Machine learning from zero to sklearn ·0.1.1· Linear fitting

Machine learning from zero to sklearn ·0.1.1· Linear fitting

• code:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# introduction package
from sklearn.linear_model.stochastic_gradient import SGDRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split



class Args:

    seed=1234
    data_file="sample_data.csv"
    num_samples=100
    train_size=0.75
    test_size=0.25
    num_epochs=100

args = Args()

# Set random seeds to ensure reproducibility of results
np.random.seed(args.seed)

# Generate data
def generate_data(num_samples) :
    X = np.array(range(num_samples))
    random_noise = np.random.uniform(-20.20,size=num_samples)
    y = 3.65*X + 10 + random_noise # add some noise
    return X, y

Generate random but linear data
X, y = generate_data(args.num_samples)
data = np.vstack([X, y]).T
df = pd.DataFrame(data, columns=['X'.'y'])
print(df.head())


Divide the data set
X_train, X_test, y_train, y_test = train_test_split(
    df["X"].values.reshape(-1.1), df["y"], test_size=args.test_size, 
    random_state=args.seed)
print ("X_train:", X_train.shape)
print ("y_train:", y_train.shape)
print ("X_test:", X_test.shape)
print ("y_test:", y_test.shape)

# Standardized training set
X_scaler = StandardScaler().fit(X_train)
y_scaler = StandardScaler().fit(y_train.values.reshape(-1.1))

# Standardize on training set and test set respectively
standardized_X_train = X_scaler.transform(X_train)
standardized_y_train = y_scaler.transform(y_train.values.reshape(-1.1)).ravel()
standardized_X_test = X_scaler.transform(X_test)
standardized_y_test = y_scaler.transform(y_test.values.reshape(-1.1)).ravel()

print ("mean:", np.mean(standardized_X_train, axis=0), 
       np.mean(standardized_y_train, axis=0)) # mean should be ~0
print ("std:", np.std(standardized_X_train, axis=0), 
       np.std(standardized_y_train, axis=0))   # std should be 1

lm = SGDRegressor(loss="squared_loss", penalty="none", max_iter=args.num_epochs)
# lm model
print(lm)

lm.fit(X=standardized_X_train, y=standardized_y_train)

pred_train = (lm.predict(standardized_X_train) * np.sqrt(y_scaler.var_)) + y_scaler.mean_
pred_test = (lm.predict(standardized_X_test) * np.sqrt(y_scaler.var_)) + y_scaler.mean_


# Resize the graph
plt.figure(figsize=(16.9))


# Draw the training data



plt.subplot(1.2.1)
plt.title("Train")
plt.scatter(X_train, y_train, label="y_train")
plt.plot(X_train, pred_train, color="red", linewidth=1, linestyle="-", label="lm")
plt.legend(loc='lower right')

# Draw the 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, color="red", linewidth=1, linestyle="-", label="lm")
plt.legend(loc='lower right')

plt.show()
Copy the code

• bash out:

X y 0 1 1.0 18.534351 0.0 2.339222 2, 2.0 14.809110 3.0 32.364343 4.0 35.799032 X_train: 3 (75, 1) y_train: (75,) X_test: (25, 1) Y_test: (25, 1) mean: [8.22952817E-17] 3.1798637796972193E-16 STD: [1.] 1.0000000000000002 SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1, eTA0 =0.01, Fit_intercept = True, l1_ratio = 0.15, learning_rate ='invscaling', loss='squared_loss', max_iter=100,
       n_iter=None, n_iter_no_change=5, penalty='none', power_t=0.25, random_state=None, shuffle=True, TOl =None, validation_fraction=0.1, verbose=0, warm_start=False)Copy the code

• image out: