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Last active December 22, 2020 03:41
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import sklearn
if (sklearn.__version__ != '0.23.2'):
raise Exception("scikit-learn package version must be 0.23.2")
import os
import numpy as np
from import loadmat
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
for datanum in np.arange(8):
# Load .mat data
BASE_PATH = r'C:\Users\knowb\SynologyDrive\20JUN'
datalist = os.listdir(BASE_PATH)
data = loadmat(os.path.join(BASE_PATH, datalist[datanum]))
print(datalist[datanum] + ' is loaded \n')
X = data.get('X')
Y = data.get('y')
Y = np.squeeze(Y)
Y_label = ['Head Entry', 'Avoidance', 'Escape']
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.5, stratify=Y)
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
from sklearn.naive_bayes import BernoulliNB
from sklearn.naive_bayes import CategoricalNB
param_grid = {'C' : np.linspace(1,3,21), 'kernel': ['poly','rbf'], 'gamma' : ['auto','scale'], 'tol' : [1e-3, 1e-4, 1e-5]}
#param_grid = {'alpha' : np.linspace(0, 1, 11)}
scores = ['accuracy','precision', 'recall']
## Parameter search
print('Hyper parameter tuning for accuracy')
search = GridSearchCV(SVC(), iid=False, param_grid=param_grid, cv=3, n_jobs=-1, scoring='balanced_accuracy')
#search = GridSearchCV(CategoricalNB(), param_grid=param_grid, cv=5, n_jobs=-1, scoring='balanced_accuracy'), Y_train)
print("Grid scores on development set:")
means = search.cv_results_['mean_test_score']
stds = search.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, search.cv_results_['params']):
print("%0.3f (+/-%0.03f) for %r"
% (mean, std * 2, params))
print("Detailed classification report:")
print("The model is trained on the full development set.")
print("The scores are computed on the full evaluation set.")
Y_true, Y_pred = Y_test, search.predict(X_test)
print(classification_report(Y_true, Y_pred))
print('Best parameter')
print("%d %d %d" %(np.sum(Y_test == 1), np.sum(Y_test ==2), np.sum(Y_test ==3)))
# Classification Result
confusion_mat = confusion_matrix(Y_true, Y_pred,normalize='true') # row is actual. # column is predicted
cmap = sns.cubehelix_palette(start=.5, rot=-.5, as_cmap=True)
f, ax = plt.subplots(figsize=(11, 9))
sns.heatmap(confusion_mat, cmap=cmap, vmin=0, vmax=1, annot=True, square=True, linewidths=.5, cbar_kws={"shrink": .5}, xticklabels=Y_label, yticklabels=Y_label)
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