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import csv
import os
import numpy as np
import pandas as pd
def loadData(purpose, fpath, has_labels=True):
print("Reading labels from: ", fpath)
print("Loading %s data..." % purpose)
#return lat, longi
if has_labels:
return parseFileWithLabel(fpath)
return parseFileWithoutLabel(fpath)
# parse training and validation files
def parseFileWithLabel(file_path):
with open(file_path, "r") as f:
data = f.read().splitlines()
features = [splitted_line[1] for splitted_line in
[line.split("\t", maxsplit=1) for line in data[1:]]]
labels = np.array([splitted_line[0] for splitted_line in
[line.split("\t", maxsplit=1) for line in data[1:]]])
print("Labels shape: ", labels.shape)
print("Samples length: ", len(features))
return features, labels #list(labels[:, 0]), list(labels[:, 1])
# parse test file
def parseFileWithoutLabel(file_path):
with open(file_path, "r") as f:
data = f.read().splitlines()
features = [splitted_line[0] for splitted_line in
[line.split("\t", maxsplit=1) for line in data[1:]]]
return features
def labels_to_numeric(labels):
# Data frame from list
labels_df = pd.DataFrame(labels)
# 0 - BE, 1 - CA, 2 - CH, 3 - FR
labels_df[0] = labels_df[0].replace({'BE': 0})
labels_df[0] = labels_df[0].replace({'CA': 1})
labels_df[0] = labels_df[0].replace({'CH': 2})
labels_df[0] = labels_df[0].replace({'FR': 3})
print(np.array(labels_df.values).flatten())
return list(np.array(labels_df.values).flatten())
def flatten_labels(true_labels, predictions):
true_labels_flat = []
predictions_flat = []
for index in range(len(true_labels)):
true_labels_flat += list(true_labels[index])
pred_flat = np.argmax(predictions[index], axis=1).flatten()
predictions_flat += list(pred_flat)
#print(true_labels_flat)
#print(predictions_flat)
return true_labels_flat, predictions_flat
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