for row in reader:
my_obj = {}
for index, col in enumerate(row):
my_obj[headers[index]] = col
data.append(my_obj)
np.random.shuffle(data)
split_point = int(np.floor(len(data) * 0.9))
train = data[0:split_point]
test = data[split_point:-1]
train_datatypes = list(map(lambda item: item['datatype'], train))
train_unique_datatypes, train_unique_datatypes_count = np.unique(
train_datatypes, return_counts=True)
print.info("Lets look at the statistics of the training dataset")
sort_index = np.argsort(-train_unique_datatypes_count)
y_pos = np.arange(len(train_unique_datatypes))
plt.bar(y_pos,
train_unique_datatypes_count[sort_index],
align='center',
alpha=0.5)
plt.xticks(y_pos, train_unique_datatypes[sort_index], rotation=90)
plt.subplots_adjust(bottom=0.35)
plt.ylabel('Usage')
plt.title('Data type')
plt.show()
