Machine Learning Library

### Introduction

This is a Python script for a machine learning project. It includes several classes that define models, preprocessors, and feature extractors for performing supervised machine learning tasks, including binary classification, multiclass classification, and regression. The script imports several libraries from the `sklearn` package, including `KNeighborsClassifier`, `SVC`, `DecisionTreeClassifier`, `RandomForestClassifier`, `GradientBoostingClassifier`, `MLPClassifier`, `Pipeline`, `make_pipeline`, `ColumnTransformer`, `StandardScaler`, `OneHotEncoder`, `CountVectorizer`, `DictVectorizer`, `PCA`, `cross_val_score`, and `joblib`.

The `Preprocessor` class defines a transformer that scales and standardizes input data. The `FeatureExtractor` class defines a transformer that uses principal component analysis (PCA) to extract features from input data. The `Model` class defines an abstract class that specifies the interface for machine learning models, including `train`, `predict`, and `evaluate` methods.

There are three model classes in the script. The `LinearRegression` class defines a linear regression model that uses the normal equation to calculate coefficients. The `LogisticRegression` class defines a logistic regression model that uses gradient boosting to classify input data. The `DecisionTree` class defines a decision tree model that can be used for binary classification, multiclass classification, or regression.

### PROTOTYPE

```
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction import DictVectorizer
from sklearn.decomposition import PCA
from sklearn.model_selection import cross_val_score
from sklearn.externals import joblib

        self.scaler = StandardScaler()

    def fit(self, X, y=None):
        self.scaler.fit(X)
        return self

    def transform(self, X):
        return self.scaler.transform(X)

        self.pca = PCA(n_components=n_components)

    def fit(self, X, y=None):
        self.pca.fit(X)
        return self

    def transform(self, X):
        return self.pca.transform(X)

        # Initialize any necessary variables or parameters here
        pass

    def train(self, X, y):
        # Train the model on the given input data (X) and labels (y)
        pass

    def predict(self, X):
        # Make predictions for the given input data (X)
        pass

    def evaluate(self, X, y):
        # Evaluate the performance of the model on the given input data (X) and labels (y)
        pass

        self.pipeline = make_pipeline(
            StandardScaler(),
            PCA(),
            StandardScaler(with_mean=False)
        )

        self.coefficients = None

    def train(self, X, y):
        X = self.pipeline.fit_transform(X, y)
        X = np.insert(X, 0, 1, axis=1) # add column of ones for bias term
        self.coefficients = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y) # calculate coefficients using normal equation
        joblib.dump(self, 'linear_regression.joblib') # save the model to disk

    def predict(self, X):
        X = self.pipeline.transform(X)
        X = np.insert(X, 0, 1, axis=1) # add column of ones for bias term
        return X.dot(self.coefficients) # return predictions

    def evaluate(self, X, y):
        pipeline = make_pipeline(self.pipeline, LinearRegression())
        scores = cross_val_score(pipeline, X, y, scoring='neg_mean_squared_error', cv=5)
        return -np.mean(scores)

        self.pipeline = make_pipeline(
            StandardScaler(),
            PCA(),
            StandardScaler(with_mean=False)
        )

        self.clf = None

    def train(self, X, y):
        X = self.pipeline.fit_transform(X, y)
        self.clf = GradientBoostingClassifier().fit(X, y)
        dump(self.clf, 'model.joblib')

    def predict(self, X):
        # Implement prediction using logistic regression model
        X = self.pipeline.transform(X)
        return load('model.joblib').predict(X)

    def predict_proba(self, X):
        # Return probability estimates for each class
        X = self.pipeline.transform(X)
        return load('model.joblib').predict_proba(X)

    def evaluate(self, X, y):
        pipeline = make_pipeline(self.pipeline, LogisticRegression())
        return np.mean(cross_val_score(pipeline, X, y, cv=5))

# Binary Classification
class DecisionTree(BaseEstimator, TransformerMixin):
    def __init__(self, max_depth=None, n_components=None):
        self.max_depth = max_depth
        self.n_components = n_components
        self.pipeline = Pipeline([
            ('scaler', StandardScaler()),
            ('pca', PCA(n_components=self.n_components)),
            ('tree', DecisionTreeClassifier(max_depth=self.max_depth))
        ])

    def fit(self, X, y):
        self.pipeline.fit(X, y)
        return self

    def transform(self, X):
        return self.pipeline.predict(X)

    def score(self, X, y):
        return self.pipeline.score(X, y)

    def evaluate(self, X, y):
        scores = cross_val_score(self.pipeline, X, y, cv=5)
