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gmm_health.py
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gmm_health.py
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# Import Modules:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold, RepeatedStratifiedKFold, GridSearchCV, train_test_split
from sklearn.metrics import accuracy_score
import pandas as pd
from sklearn.mixture import GaussianMixture as GMM
#-------------------define functions--------------------------------
# Plot Functions:
def plot_data_2d(X, Y, d1_idx, d2_idx, colors, title):
n_classes = len(np.unique(Y))
fig, ax = plt.subplots()
for i in range(n_classes):
plt.scatter(X[:, d1_idx][np.where(Y == i)[0]],
X[:, d2_idx][np.where(Y == i)[0]],
c=colors[i])
ax.set_ylabel('Feature 1')
ax.set_xlabel('Feature 2')
ax.set_xticks(())
ax.set_yticks(())
ax.set_title(title)
plt.show()
# GridSearch Functions:
def MyGridSearchCV_GMM(X, Y, n_components_range, folds, times):
results = {}
mean_train_accuracies_k_time = []
mean_test_accuracies_k_time = []
for n, k in enumerate(n_components_range):
gmm_model = GMM(n_components=k)
mean_train_accuracies_k_fold = []
mean_test_accuracies_k_fold = []
std_train_accuracies_k_fold = []
std_test_accuracies_k_fold = []
for i in range(times):
kf = KFold(n_splits=folds, random_state=None, shuffle=True)
train_accuracies_per_fold = []
test_accuracies_per_fold = []
for x, y in kf.split(range(np.shape(X)[0])):
x_train, y_train = X[x], Y[x]
x_test, y_test = X[y], Y[y]
models = []
Pb_train = np.zeros((np.shape(x_train)[0], n_classes))
Pb_test = np.zeros((np.shape(x_test)[0], n_classes))
for i in range(n_classes):
models.append(gmm_model.fit(x_train[np.where(y_train == i)[0]]))
Pb_train[:, i] = models[i].score_samples(x_train)
Pb_test[:, i] = models[i].score_samples(x_test)
y_pred_train = np.argmax(Pb_train, axis=1)
y_pred_test = np.argmax(Pb_test, axis=1)
train_accuracies_per_fold.append(accuracy_score(y_train, y_pred_train))
test_accuracies_per_fold.append(accuracy_score(y_test, y_pred_test))
mean_train_accuracies_k_fold.append(np.mean(train_accuracies_per_fold))
mean_test_accuracies_k_fold.append(np.mean(test_accuracies_per_fold))
std_train_accuracies_k_fold.append(np.std(train_accuracies_per_fold))
std_test_accuracies_k_fold.append(np.std(test_accuracies_per_fold))
mean_train_accuracies_k_time.append(np.max(mean_train_accuracies_k_fold))
mean_test_accuracies_k_time.append(np.max(mean_test_accuracies_k_fold))
results[str(k)]={'train_acc': round(mean_train_accuracies_k_time[n], 2),
'test_acc': round(mean_test_accuracies_k_time[n], 2)}
best_k = n_components_range[np.argmax(mean_test_accuracies_k_time)]
return results, best_k, GMM(n_components=best_k)
# Load & Prepare Dataset:
def load_dataset(path):
df = pd.read_table(path, delimiter=' ', header=None)
X = df.drop(df.columns[13], axis=1)
Y = np.array(df.drop(df.columns[[0,1,2,3,4,5,6,7,8,9,10,11,12]], axis=1))[:, 0]
scaler = StandardScaler()
X = scaler.fit_transform(X)
Y = Y - 1
return X, Y
#-------------------call functions--------------------------------
# Configure Parameters:
data_path = "./Health.dat"
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
covariance_type_range = ['full']# ['spherical', 'tied', 'diag', 'full']
n_components_range = [1,5,10]
param_grid = dict(n_components=n_components_range, covariance_type=covariance_type_range)
# Load & Plot Dataset:
X, Y = load_dataset(data_path)
n_classes = len(np.unique(Y))
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
s1, s2 = 3, 4 # selected features to plot
plot_data_2d(X_train, Y_train, s1, s2, colors, 'plot-2d (train data)')
plot_data_2d(X_test, Y_test, s1, s2, colors, 'plot-2d (test data)')
for k in n_components_range:
gmm_model = GMM(n_components=k)
models = []
Pb_test = np.zeros((np.shape(X_test)[0], n_classes))
for i in range(n_classes):
models.append(gmm_model.fit(X_train[np.where(Y_train == i)[0]]))
Pb_test[:, i] = models[i].score_samples(X_test)
Y_pred_test = np.argmax(Pb_test, axis=1)
plot_data_2d(X_test, Y_pred_test, s1, s2, colors, 'plot-2d (predict test data & k = ' + str(k) + ')')
## Find Best K for GMM (k-time-k-fold cv):
times = 5
folds = 5
results, best_k, best_gmm = MyGridSearchCV_GMM(X, Y, n_components_range, folds, times)
print("The best n component is: ", best_k)
print(results)