Merge pull request #8 from NDCLab/bryan/script

Bryan/script

multi_participants/cong_incong/cong_incong.py

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+import os
+from mind_reading_package import mind_reading as mr
+import pandas as pd
+
+# list all folders' name
+participants = os.listdir('path')
+
+# remove the 'cha' folder we don't need
+participants = participants.remove('cha')
+
+# create the initial dataframe
+df = pd.DataFrame(index=['SVC', 'DTC', 'NB', 'NN'])
+
+for participant in participants:
+    # iterate all the folders
+
+    for file in os.listdir(participant):
+        # iterate all files in every folder, find out the one end with 'Cong.csv' and 'Incong.csv' as input data
+
+        if file.endswith('Cong.csv'):
+            file1 = f"{participant}/{file}"
+        if file.endswith('Incong.csv'):
+            file2 = f"{participant}/{file}"
+
+    # load in cong and incong data for them
+    df1 = mr.load_data(file1)
+    df2 = mr.load_data(file2)
+
+    # concatenate such data
+    data = mr.concatenate_data(df1, df2)
+
+    # find trials to later separate
+    trials_index = mr.find_trials(data)
+
+    # separate trials
+    trials = mr.separate_trials(data, trials_index)
+
+    # create the label column
+    labels = mr.create_ic_labels(data)
+
+    # Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample))
+    pro_trials = mr.process_trials(trials, 250, 550)
+
+    # Find the mean across channels
+    avg_trials = mr.average_trials(pro_trials)
+
+    # concatenates the average trials dataframe with labels
+    ml_df = mr.create_ml_df(avg_trials, labels)
+
+    # train models
+    X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df)
+
+    acc_svc, precision_svc = mr.train_svc(X_train, X_test, y_train, y_test)
+
+    acc_dtc, precision_dtc = mr.train_dtc(X_train, X_test, y_train, y_test)
+
+    acc_nb, precision_nb = mr.train_nb(X_train, X_test, y_train, y_test)
+
+    acc_nn, precision_nn = mr.train_nn(64, X_train, X_test, y_train, y_test)
+
+    # add every participant's accuracy together
+    acc_list = [f"{acc_svc:.2f}", f"{acc_dtc:.2f}",
+                f"{acc_nb:.2f}", f"{acc_nn:.2f}"]
+
+    df = mr.res_df(df, acc_list, participant)
+
+# generate result .csv file
+df.to_csv('cong_incong_accuracy.csv')