cma0232 · Jan 28, 2021 · Jan 29, 2021 · Jan 29, 2021 · Feb 9, 2021 · Feb 9, 2021
diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
@@ -0,0 +1,30 @@
+name: Pylint
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.5, 3.6, 3.7, 3.8]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+       flake8 . --ignore=E501,W503,W504
diff --git a/multi_participants/.idea/inspectionProfiles/profiles_settings.xml b/multi_participants/.idea/inspectionProfiles/profiles_settings.xml
diff --git a/multi_participants/.idea/misc.xml b/multi_participants/.idea/misc.xml
diff --git a/multi_participants/.idea/modules.xml b/multi_participants/.idea/modules.xml
diff --git a/multi_participants/.idea/multi_participants.iml b/multi_participants/.idea/multi_participants.iml
diff --git a/multi_participants/.idea/workspace.xml b/multi_participants/.idea/workspace.xml
diff --git a/multi_participants/Graphing functions (2).py b/multi_participants/Graphing functions (2).py
@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from matplotlib import pyplot as plt 
+from matplotlib import rc
+import pylab as plb
+import pandas as pd
+import numpy as np
+
+
+# In[2]:
+
+
+data = pd.read_csv('/Users/kf/Downloads/case_4_accuracy.csv')
+
+
+# In[3]:
+
+
+standErr = np.std(data, ddof=1, axis=1) / np.sqrt(np.size(data, axis =1))
+
+
+# In[4]:
+
+
+data['mean'] = data.mean(axis=1)
+
+
+# In[15]:
+
+
+plt.figure(figsize=(7,8)) 
+plt.errorbar( data['Unnamed: 0'], data['mean'], yerr=standErr, fmt='o', color='Black', elinewidth=1,capthick=1,errorevery=1, alpha=1, ms=1, capsize = 3)
+plt.bar(data['Unnamed: 0'], data['mean'],tick_label = data['Unnamed: 0'], color = ['mediumpurple', 'cyan', 'b','r'],edgecolor='black')##Bar plot
+plt.yticks(rotation = 90)
+plt.xticks(size = 12)
+plt.yticks(size = 12)
+plt.xlabel("Classifier Type", size = 15)
+plt.ylabel("Accuracy", size = 15)
+ax = plt.gca()
+yticks = ax.yaxis.get_major_ticks() 
+yticks[0].label1.set_visible(False)
+plt.show()
+
+
+# In[ ]:
+
+
+
+
diff --git a/multi_participants/Graphing functions (3).py b/multi_participants/Graphing functions (3).py
@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+from matplotlib import pyplot as plt 
+from matplotlib import rc
+import pylab as plb
+import pandas as pd
+import numpy as np
+
+
+# In[2]:
+
+
+data = pd.read_csv('/Users/kf/Downloads/case_4_accuracy.csv')
+
+
+# In[3]:
+
+
+standErr = np.std(data, ddof=1, axis=1) / np.sqrt(np.size(data, axis =1))
+
+
+# In[4]:
+
+
+data['mean'] = data.mean(axis=1)
+
+
+# In[16]:
+
+
+plt.figure(figsize=(7,8)) 
+plt.errorbar( data['Unnamed: 0'], data['mean'], yerr=standErr, fmt='o', color='Black', elinewidth=1,capthick=1,errorevery=1, alpha=1, ms=1, capsize = 3)
+plt.bar(data['Unnamed: 0'], data['mean'],tick_label = data['Unnamed: 0'], color = ['mediumpurple', 'cyan', 'b','r'],edgecolor='black')##Bar plot
+plt.yticks(rotation = 90)
+plt.xticks(size = 12)
+plt.yticks(size = 12)
+plt.xlabel("Classifier Type", size = 15)
+plt.ylabel("Precision", size = 15)
+ax = plt.gca()
+yticks = ax.yaxis.get_major_ticks() 
+yticks[0].label1.set_visible(False)
+plt.show()
+
+
+# In[ ]:
+
+
+
+
diff --git a/multi_participants/case_3/case_3_accuracy.py b/multi_participants/case_3/case_3_accuracy.py
@@ -0,0 +1,65 @@
+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_binary_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)
+
+    # 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('case_3_accuracy.csv') 
diff --git a/multi_participants/case_3/case_3_precision.py b/multi_participants/case_3/case_3_precision.py
@@ -0,0 +1,66 @@
+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_binary_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)
+
+    # 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 precision together
+    precision_list = [f"{precision_svc:.2f}", f"{precision_dtc:.2f}", f"{precision_nb:.2f}", f"{precision_nn:.2f}"]
+
+    df = mr.res_df(df, precision_list, participant)
+
+# generate result .csv file
+df.to_csv('case_3_accuracy.csv')
+