        return scores.mean(), scores.std()

# Multiclass Classification
class DecisionTree(BaseEstimator, TransformerMixin):
    def __init__(self, max_depth=None, n_components=None):
        self.max_depth = max_depth
        self.n_components = n_components
        self.pipeline = Pipeline([
            ('scaler', StandardScaler()),
            ('pca', PCA(n_components=self.n_components)),
            ('tree', DecisionTreeClassifier(max_depth=self.max_depth, criterion='entropy', splitter='best'))
        ])

    def fit(self, X, y):
        self.pipeline.fit(X, y)
        return self

    def transform(self, X):
        return self.pipeline.predict(X)

    def score(self, X, y):
        return self.pipeline.score(X, y)

    def evaluate(self, X, y):
        scores = cross_val_score(self.pipeline, X, y, cv=5)
        return scores.mean(), scores.std()

# Regression Classification
class DecisionTree(BaseEstimator, TransformerMixin):
    def __init__(self, max_depth=None, n_components=None):
        self.max_depth = max_depth
        self.n_components = n_components
        self.pipeline = Pipeline([
            ('scaler', StandardScaler()),
            ('pca', PCA(n_components=self.n_components)),
            ('tree', DecisionTreeRegressor(max_depth=self.max_depth, criterion='mse', splitter='best'))
        ])

    def fit(self, X, y):
        self.pipeline.fit(X, y)
        return self

    def transform(self, X):
        return self.pipeline.predict(X)

    def score(self, X, y):
        return self.pipeline.score(X, y)

    def evaluate(self, X, y):
        scores = cross_val_score(self.pipeline, X, y, cv=5, scoring='neg_mean_squared_error')
        return -scores.mean(), scores.std()

class RandomForest(BaseEstimator, TransformerMixin):
    def __init__(self, n_estimators=100, max_depth=None):
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.pipeline = Pipeline([
            ('scaler', StandardScaler()),
            ('rf', RandomForestClassifier(n_estimators=self.n_estimators, max_depth=self.max_depth))
        ])

    def fit(self, X, y):
        self.pipeline.fit(X, y)
        return self

    def transform(self, X):
        return self.pipeline.predict(X)

    def score(self, X, y):
        return self.pipeline.score(X, y)

    def evaluate(self, X, y):
        scores = cross_val_score(self.pipeline, X, y, cv=5)
        return scores.mean(), scores.std()

class SupportVectorMachine(BaseEstimator, TransformerMixin):
    def __init__(self, C=1.0, kernel='rbf'):
        self.C = C
        self.kernel = kernel
        self.pipeline = Pipeline([
            ('scaler', StandardScaler()),
            ('svm', SVC(C=self.C, kernel=self.kernel))
        ])

    def fit(self, X, y):
        self.pipeline.fit(X, y)
        return self

    def transform(self, X):
        return self.pipeline.predict(X)

    def score(self, X, y):
        X = self.pipeline.named_steps['scaler'].transform(X)
        return self.pipeline.named_steps['svm'].score(X, y)

    def evaluate(self, X, y):
        scores = cross_val_score(self.pipeline, X, y, cv=5)
        return scores.mean(), scores.std()

class NeuralNetwork():
    def __init__(self, hidden_layer_sizes=(100,), activation='relu', solver='adam', alpha=0.0001, max_iter=200):
        self.hidden_layer_sizes = hidden_layer_sizes
        self.activation = activation
        self.solver = solver
        self.alpha = alpha
        self.max_iter = max_iter
        self.model = Pipeline([
            ('scaler', StandardScaler()),
            ('mlp', MLPClassifier(hidden_layer_sizes=self.hidden_layer_sizes,
                                   activation=self.activation,
                                   solver=self.solver,
                                   alpha=self.alpha,
                                   max_iter=self.max_iter))
        ])

    def preprocess_data(self, X):
        return StandardScaler().fit_transform(X)

    def extract_features(self, X):
        return X

    def train(self, X, y):
        self.model.fit(X, y)
        joblib.dump(self.model, 'model.joblib')

    def predict(self, X):
        self.model = joblib.load('model.joblib')
        X = self.preprocess_data(X)
        X = self.extract_features(X)
        return self.model.predict(X)

    def evaluate(self, X, y):
        self.model = joblib.load('model.joblib')
        X = self.preprocess_data(X)
        X = self.extract_features(X)
        return cross_val_score(self.model, X, y).mean()

        self.k = k
        self.knn = KNeighborsClassifier(n_neighbors=k)

    def fit(self, X, y):
        self.knn.fit(X, y)

    def transform(self, X):
        return self.knn.predict(X)

    def score(self, X, y):
        return self.knn.score(X, y)

        self.gb = GradientBoostingClassifier()

    def fit(self, X, y):
        self.gb.fit(X, y)

    def transform(self, X):
        return self.gb.predict(X)

    def predict_proba(self, X):
        return self.gb.predict_proba(X)

    def score(self, X, y):
        return self.gb.score(X, y)
```