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from typing import List, Optional, Tuple |
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from models.viscosity_models import CNN3D |
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import torch |
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from torch.utils.data.dataloader import DataLoader |
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from sklearn.metrics import r2_score |
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import numpy as np |
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import pandas as pd |
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@torch.no_grad() |
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def get_inference(model : CNN3D, data_loader : DataLoader, device : torch.device) -> Tuple[float, float]: |
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y_h_all = [] |
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y_all =[] |
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for (X,y) in data_loader: |
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X = X.to(device) |
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y = y.to(torch.float32) |
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y_h = model(X) |
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y_h_all.extend(y_h.detach().cpu().numpy()) |
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y_all.extend(y.numpy()) |
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df = pd.DataFrame({'y': np.array(y_all).ravel(), 'y_h': np.array(y_h_all).ravel()}) |
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return (df, r2_score(np.array(y_all),np.array(y_h_all))) |
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def combine_train_and_val(df_train,df_val): |
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df = pd.concat([df_train,df_val]) |
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r2 = r2_score(df['y'],df['y_h']) |
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return df,r2 |
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