def test(classes=2):
data, labels = DataGenerator.blobs_2d(100, classes)
labels = labels.ravel()
fig = plt.figure(constrained_layout=True)
gs = fig.add_gridspec(2, 2)
N = 1000
axis = (0, 1, 0, 1)
data_distr = add_plot_data_2d(fig, gs[0, 0], data, labels,
"Data distribution", axis)
xs = np.random.rand(N, 2)
bc = GaussianBayesClassifier()
bc.fit(data, labels)
res = add_plot_model_predictions(fig, gs[0, 1], xs, bc, "Predictions",
axis, True)
sk_bc = naive_bayes.GaussianNB()
sk_bc.fit(data, labels)
sk_res = add_plot_model_predictions(fig, gs[1, 1], xs, sk_bc,
"sklearn predictions", axis, True)
plt.show()
def test():
data, labels = DataGenerator.blobs_2d(1000, 3)
trainX, trainY, testX, testY = split_data(data, labels)
fig = plt.figure(constrained_layout=True)
gs = fig.add_gridspec(2, 3)
N = 1000
axis = (0, 1, 0, 1)
dtc = DecisionTreeClassifier(10)
dtc.fit(trainX, trainY)
sk_dtc = DTClassifier(max_depth=10)
sk_dtc.fit(trainX, trainY)
data_distr = add_plot_data_2d(fig, gs[0, 0], data, labels,
"Data distributions", axis)
#xs = np.random.rand(N,2)
res = add_plot_model_predictions(fig, gs[0, 1], testX, dtc, "Predictions",
axis, True)
sk_res = add_plot_model_predictions(fig, gs[1, 1], testX, sk_dtc,
"sklearn predictions", axis, True)
add_plot_with_roc_curve(fig, gs[0, 2], testY, dtc.predict(testX))
add_plot_with_roc_curve(fig, gs[1, 2], testY, sk_dtc.predict(testX))
plt.show()
def test(k_cnt, classes=2):
data, labels = DataGenerator.blobs_2d(100,classes)
labels = labels.ravel()
fig = plt.figure(constrained_layout=True)
gs = fig.add_gridspec(2,1+k_cnt)
N = 1000
axis = (0,1,0,1)
data_distr = add_plot_data_2d(fig, gs[0,0], data, labels, "Data distribution", axis)
xs = np.random.rand(N,2)
for i in range(k_cnt):
k = (i+1)*2
knn = KNNClassifier(k)
knn.fit(data, labels)
res = add_plot_model_predictions(fig, gs[0,i+1], xs, knn, "Predictions with k = ", axis, True)
sk_knn = KNeighborsClassifier(n_neighbors=k)
sk_knn.fit(data, labels)
sk_res = res = add_plot_model_predictions(fig, gs[1,i+1], xs, sk_knn, "sklearn predictions with k = " + str(k), axis, True)
plt.show()
def test():
fig = plt.figure(constrained_layout=True)
gs = fig.add_gridspec(2, 3)
data, labels = DataGenerator.blobs_2d(100, 2)
lg = LogisticRegression(learning_rate=0.1, iterations=1000)
lg.fit(data, labels)
sk_lg = LGR()
sk_lg.fit(data, labels)
N = 5000
xs = np.random.rand(N, 2)
axis = (0, 1, 0, 1)
data_distrib = add_plot_data_2d(fig, gs[0, 0], data, labels,
"Data distribution", axis)
res = add_plot_model_predictions(fig, gs[0, 1], xs, lg, "Predictions",
axis)
sk_res = add_plot_model_predictions(fig, gs[0, 2], xs, sk_lg,
"sklearn predictions", axis)
ys = lg.predict(xs, True)
smooth = add_plot_data_2d(fig, gs[1, 1], xs, ys, "Smooth predictions",
axis)
ys = sk_lg.predict_proba(xs)
ys = np.array([max(1 - y[0], y[1]) for y in ys])
sk_smooth = add_plot_data_2d(fig, gs[1, 2], xs, ys,
"sklearn smooth predictions", axis)
error = add_plot_with_error_distrib(fig, gs[1, 0], lg.history)
plt.show()
def test():
data, labels = DataGenerator.blobs_2d(100,5)
fig = plt.figure(constrained_layout=True)
gs = fig.add_gridspec(2,2)
N = 1000
axis = (0,1,0,1)
dtc = RandomForestClassifier(10)
dtc.fit(data, labels)
sk_dtc = RFC(max_depth = 10)
sk_dtc.fit(data, labels)
data_distr = add_plot_data_2d(fig, gs[0,0], data, labels, "Data distributions", axis)
xs = np.random.rand(N,2)
res = add_plot_model_predictions(fig, gs[0,1], xs, dtc, "Predictions", axis, True)
sk_res = add_plot_model_predictions(fig, gs[1,1], xs, sk_dtc, "sklearn predictions", axis, True)
plt.show()
