File size: 9,060 Bytes
ef16512 66f43ca ef16512 66f43ca ef16512 66f43ca ef16512 66f43ca ef16512 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
# example_inference
import torch
from MultiTaskConvLSTM import ConvLSTMNetwork
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
import torch
import torch.nn as nn
from tqdm.auto import tqdm
from utils import (
mse, mae, nash_sutcliffe_efficiency, r2_score, pearson_correlation,
spearman_correlation, percentage_error, percentage_bias,
kendall_tau, spatial_correlation
)
import torch.optim as optim
device = 'cpu'
height = 81
width = 97
set_lookback = 1
set_forecast_horizon = 1
#Define variables for evaluation
batch_size = 16
time_steps_out = set_forecast_horizon
channels = 8
#Variable names
#Variable names
variable_names = ['10 metre U wind component', '10 metre V wind component', '2 metre dewpoint temperature', '2 metre temperature', 'Total column rain water', 'Total precipitation', 'Time-integrated surface latent heat net flux']
# Adjust input_dim and output_channels according to your data specifics
model = ConvLSTMNetwork(
input_dim=8 * set_lookback,
hidden_dims=[8, 32, 64],
kernel_size=(3,3),
num_layers=3,
output_channels=64 * set_forecast_horizon,
batch_first=True
).to(device)
# Define separate loss functions
loss_fn = nn.MSELoss() # For regression output
bce_loss_fn = nn.BCELoss() # For classification output
optimizer = optim.AdamW(model.parameters(), lr = 0.005)
checkpoint = torch.load("MultiTaskConvLSTM_no_veg_variables.pth", map_location = device)
model.load_state_dict(checkpoint['model_state_dict'])
# If you want to move the model to the GPU (optional, depending on your setup)
model.to(device) # Assuming you have a variable `device` for CUDA or CPU
# Ensure that the model is in evaluation mode if you're using it for inference
model.eval()
print("Model loaded successfully")
threshold = 0.1
precip_index = 10
def evaluate(model, test_loader, reg_loss_fn, class_loss_fn, device, variable_names, height, width):
"""
Evaluate the model on the test set for both regression and classification tasks.
"""
model.eval() # Set the model to evaluation model
# input_to_true = {'zero_to_non_zero': 0, 'non_zero_to_zero': 0}
# input_to_pred_REG = {'zero_to_non_zero': 0, 'non_zero_to_zero': 0}
# input_to_pred_CLASS = {'zero_to_non_zero': 0, 'non_zero_to_zero': 0}
test_reg_loss = 0.0
test_class_loss = 0.0
test_total_loss = 0.0
y_true_reg = [] # List to store true values for regression
y_pred_reg = [] # List to store predicted values for regression
y_pred_reg2 = []
y_true_class = [] # List to store true values for classification
y_pred_class = [] # List to store predicted probabilities for classification
# Disable gradient computation
with torch.no_grad():
for X_test, y_test, y_zero_test in tqdm(test_loader, desc="Evaluating on Test Set"):
# Move the batch to the device
X_test, y_test, y_zero_test = X_test.to(device), y_test.to(device), y_zero_test.to(device)
# Reshape inputs and targets
batch_size, time_steps_in, channels_in, grid_points = X_test.shape
batch_size, time_steps_out, channels_out, grid_points = y_test.shape
X_test = X_test.view(batch_size, time_steps_in, channels_in, height, width)
y_test = y_test.view(batch_size, time_steps_out, channels_out, height, width)
y_zero_test = y_zero_test.view(batch_size, time_steps_out, channels_out, height, width)
# Forward pass
regression_output, classification_output = model(X_test)
classification_predictions = (classification_output > 0.7).float()
# Compute regression loss
reg_loss = reg_loss_fn(regression_output, y_test)
# Compute classification loss
class_loss = class_loss_fn(classification_output, y_zero_test)
# Total loss
total_loss = reg_loss + class_loss
regression_output2 = torch.where(classification_predictions == 0, regression_output, classification_predictions)
# Accumulate losses
test_reg_loss += reg_loss.item() * X_test.size(0)
test_class_loss += class_loss.item() * X_test.size(0)
test_total_loss += total_loss.item() * X_test.size(0)
# Collect true and predicted values for regression and classification
y_true_reg.append(y_test.cpu())
