def norm_encode(y_true, y_pred):
y_true_1 = set(y_true)
y_pred_1 = set(y_pred)
joint = y_true_1.copy()
joint.update(y_pred_1)
norm = OrdinalEncoder().fit_transform(np.array(list(joint)).reshape(-1, 1))
norm = norm.reshape(1, -1)[0]
coding = {k: v for k, v in zip(np.array(list(joint)), norm)}
y_true = [coding[i] for i in y_true]
y_pred = [coding[i] for i in y_pred]
return np.array(y_true).astype(int), np.array(y_pred).astype(int)
plt.rcParams['figure.figsize'] = [3.2, 2.4]
plt.rcParams['figure.dpi'] = 144
plt.hist(y_train)
plt.title('y_train {}'.format(response_list[0]))
plt.show()
# ## Random Forest classification <a id='two'></a>
# In[ ]:
# Reshape arrays
X_train_nx, X_train_ny, X_train_ns = X_train.shape
X_train = X_train.reshape((X_train_nx * X_train_ny, X_train_ns))
y_train_nx, y_train_ny = y_train.shape
y_train = y_train.reshape((y_train_nx * y_train_ny))
X_test_nx, X_test_ny, X_test_ns = X_test.shape
X_test = X_test.reshape((X_test_nx * X_test_ny, X_test_ns))
y_test_nx, y_test_ny = y_test.shape
y_test = y_test.reshape((y_test_nx * y_test_ny))
print("Dimensions of X_train: {}".format(X_train.shape))
print("Dimensions of y_train: {}".format(y_train.shape))
print("Dimensions of X_test: {}".format(X_test.shape))
print("Dimensions of y_test: {}".format(y_test.shape))
# In[ ]:
# Unique value counts
