update learning

lab05_deepLearning.py

@@ -11,7 +11,12 @@ import torch.nn.functional as F
 import pandas as pd
 from sklearn import preprocessing
 import sys
+import os
 
+path = '.'
+files = os.listdir(".")
+if not "Car_Prices_Poland_Kaggle.csv" in files:
+    path = "data"
 
 class Model(nn.Module):
     def __init__(self, input_dim):
@@ -29,15 +34,15 @@ class Model(nn.Module):
 
 def load_dataset_raw():
     """ Load data from .csv file. """
-    cars = pd.read_csv('./Car_Prices_Poland_Kaggle.csv', usecols=[1, 4, 5, 6, 10], sep=',')
+    cars = pd.read_csv(f'{path}/Car_Prices_Poland_Kaggle.csv', usecols=[1, 4, 5, 6, 10], sep=',')
     return cars
 
 
 def load_dataset_files():
     """ Load shuffled, splitted dev and train files from .csv files. """
 
-    cars_dev = pd.read_csv('./Car_Prices_Poland_Kaggle_dev.csv', usecols=[1, 4, 5, 6, 10], sep=',', names= [str(i) for i in range(5)])
-    cars_train = pd.read_csv('./Car_Prices_Poland_Kaggle_train.csv', usecols=[1, 4, 5, 6, 10], sep=',', names= [str(i) for i in range(5)])
+    cars_dev = pd.read_csv(f'{path}/Car_Prices_Poland_Kaggle_dev.csv', usecols=[1, 4, 5, 6, 10], sep=',', names= [str(i) for i in range(5)])
+    cars_train = pd.read_csv(f'{path}/Car_Prices_Poland_Kaggle_train.csv', usecols=[1, 4, 5, 6, 10], sep=',', names= [str(i) for i in range(5)])
 
     return cars_dev, cars_train
 
@@ -65,15 +70,6 @@ def prepare_labels_features(dataset):
     return lab, feat
 
 
-
-
-
-# def draw_plot(lbl):
-# need to import matplotlib to work
-#     plt.hist(lbl, bins=[i for i in range(len(set(lbl)))], edgecolor="black")
-#     plt.xticks(np.arange(0, len(set(lbl)), 1))
-#     plt.show()
-
 # Prepare dataset
 print("Loading dataset...")
 dev, train = load_dataset_files()