores.csv"
+
+df = pd.read_csv(datafile_path)
+
+# Convert embeddings to numpy arrays
+df['embedding'] = df['embedding'].apply(lambda x: np.array(eval(x)))
+
+# Split the data into features (X) and labels (y)
+X = list(df.embedding.values)
+y = df[['avoide', 'avoida', 'avoidb', 'avoidc', 'avoidd', 'anxietye', 'anxietya', 'anxietyb', 'anxietyc', 'anxietyd']].values
+
+# Split data into training, validation, and testing sets
+X_train, X_val_test, y_train, y_val_test = train_test_split(X, y, test_size=0.3, random_state=42)
+X_val, X_test, y_val, y_test = train_test_split(X_val_test, y_val_test, test_size=0.5, random_state=42)
+
+# Train your regression model
+xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1, max_depth = 5, alpha = 10, n_estimators = 10)
+multioutput_reg = MultiOutputRegressor(xg_reg)
+multioutput_reg.fit(np.array(X_train).tolist(), y_train)
+
+# Make predictions on the validation data and tune your model parameters accordingly
+val_preds = multioutput_reg.predict(np.array(X_val).tolist())
+val_mse = mean_squared_error(y_val, val_preds)
+val_mae = mean_absolute_error(y_val, val_preds)
+print(f"Validation MSE: {val_mse:.2f}, Validation MAE: {val_mae:.2f}")
+
+# After tuning your model, make predictions on the test data
+test_preds = multioutput_reg.predict(np.array(X_test).tolist())
+
+# Evaluate your model
+test_mse = mean_squared_error(y_test, test_preds)
+test_mae = mean_absolute_error(y_test, test_preds)
+print(f"Test MSE: {test_mse:.2f}, Test MAE: {test_mae:.2f}")