45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
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import torch
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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import torch.nn as nn
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import torch.nn.functional as F
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class ANN_Model(nn.Module):
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def __init__(self,input_features=82,hidden1=20,hidden2=20,out_features=3):
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super().__init__()
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self.f_connected1=nn.Linear(input_features,hidden1)
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self.f_connected2=nn.Linear(hidden1,hidden2)
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self.out=nn.Linear(hidden2,out_features)
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def forward(self, x):
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x=F.relu(self.f_connected1(x))
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x=F.relu(self.f_connected2(x))
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x=self.out(x)
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return x
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data = pd.read_csv("Sales.csv")
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data["Profit_Category"] = pd.cut(data["Profit"], bins=[-np.inf, 500, 1000, np.inf], labels=[0, 1, 2])
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bike = data.loc[:, ['Customer_Age', 'Customer_Gender', 'Country','State', 'Product_Category', 'Sub_Category', 'Profit_Category']]
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bikes = pd.get_dummies(bike, columns=['Country', 'State', 'Product_Category', 'Sub_Category', 'Customer_Gender'])
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X = bikes.drop('Profit_Category', axis=1).values
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y = bikes['Profit_Category'].values
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
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scaler = StandardScaler()
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X_test = scaler.fit_transform(X_test)
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X_test = torch.FloatTensor(X_test)
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y_test = torch.LongTensor(y_test)
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model = torch.load("classificationn_model.pt")
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model.eval()
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with torch.no_grad():
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y_pred = model(X_test)
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_, predicted = torch.max(y_pred.data, 1)
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np.savetxt("predictions1.txt", predicted.numpy(), fmt='%d')
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