y_pred_reg.append(regression_output.cpu())
y_pred_reg2.append(regression_output2.cpu())
y_true_class.append(y_zero_test.cpu())
y_pred_class.append(classification_output.cpu())
# Normalize losses by the total dataset size
test_reg_loss /= len(test_loader)
test_class_loss /= len(test_loader)
test_total_loss /= len(test_loader)
print(f"Test Regression Loss: {test_reg_loss:.16f}")
print(f"Test Classification Loss: {test_class_loss:.16f}")
print(f"Test Total Loss: {test_total_loss:.16f}")
y_true_reg_flat = torch.cat(y_true_reg, dim=0).flatten() # Keep as PyTorch tensor
y_pred_reg_flat = torch.cat(y_pred_reg, dim=0).flatten() # Keep as PyTorch tensor
y_true_class_flat = torch.cat(y_true_class, dim=0).flatten() # Keep as PyTorch tensor
y_pred_class_flat = torch.cat(y_pred_class, dim=0).flatten() # Keep as PyTorch tensor
# Compute regression metrics
regression_metrics = {
"MSE": mse(y_true_reg_flat, y_pred_reg_flat),
"MAE": mae(y_true_reg_flat, y_pred_reg_flat),
"NSE": nash_sutcliffe_efficiency(y_true_reg_flat, y_pred_reg_flat),
"R2": r2_score(y_true_reg_flat, y_pred_reg_flat),
"Pearson": pearson_correlation(y_true_reg_flat, y_pred_reg_flat),
"Spearman": spearman_correlation(y_true_reg_flat, y_pred_reg_flat),
"NSE": nash_sutcliffe_efficiency(y_true_reg_flat, y_pred_reg_flat),
"Percentage Error": percentage_error(y_true_reg_flat, y_pred_reg_flat),
"Percentage Bias": percentage_bias(y_true_reg_flat, y_pred_reg_flat),
"Kendall Tau": kendall_tau(y_true_reg_flat, y_pred_reg_flat),
"Spatial Correlation": spatial_correlation(y_true_reg_flat, y_pred_reg_flat)}
print("\nRegression Metrics:")
for metric, value in regression_metrics.items():
print(f"{metric}: {value:.16f}")
# Compute classification metrics
classification_metrics = {
"Accuracy": accuracy_score(y_true_class_flat, (y_pred_class_flat > 0.7)),
"Precision": precision_score(y_true_class_flat, (y_pred_class_flat > 0.7)),
"Recall": recall_score(y_true_class_flat, (y_pred_class_flat > 0.7)),
"F1": f1_score(y_true_class_flat, (y_pred_class_flat > 0.7)),
"ROC-AUC": roc_auc_score(y_true_class_flat, y_pred_class_flat),
}
print("\nClassification Metrics:")
for metric, value in classification_metrics.items():
print(f"{metric}: {value:.16f}")
torch.save({
'y_true_reg': y_true_reg_flat,
'y_pred_reg': y_pred_reg_flat,
'y_true_class': y_true_class_flat,
'y_pred_class': y_pred_class_flat,
}, 'results')
return test_total_loss, regression_metrics, classification_metrics
"""
EXPECTED DATALOADER BATCH FORMAT (normalized_test_data):
Each batch must be a tuple: (X_batch, y_batch, y_zero_batch)
X_batch contains the previous hours variables. y_batch contains the next hour's precipitation.
y_zero_batch contains the next hour's precipitation thresholded as 0 for precipiation <=0.1mm/h and
1 for precipitation >0.1mm.
Shapes BEFORE reshaping inside `evaluate`:
X_batch: (B, T_in, C_in, G) # G = H*W = 81*97 = 7857
y_batch: (B, T_out, C_out, G)
y_zero_batch: (B, T_out, C_out, G) # binary 0/1 "zero-precip" targets
If your preprocessing produces (B,T, C, H, W), reshape to (B, T, C, H*W) before inference.
DTypes:
X_batch, y_batch: torch.float32
y_zero_batch: torch.float32 (will be used with BCELoss)
Reshaping done in 'evaluate':
X_test = X_batch.view(B, T_in, C_in, H, W) -> (B, T_in, C_in, 81, 97)
y_test = y_batch.view(B, T_out, C_out, H, W) -> (B, T_out, C_out, 81, 97)
y_zero_test = y_zero_batch.view(B, T_out, C_out, H, W)
Model input:
model expects X_test shaped (B, T_in, input_dim, H, W)
where input_dim == 9 * set_lookback (with set_lookback=1 -> input_dim=9)
Notes:
• Make sure G == H*W (i.e., 7857 for 81x97).
• C_out for precipitation should be 1 (one target channel), and y_zero_batch
is the 0/1 mask for “zero precipitation” at each pixel & time.
• y_zero_batch should be probabilities/labels in {0,1} for BCELoss.
"""
normalized_test_data = torch.load("data/normalized_test_data_no_veg_input.pth")
test_total_loss, regression_metrics, classification_metrics = evaluate(
model=model,
test_loader=normalized_test_data,
reg_loss_fn=loss_fn,
class_loss_fn=bce_loss_fn,
device=device,
variable_names=variable_names,
height=height,
width=width,
) |