LinearRegression

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch.optim import Adam
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
SEED = 1234
NUM_SAMPLES = 50
np.random.seed(SEED)

TRAIN_SIZE = 0.7
VAL_SIZE = 0.15
TEST_SIZE = 0.15

NUM_EPOCHS = 100
LEARNING_RATE = 1e-1

# Generate synthetic data
def generate_data(num_samples):
    """Generate dummy data for linear regression."""
    X = np.array(range(num_samples))
    random_noise = np.random.uniform(-10,20,size=num_samples)
    y = 3.5*X + random_noise # add some noise
    return X, y

# Generate random (linear) data
X, y = generate_data(num_samples=NUM_SAMPLES)
X = [[x] for x in X]
y = [[i] for i in y]
print(f"X:\n{X}")
print(f"y:\n{y}")

# Set seed for reproducibility
torch.manual_seed(SEED)
# Split (train)
X_train, X_, y_train, y_ = train_test_split(X, y, train_size=TRAIN_SIZE)
print(f"train: {len(X_train)} ({(len(X_train) / len(X)):.2f})\n"
       f"remaining: {len(X_)} ({(len(X_) / len(X)):.2f})")
# Split (test)
X_val, X_test, y_val, y_test = train_test_split(
    X_, y_, train_size=0.5)
print(f"train: {len(X_train)} ({len(X_train)/len(X):.2f})\n"
      f"val: {len(X_val)} ({len(X_val)/len(X):.2f})\n"
      f"test: {len(X_test)} ({len(X_test)/len(X):.2f})")

print(f"X_train:\n{X_train}")
print(f"y_train:\n{y_train}")
# Standardize the data (mean=0, std=1) using training data
X_scaler = StandardScaler().fit(X_train)
y_scaler = StandardScaler().fit(y_train)

print(f"X_scaler:\n{X_scaler}")
print(f"y_scaler:\n{y_scaler}")

# Apply scaler on training and test data
X_train = X_scaler.transform(X_train)
y_train = y_scaler.transform(y_train).ravel().reshape(-1, 1)
X_val = X_scaler.transform(X_val)
y_val = y_scaler.transform(y_val).ravel().reshape(-1, 1)
X_test = X_scaler.transform(X_test)
y_test = y_scaler.transform(y_test).ravel().reshape(-1, 1)

# Check (means should be ~0 and std should be ~1)
print (f"mean: {np.mean(X_test, axis=0)[0]:.1f}, std: {np.std(X_test, axis=0)[0]:.1f}")
print (f"mean: {np.mean(y_test, axis=0)[0]:.1f}, std: {np.std(y_test, axis=0)[0]:.1f}")
INPUT_DIM = X_train.shape[1] # X is 1-dimensional
OUTPUT_DIM = y_train.shape[1] # y is 1-dimensional
print(f"INPUT_DI:\n{INPUT_DIM}")
print(f"OUTPUT_DIM:\n{OUTPUT_DIM}")
# Inputs
N = 3 # num samples
x = torch.randn(N, INPUT_DIM)
print(x.shape)
print(x.numpy())
# Weights
m = nn.Linear(INPUT_DIM, OUTPUT_DIM)
print (m)
print (f"weights ({m.weight.shape}): {m.weight[0][0]}")
print (f"bias ({m.bias.shape}): {m.bias[0]}")
# Forward pass
z = m(x)
print(z.shape)
print(z.detach().numpy())


class LinearRegression(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LinearRegression, self).__init__()
        self.fc1 = nn.Linear(input_dim, output_dim)

    def forward(self, x_in):
        y_pred = self.fc1(x_in)
        return y_pred

# Initialize model
model = LinearRegression(input_dim=INPUT_DIM, output_dim=OUTPUT_DIM)
print(model.named_parameters)
print(model.parameters())
loss_fn = nn.MSELoss()
y_pred = torch.Tensor([0., 0., 1., 1.])
y_true = torch.Tensor([1., 1., 1., 0.])
loss = loss_fn(y_pred, y_true)
print('Loss: ', loss.numpy())

# Optimizer
optimizer = Adam(model.parameters(), lr=LEARNING_RATE)

# Convert data to tensors
X_train = torch.Tensor(X_train)
y_train = torch.Tensor(y_train)
X_val = torch.Tensor(X_val)
y_val = torch.Tensor(y_val)
X_test = torch.Tensor(X_test)
y_test = torch.Tensor(y_test)

# Training
for epoch in range(NUM_EPOCHS):
    # Forward pass
    y_pred = model(X_train)

    # Loss
    loss = loss_fn(y_pred, y_train)

    # Zero all gradients
    optimizer.zero_grad()

    # Backward pass
    loss.backward()

    # Update weights
    optimizer.step()

    if epoch%20==0:
        print (f"Epoch: {epoch} | loss: {loss:.2f}")

# Predictions
pred_train = model(X_train)
pred_test = model(X_test)
# Performance
train_error = loss_fn(pred_train, y_train)
test_error = loss_fn(pred_test, y_test)
print(f'train_error: {train_error:.2f}')
print(f'test_error: {test_error:.2f}')

# Figure size
plt.figure(figsize=(15,5))

# Plot train data
plt.subplot(1, 2, 1)
plt.title("Train")
plt.scatter(X_train, y_train, label='y_train')
plt.plot(X_train, pred_train.detach().numpy(), color='red', linewidth=1, linestyle='-', label='model')
plt.legend(loc='lower right')

# Plot test data
plt.subplot(1, 2, 2)
plt.title("Test")
plt.scatter(X_test, y_test, label='y_test')
plt.plot(X_test, pred_test.detach().numpy(), color='red', linewidth=1, linestyle='-', label='model')
plt.legend(loc='lower right')

# Show plots
plt.show()

标签：val,scaler,len,print,train,LinearRegression,test
来源： https://www.cnblogs.com/songyuejie/p/14954839.html