first try

dev-0/out.tsv

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predict.py

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+import pickle
+import sys
+import torch
+import pandas as pd
+
+def read_data_file(filepath):
+    df = pd.read_csv(filepath, sep='\t', header=None, index_col=None)
+    dataframe = df.iloc[:, [7,10]]
+    dataframe.columns = ['biggy','type']
+    #print(dataframe.size[0])
+    # for x in range(len(dataframe)):
+    #     dataframe['biggy'].loc[x] = dataframe['biggy'].loc[x].replace(" ","")
+        #such dumb solution, well, but at least it works
+    dataframe['bias'] = 1
+    dataframe['biggy'] = dataframe['biggy'].astype(float)
+    return dataframe
+
+def dataframe_to_arrays(dataframe):
+    dataframe1 = dataframe.copy(deep=True)
+    dataframe1["type"] = dataframe1["type"].astype('category').cat.codes
+    return dataframe1
+
+PREDICT_FILE_PATH = 'test-A/in.tsv'
+
+def main():
+    w = pickle.load(open('model.pkl', 'rb'))
+
+    data = read_data_file(PREDICT_FILE_PATH)
+    data = dataframe_to_arrays(data)
+
+    for index, row in data.iterrows():
+        #print(row[0], row[1])
+        x = torch.tensor([float(row[0]), float(row[1]), 1])
+        y = x @ w
+        print(y.item())
+
+main()

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train.py

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+import torch
+import torch.nn as nn
+import numpy as np
+import pandas as pd
+import sys
+import torch.nn.functional as F
+import pickle
+from torch.utils.data import TensorDataset, DataLoader
+
+TRAIN_FILE_PATH = 'train/train.tsv'
+
+
+#prepare data methods
+def read_data_file(filepath):
+    df = pd.read_csv(filepath, sep='\t', header=None, index_col=None)
+    dataframe = df.iloc[:, [0,8,11]]
+    dataframe.columns = ['price','biggy','type']
+    #print(dataframe.size[0])
+    for x in range(len(dataframe)):
+        dataframe['biggy'].loc[x] = dataframe['biggy'].loc[x].replace(" ","")
+        #such dumb solution, well, but at least it works
+    dataframe['bias'] = 1
+    dataframe['biggy'] = dataframe['biggy'].astype(float)
+    return dataframe
+
+
+def dataframe_to_arrays(dataframe):
+    dataframe1 = dataframe.copy(deep=True)
+    dataframe1["type"] = dataframe1["type"].astype('category').cat.codes
+    inputs_array = dataframe1[input_cols].to_numpy()
+    targets_array = dataframe1[output_cols].to_numpy()
+    return inputs_array, targets_array
+
+data =  read_data_file(TRAIN_FILE_PATH)
+input_cols = data.columns.values[1:]
+output_cols = data.columns.values[:1]
+
+
+
+inputs_array_training, targets_array_training = dataframe_to_arrays(data)
+
+inputs_training = torch.from_numpy(inputs_array_training).type(torch.float32)
+targets_training = torch.from_numpy(targets_array_training).type(torch.float32)
+
+print(inputs_training)
+w = torch.tensor([7201.61492633873, 1,7201.500], requires_grad=True)
+learning_rate = torch.tensor(0.000000000005)
+print("training started")
+for i in range(10000):
+    y_predicted = inputs_training @ w
+    cost = torch.sum((y_predicted - targets_training) ** 2)
+    cost.backward()
+    with torch.no_grad():
+        w -= learning_rate * w.grad
+    w.requires_grad = True
+
+print(w)
+pickle.dump(w, open('model.pkl', 'wb'))