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Michal Maciaszek 2020-12-08 20:50:35 +01:00
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commit de3f755d2b
5 changed files with 892 additions and 893 deletions
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BIN
model.pkl
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1
predict.py
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@ -20,6 +20,7 @@ def dataframe_to_arrays(dataframe):
dataframe1["type"] = dataframe1["type"].astype('category').cat.codes dataframe1["type"] = dataframe1["type"].astype('category').cat.codes
return dataframe1 return dataframe1
#PREDICT_FILE_PATH = 'dev-0/in.tsv'
PREDICT_FILE_PATH = 'test-A/in.tsv' PREDICT_FILE_PATH = 'test-A/in.tsv'
def main(): def main():

836
test-A/out.tsv
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24
train.py
View File

@ -2,10 +2,8 @@ import torch
import torch.nn as nn import torch.nn as nn
import numpy as np import numpy as np
import pandas as pd import pandas as pd
import sys
import torch.nn.functional as F
import pickle import pickle
from torch.utils.data import TensorDataset, DataLoader
TRAIN_FILE_PATH = 'train/train.tsv' TRAIN_FILE_PATH = 'train/train.tsv'
@ -15,7 +13,7 @@ def read_data_file(filepath):
df = pd.read_csv(filepath, sep='\t', header=None, index_col=None) df = pd.read_csv(filepath, sep='\t', header=None, index_col=None)
dataframe = df.iloc[:, [0,8,11]] dataframe = df.iloc[:, [0,8,11]]
dataframe.columns = ['price','biggy','type'] dataframe.columns = ['price','biggy','type']
#print(dataframe.size[0])
for x in range(len(dataframe)): for x in range(len(dataframe)):
dataframe['biggy'].loc[x] = dataframe['biggy'].loc[x].replace(" ","") dataframe['biggy'].loc[x] = dataframe['biggy'].loc[x].replace(" ","")
#such dumb solution, well, but at least it works #such dumb solution, well, but at least it works
@ -42,17 +40,17 @@ inputs_array_training, targets_array_training = dataframe_to_arrays(data)
inputs_training = torch.from_numpy(inputs_array_training).type(torch.float32) inputs_training = torch.from_numpy(inputs_array_training).type(torch.float32)
targets_training = torch.from_numpy(targets_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) weights = torch.tensor([1.0, 1.0,300000.0], requires_grad=True)
learning_rate = torch.tensor(0.000000000005) learning_rate = torch.tensor(0.00000000001)
print("training started") print("training started")
for i in range(10000): for i in range(20000):
y_predicted = inputs_training @ w y_predicted = inputs_training @ weights
cost = torch.sum((y_predicted - targets_training) ** 2) cost = torch.sum((y_predicted - targets_training) ** 2)
cost.backward() cost.backward()
with torch.no_grad(): with torch.no_grad():
w -= learning_rate * w.grad weights -= learning_rate * weights.grad
w.requires_grad = True weights.requires_grad = True
print(w) print(weights)
pickle.dump(w, open('model.pkl', 'wb')) pickle.dump(weights, open('model.pkl', 'wb'))