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97 lines
2.6 KiB
Python
97 lines
2.6 KiB
Python
# %%
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from typing import Union
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import pandas as pd
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# %%
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from itertools import product
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from torch.nn.modules.activation import ReLU
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from torch.nn.modules.linear import Linear
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def get_state_vect_cols(prefix=''):
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if prefix:
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prefix += '_'
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vectors = ['r', 'v']
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components = ['x', 'y', 'z']
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col_names = [f'{prefix}{v}_{c}' for v, c in product(vectors, components)]
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return col_names
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# %%
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df = pd.read_parquet("traindata/physics_preds.parquet")
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test_set = df[df['aso_id'] == "05277"]
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train_set = df.groupby('aso_id').apply(lambda x: x.head(x.count()[0] - 3))
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print(df.count()[0], train_set.count()[0], test_set.count()[0])
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test_set
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# %%
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from sklearn.model_selection import train_test_split
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feature_cols = [
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'elapsed_seconds'
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] + get_state_vect_cols('physics_pred') + get_state_vect_cols('start')
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print(feature_cols)
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# The target values are the errors between the physical model predictions
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# and the ground truth observations
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target_cols = get_state_vect_cols('physics_err')
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print(target_cols)
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# Create feature and target matrices
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X = df[feature_cols]
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y = df[target_cols]
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data_keys = ['X_train', 'X_test', 'y_train', 'y_test']
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data_vals = train_test_split(X, y, test_size=0.2)
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train_test_data = dict(zip(data_keys, data_vals))
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# %%
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import torch
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import torch.nn as nn
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TestNet2 = lambda: nn.Sequential(
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nn.Linear(13, 32),
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nn.LeakyReLU(),
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nn.Linear(32, 16),
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nn.Sigmoid(),
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nn.Linear(16, 1),
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)
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nets = {}
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X_train = torch.tensor(train_test_data["X_train"].values)
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y_train = train_test_data["y_train"]
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for target_col in y_train.columns:
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y1 = torch.tensor(y_train[target_col].values).reshape(-1, 1)
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print(X.shape, y1.shape)
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net = TestNet2().double()
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opti = torch.optim.SGD(net.parameters(), lr=0.04)
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loss_func = nn.MSELoss()
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for t in range(10000):
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pred = net(X_train)
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loss = loss_func(pred, y1)
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if t % 1000 == 0:
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print(f'Epoch {t}, loss {loss}')
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opti.zero_grad()
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loss.backward()
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opti.step()
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nets[target_col] = net
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print(target_col)
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break
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# %%
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X, ys = train_test_data['X_test'], train_test_data['y_test']
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evals = []
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from sklearn import metrics
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with torch.no_grad():
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for target_col, net in nets.items():
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y_hat = net(torch.tensor(X.values)) # fake
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y_hat = y_hat.detach().numpy()
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y = ys[target_col] # real
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print(y)
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print(y_hat)
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rmse = metrics.mean_squared_error(y, y_hat, squared=False)
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r2 = metrics.r2_score(y, y_hat)
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eval_dict = {'Error': target_col, 'RMSE': rmse, 'R^2': r2}
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evals.append(eval_dict)
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print(pd.DataFrame(evals))
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# %%
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