Ejemplos de Bibliotecas Python ML

# Scikit-learn Fundamentals

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.svm import SVC, SVR
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score, classification_report, mean_squared_error, r2_score
from sklearn.feature_extraction.text import TfidfVectorizer
import matplotlib.pyplot as plt

# 1. Classification Example
def classification_example():
    # Load dataset
    iris = datasets.load_iris()
    X, y = iris.data, iris.target

    # Split data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

    # Scale features
    scaler = StandardScaler()
    X_train_scaled = scaler.fit_transform(X_train)
    X_test_scaled = scaler.transform(X_test)

    # Train models
    models = {
        'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),
        'Logistic Regression': LogisticRegression(random_state=42),
        'SVM': SVC(random_state=42)
    }

    results = {}
    for name, model in models.items():
        model.fit(X_train_scaled, y_train)
        y_pred = model.predict(X_test_scaled)
        accuracy = accuracy_score(y_test, y_pred)
        results[name] = accuracy
        print(f"{name} Accuracy: {accuracy:.4f}")

    return results

# 2. Regression Example
def regression_example():
    # Generate synthetic data
    np.random.seed(42)
    X = np.random.randn(100, 5)
    y = 2 * X[:, 0] + 3 * X[:, 1] - X[:, 2] + 0.5 * X[:, 3] + np.random.randn(100) * 0.1

    # Split data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Train models
    models = {
        'Random Forest': RandomForestRegressor(n_estimators=100, random_state=42),
        'Linear Regression': LinearRegression(),
        'SVR': SVR(kernel='rbf')
    }

    results = {}
    for name, model in models.items():
        model.fit(X_train, y_train)
        y_pred = model.predict(X_test)
        mse = mean_squared_error(y_test, y_pred)
        r2 = r2_score(y_test, y_pred)
        results[name] = {'MSE': mse, 'R2': r2}
        print(f"{name} - MSE: {mse:.4f}, R2: {r2:.4f}")

    return results

# 3. Clustering Example
def clustering_example():
    # Generate synthetic data
    np.random.seed(42)
    X, _ = datasets.make_blobs(n_samples=300, centers=4, cluster_std=1.0, random_state=42)

    # Apply K-means clustering
    kmeans = KMeans(n_clusters=4, random_state=42)
    cluster_labels = kmeans.fit_predict(X)

    # Visualize results
    plt.figure(figsize=(10, 6))
    plt.scatter(X[:, 0], X[:, 1], c=cluster_labels, cmap='viridis', alpha=0.6)
    plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1],
                marker='x', s=300, c='red', label='Centroids')
    plt.title('K-means Clustering')
    plt.legend()
    plt.show()

    return cluster_labels

# 4. Text Processing Example
def text_classification_example():
    # Sample text data
    texts = [
        "I love this movie, it's fantastic!",
        "This movie is terrible and boring.",
        "Great acting and amazing storyline.",
        "Worst movie I have ever seen.",
        "Absolutely brilliant and captivating.",
        "Poor acting and weak plot."
    ]
    labels = [1, 0, 1, 0, 1, 0]  # 1: positive, 0: negative

    # Vectorize text
    vectorizer = TfidfVectorizer(max_features=1000)
    X = vectorizer.fit_transform(texts)

    # Train classifier
    X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.3, random_state=42)
    classifier = LogisticRegression(random_state=42)
    classifier.fit(X_train, y_train)

    # Evaluate
    y_pred = classifier.predict(X_test)
    print("Text Classification Results:")
    print(classification_report(y_test, y_pred))

    return classifier, vectorizer

# 5. Data Preprocessing Pipeline
def preprocessing_pipeline():
    # Sample data with missing values and categorical features
    data = {
        'age': [25, 30, 35, np.nan, 45, 50, 55, 60],
        'income': [50000, 60000, 70000, 80000, 90000, np.nan, 110000, 120000],
        'education': ['High School', 'Bachelor', 'Master', 'PhD', 'Bachelor', 'Master', 'PhD', 'Bachelor'],
        'target': [0, 1, 1, 1, 0, 1, 1, 0]
    }

    df = pd.DataFrame(data)

    # Handle missing values
    df.fillna({
        'age': df['age'].mean(),
        'income': df['income'].median()
    }, inplace=True)

