88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
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import sys
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import torch
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import torch.nn as nn
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from sklearn import preprocessing
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import numpy as np
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import pandas as pd
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np.set_printoptions(suppress=False)
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class LogisticRegressionModel(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(LogisticRegressionModel, self).__init__()
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self.linear = nn.Linear(input_dim, output_dim)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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out = self.linear(x)
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return self.sigmoid(out)
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train = pd.read_csv('train.csv')
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test = pd.read_csv('test.csv')
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categorical_cols = train.select_dtypes(include=object).columns.values
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input_cols = train.columns.values[1:-1]
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output_cols = train.columns.values[-1:]
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def dataframe_to_arrays(dataframe):
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# Make a copy of the original dataframe
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dataframe1 = dataframe.copy(deep=True)
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# Convert non-numeric categorical columns to numbers
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for col in categorical_cols:
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dataframe1[col] = dataframe1[col].astype('category').cat.codes
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# Extract input & outupts as numpy arrays
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min_max_scaler = preprocessing.MinMaxScaler()
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x_scaled = min_max_scaler.fit_transform(dataframe1)
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dataframe1 = pd.DataFrame(x_scaled, columns = dataframe1.columns)
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inputs_array = dataframe1[input_cols].to_numpy()
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targets_array = dataframe1[output_cols].to_numpy()
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return inputs_array, targets_array
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inputs_array_training, targets_array_training = dataframe_to_arrays(train)
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inputs_array_testing, targets_array_testing = dataframe_to_arrays(test)
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inputs_training = torch.from_numpy(inputs_array_training).type(torch.float32)
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targets_training = torch.from_numpy(targets_array_training).type(torch.float32)
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inputs_testing = torch.from_numpy(inputs_array_testing).type(torch.float32)
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targets_testing = torch.from_numpy(targets_array_testing).type(torch.float32)
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fTrain = inputs_training.values
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tTrain = targets_training.values
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fTest= inputs_testing.values
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tTest = targets_testing.values
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batch_size = 16
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num_epochs = 5
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learning_rate = 0.001
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input_dim = 6
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output_dim = 1
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model = LogisticRegressionModel(input_dim, output_dim)
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criterion = torch.nn.BCELoss(reduction='mean')
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optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
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for epoch in range(num_epochs):
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# print ("Epoch #",epoch)
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model.train()
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optimizer.zero_grad()
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# Forward pass
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y_pred = model(fTrain)
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# Compute Loss
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loss = criterion(y_pred, tTrain)
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# print(loss.item())
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# Backward pass
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loss.backward()
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optimizer.step()
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y_pred = model(fTest, return_dict=False)
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print("predicted Y value: ", y_pred.data)
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torch.save(model.state_dict(), 'stroke.pth')
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