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Michal Maciaszek 2020-12-08 23:13:06 +01:00
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5 changed files with 910 additions and 889 deletions
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BIN
model.pkl
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Binary file not shown.

22
predict.py
View File

@ -17,7 +17,25 @@ def read_data_file(filepath):
def dataframe_to_arrays(dataframe): def dataframe_to_arrays(dataframe):
dataframe1 = dataframe.copy(deep=True) dataframe1 = dataframe.copy(deep=True)
dataframe1["type"] = dataframe1["type"].astype('category').cat.codes #dataframe1["type"] = dataframe1["type"].astype('category').cat.codes
dataframe1 = pd.get_dummies(dataframe1, columns=['type'])
if 'type_apartamentowiec' not in dataframe1:
dataframe1['type_apartamentowiec'] = 0
if 'type_blok' not in dataframe1:
dataframe1['type_blok'] = 0
if 'type_dom wolnostojący' not in dataframe1:
dataframe1['type_dom wolnostojący'] = 0
if 'type_loft' not in dataframe1:
dataframe1['type_loft'] = 0
if 'type_kamienica' not in dataframe1:
dataframe1['type_kamienica'] = 0
if 'type_plomba' not in dataframe1:
dataframe1['type_plomba'] = 0
if 'type_szeregowiec' not in dataframe1:
dataframe1['type_szeregowiec'] = 0
return dataframe1 return dataframe1
#PREDICT_FILE_PATH = 'dev-0/in.tsv' #PREDICT_FILE_PATH = 'dev-0/in.tsv'
@ -31,7 +49,7 @@ def main():
for index, row in data.iterrows(): for index, row in data.iterrows():
#print(row[0], row[1]) #print(row[0], row[1])
x = torch.tensor([float(row[0]), float(row[1]), 1]) x = torch.tensor([float(row[0]), float(row[1]), 1, float(row[3]), float(row[4]),float(row[5]),float(row[6]),float(row[7]),float(row[8])])
y = x @ w y = x @ w
print(y.item()) print(y.item())

836
test-A/out.tsv
View File

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17
train.py
View File

@ -24,14 +24,18 @@ def read_data_file(filepath):
def dataframe_to_arrays(dataframe): def dataframe_to_arrays(dataframe):
dataframe1 = dataframe.copy(deep=True) dataframe1 = dataframe.copy(deep=True)
dataframe1["type"] = dataframe1["type"].astype('category').cat.codes #dataframe1["type"] = dataframe1["type"].astype('category').cat.codes
dataframe1 = pd.get_dummies(dataframe1, columns =['type'])
print(dataframe1.columns)
input_cols = dataframe1.columns.values[1:]
output_cols = dataframe1.columns.values[:1]
inputs_array = dataframe1[input_cols].to_numpy() inputs_array = dataframe1[input_cols].to_numpy()
targets_array = dataframe1[output_cols].to_numpy() targets_array = dataframe1[output_cols].to_numpy()
return inputs_array, targets_array return inputs_array, targets_array
data = read_data_file(TRAIN_FILE_PATH) data = read_data_file(TRAIN_FILE_PATH)
input_cols = data.columns.values[1:]
output_cols = data.columns.values[:1]
@ -40,11 +44,10 @@ 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)
weights = torch.tensor([1.0, 300000.0,1.0, 1.0, 1.0, 1.0,1.0,1.0,1.0], requires_grad=True)
weights = torch.tensor([1.0, 1.0,300000.0], requires_grad=True) learning_rate = torch.tensor(0.0000000000005)
learning_rate = torch.tensor(0.00000000001)
print("training started") print("training started")
for i in range(20000): for i in range(300):
y_predicted = inputs_training @ weights 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()