    # Encode categorical variables
    le = LabelEncoder()
    df['education_encoded'] = le.fit_transform(df['education'])

    # Prepare features and target
    features = ['age', 'income', 'education_encoded']
    X = df[features]
    y = df['target']

    print("Preprocessed Data:")
    print(df.head())

    return X, y, le

# Run all examples
if __name__ == "__main__":
    print("=== Scikit-learn Examples ===")

    print("\n1. Classification Results:")
    classification_results = classification_example()

    print("\n2. Regression Results:")
    regression_results = regression_example()

    print("\n3. Clustering Results:")
    cluster_labels = clustering_example()

    print("\n4. Text Classification:")
    classifier, vectorizer = text_classification_example()

    print("\n5. Data Preprocessing:")
    X, y, encoder = preprocessing_pipeline()

# TensorFlow Deep Learning Examples

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# 1. Basic Neural Network for Classification
def neural_network_classification():
    # Load MNIST dataset
    (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

    # Preprocess data
    x_train = x_train.reshape(-1, 28*28).astype('float32') / 255.0
    x_test = x_test.reshape(-1, 28*28).astype('float32') / 255.0
    y_train = keras.utils.to_categorical(y_train, 10)
    y_test = keras.utils.to_categorical(y_test, 10)

    # Build model
    model = keras.Sequential([
        layers.Dense(512, activation='relu', input_shape=(784,)),
        layers.Dropout(0.3),
        layers.Dense(256, activation='relu'),
        layers.Dropout(0.3),
        layers.Dense(10, activation='softmax')
    ])

    # Compile model
    model.compile(
        optimizer='adam',
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )

    # Train model
    history = model.fit(
        x_train, y_train,
        batch_size=128,
        epochs=10,
        validation_split=0.2,
        verbose=1
    )

    # Evaluate model
    test_loss, test_acc = model.evaluate(x_test, y_test)
    print(f"Test Accuracy: {test_acc:.4f}")

    return model, history

# 2. CNN for Image Classification
def convnet_classification():
    # Load CIFAR-10 dataset
    (x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()

    # Preprocess data
    x_train = x_train.astype('float32') / 255.0
    x_test = x_test.astype('float32') / 255.0
    y_train = keras.utils.to_categorical(y_train, 10)
    y_test = keras.utils.to_categorical(y_test, 10)

    # Build CNN model
    model = keras.Sequential([
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.Flatten(),
        layers.Dense(64, activation='relu'),
        layers.Dense(10, activation='softmax')
    ])

    # Compile model
    model.compile(
        optimizer='adam',
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )

    # Train model
    history = model.fit(
        x_train, y_train,
        batch_size=64,
        epochs=20,
        validation_split=0.2,
        verbose=1
    )

    return model, history

# 3. Regression Neural Network
def neural_network_regression():
    # Generate synthetic regression data
    np.random.seed(42)
    X = np.random.randn(1000, 10)
    y = 2 * X[:, 0] + 3 * X[:, 1] - X[:, 2] + np.random.randn(1000) * 0.1

    # Split and scale data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    scaler = StandardScaler()
    X_train_scaled = scaler.fit_transform(X_train)
    X_test_scaled = scaler.transform(X_test)

    # Build regression model
    model = keras.Sequential([
        layers.Dense(64, activation='relu', input_shape=(10,)),
        layers.Dense(32, activation='relu'),
        layers.Dense(1)  # Linear activation for regression
    ])

    # Compile model
    model.compile(
        optimizer='adam',
        loss='mse',
        metrics=['mae']
    )

    # Train model
    history = model.fit(
        X_train_scaled, y_train,
        batch_size=32,
        epochs=100,
        validation_split=0.2,
        verbose=0
    )

    # Evaluate model
    test_loss, test_mae = model.evaluate(X_test_scaled, y_test)
    print(f"Test MAE: {test_mae:.4f}")

    return model, history

# 4. Custom Model Class
class CustomModel(keras.Model):
    def __init__(self, units=32, **kwargs):
        super(CustomModel, self).__init__(**kwargs)
        self.dense1 = layers.Dense(units, activation='relu')
        self.dense2 = layers.Dense(units, activation='relu')
        self.dropout = layers.Dropout(0.3)
        self.output_layer = layers.Dense(1)

    def call(self, inputs, training=False):
        x = self.dense1(inputs)
        x = self.dropout(x, training=training)
        x = self.dense2(x)
        return self.output_layer(x)

# 5. Transfer Learning Example
def transfer_learning():
    # Load pre-trained MobileNetV2
    base_model = keras.applications.MobileNetV2(
        input_shape=(224, 224, 3),
        include_top=False,
        weights='imagenet'
    )

    # Freeze base layers
    base_model.trainable = False

    # Add custom classification head
    model = keras.Sequential([
        base_model,
        layers.GlobalAveragePooling2D(),
        layers.Dense(128, activation='relu'),
        layers.Dropout(0.2),
        layers.Dense(10, activation='softmax')  # Assuming 10 classes
    ])

    # Compile model
    model.compile(
        optimizer=keras.optimizers.Adam(1e-3),
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )

    return model

# 6. Callbacks and Model Saving
def callbacks_example():
    # Define callbacks
    callbacks = [
        keras.callbacks.EarlyStopping(patience=5, restore_best_weights=True),
        keras.callbacks.ReduceLROnPlateau(factor=0.5, patience=3),
        keras.callbacks.ModelCheckpoint(
            'best_model.keras',
            save_best_only=True,
            monitor='val_loss'
        )
    ]

    return callbacks

# 7. Data Augmentation
def data_augmentation_example():
    # Create data augmentation layer
    data_augmentation = keras.Sequential([
        layers.RandomFlip("horizontal"),
        layers.RandomRotation(0.1),
        layers.RandomZoom(0.1),
        layers.RandomContrast(0.1)
    ])

    # Sample model with augmentation
    model = keras.Sequential([
        data_augmentation,
        layers.Conv2D(32, 3, activation='relu'),
        layers.MaxPooling2D(),
        layers.Flatten(),
        layers.Dense(10, activation='softmax')
    ])

    return model

# Run examples
if __name__ == "__main__":
    print("=== TensorFlow Examples ===")

    print("\n1. Neural Network Classification:")
    nn_model, nn_history = neural_network_classification()

    print("\n2. CNN Classification:")
    cnn_model, cnn_history = convnet_classification()

    print("\n3. Neural Network Regression:")
    reg_model, reg_history = neural_network_regression()

    print("\n4. Transfer Learning Model:")
    transfer_model = transfer_learning()

    print("\n5. Data Augmentation Model:")
    aug_model = data_augmentation_example()

# PyTorch Deep Learning Examples

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset, random_split
import torchvision
import torchvision.transforms as transforms
import numpy as np
import matplotlib.pyplot as plt

# 1. Basic Tensors and Operations
def tensor_operations():
    print("=== PyTorch Tensor Operations ===")

    # Create tensors
    x = torch.randn(3, 4)
    y = torch.randn(3, 4)

    # Basic operations
    z = x + y
    z = torch.add(x, y)
    z = x * y  # Element-wise multiplication

    print("Tensor x:\n", x)
    print("Tensor y:\n", y)
    print("Element-wise multiplication:\n", z)

    # Matrix multiplication
    a = torch.randn(4, 3)
    b = torch.randn(3, 5)
    c = torch.mm(a, b)  # or torch.matmul(a, b)
    print("Matrix multiplication (4x3 @ 3x5 = 4x5):\n", c.shape)

    # Gradient computation
    x = torch.tensor(2.0, requires_grad=True)
    y = x**2 + 3*x + 1
    y.backward()
    print(f"Gradient of y=x^2+3x+1 at x=2: {x.grad}")

# 2. Simple Neural Network
class SimpleNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

def simple_nn_example():
    print("\n=== Simple Neural Network ===")

    # Generate synthetic data
    torch.manual_seed(42)
    X = torch.randn(100, 10)
    y = torch.randint(0, 3, (100,))  # 3 classes

    # Create dataset and dataloader
    dataset = TensorDataset(X, y)
    train_size = int(0.8 * len(dataset))
    test_size = len(dataset) - train_size
    train_dataset, test_dataset = random_split(dataset, [train_size, test_size])

    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=16)

    # Initialize model
    model = SimpleNN(input_size=10, hidden_size=32, output_size=3)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # Training loop
    epochs = 20
    for epoch in range(epochs):
        model.train()
        for batch_X, batch_y in train_loader:
            optimizer.zero_grad()
            outputs = model(batch_X)
            loss = criterion(outputs, batch_y)
            loss.backward()
            optimizer.step()

        if (epoch + 1) % 5 == 0:
            print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')

    # Evaluation
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_X, batch_y in test_loader:
            outputs = model(batch_X)
            _, predicted = torch.max(outputs.data, 1)
            total += batch_y.size(0)
            correct += (predicted == batch_y).sum().item()

    print(f'Test Accuracy: {100 * correct / total:.2f}%')

# 3. CNN for Image Classification
class SimpleCNN(nn.Module):
    def __init__(self, num_classes=10):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, num_classes)
        self.dropout = nn.Dropout(0.25)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))  # 28x28 -> 14x14
        x = self.pool(self.relu(self.conv2(x)))  # 14x14 -> 7x7
        x = x.view(-1, 64 * 7 * 7)
        x = self.dropout(self.relu(self.fc1(x)))
        x = self.fc2(x)
        return x

def cnn_mnist_example():
    print("\n=== CNN for MNIST Classification ===")

    # Load MNIST dataset
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])

    train_dataset = torchvision.datasets.MNIST(
        root='./data', train=True, download=True, transform=transform
    )
    test_dataset = torchvision.datasets.MNIST(
        root='./data', train=False, download=True, transform=transform
    )

    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=64)

    # Initialize model
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = SimpleCNN().to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # Training
    epochs = 5
    for epoch in range(epochs):
        model.train()
        running_loss = 0.0
        for batch_idx, (data, target) in enumerate(train_loader):
            data, target = data.to(device), target.to(device)
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()

        print(f'Epoch {epoch+1}, Loss: {running_loss/len(train_loader):.4f}')

    # Testing
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            outputs = model(data)
            _, predicted = torch.max(outputs.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()

    print(f'Test Accuracy: {100 * correct / total:.2f}%')

# 4. Custom Dataset Class
from torch.utils.data import Dataset

class CustomDataset(Dataset):
    def __init__(self, data, targets, transform=None):
        self.data = data
        self.targets = targets
        self.transform = transform

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        sample = self.data[idx]
        label = self.targets[idx]

        if self.transform:
            sample = self.transform(sample)

        return sample, label

# 5. Transfer Learning with Pre-trained Models
def transfer_learning_example():
    print("\n=== Transfer Learning Example ===")

    # Load pre-trained ResNet
    model = torchvision.models.resnet18(pretrained=True)

    # Freeze all layers
    for param in model.parameters():
        param.requires_grad = False

    # Replace final layer
    num_features = model.fc.in_features
    model.fc = nn.Linear(num_features, 10)  # 10 classes for CIFAR-10

    # Only train the final layer
    for param in model.fc.parameters():
        param.requires_grad = True

    print("Transfer learning model created successfully")
    return model

# 6. GPU Usage
def gpu_example():
    print("\n=== GPU Usage Example ===")

    # Check if CUDA is available
    if torch.cuda.is_available():
        device = torch.device('cuda')
        print(f"GPU available: {torch.cuda.get_device_name()}")
    else:
        device = torch.device('cpu')
        print("Using CPU")

    # Move tensors to GPU
    x = torch.randn(1000, 1000).to(device)
    y = torch.randn(1000, 1000).to(device)

    # GPU computation
    z = torch.mm(x, y)
    print(f"Matrix multiplication completed on {device}")

# 7. Model Saving and Loading
def save_load_model_example():
    print("\n=== Model Saving and Loading ===")

    # Create and train a simple model
    model = SimpleNN(10, 32, 3)

    # Save model state dict
    torch.save(model.state_dict(), 'model.pth')
    print("Model saved successfully")

    # Load model
    loaded_model = SimpleNN(10, 32, 3)
    loaded_model.load_state_dict(torch.load('model.pth'))
    print("Model loaded successfully")

    # Save entire model
    torch.save(model, 'entire_model.pth')
    loaded_entire_model = torch.load('entire_model.pth')
    print("Entire model saved and loaded successfully")

# Run all examples
if __name__ == "__main__":
    tensor_operations()
    simple_nn_example()
    cnn_mnist_example()
    transfer_learning_example()
    gpu_example()
    save_load_model_example()