Bread-and-Butter_Machine-Learning/bnbML/Supervised_Learning/LinearModel.py at master · ArtistBanda/Bread-and-Butter_Machine-Learning · GitHub

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import numpy as np
from bnbML.Deep_Learning import ActivationFunctions
from bnbML.Utils import LossFunctions
from bnbML.Utils.Metrics import accuracy
from bnbML.Utils.Plotting import plotLossGraph
from bnbML.Utils.PreProcessing import normalize
import matplotlib.pyplot as plt
import pandas as pd
import progressbar


class LinearModel(object):
    def fit(self, x_train, y_train):
        raise NotImplementedError

    def predict(self, x):
        raise NotImplementedError


class LinearRegression(LinearModel):
    def __init__(self, normalize=False):
        self.weights = None
        self.history = []
        self.iter_count = 0
        self.normalize = normalize

    def fit(self, x_train, y_train, epochs=10, learning_rate=0.01):

        x_train, y_train = self._checks(x_train, y_train)

        x_train = self._insert_bias(x_train)

        if self.weights is None:
            self.weights = self._initialize_weights(x_train.shape)

        for _ in progressbar.progressbar(range(epochs)):

            y_cap = np.dot(x_train, self.weights)

            self.history.append(LossFunctions.MSE(y_train, y_cap))

            print("Loss : " + str(self.history[len(self.history) - 1]))

            self.iter_count += 1

            N = x_train.shape[0]
            slope = (1 / N) * (np.dot(x_train.T,
                                      (np.dot(x_train, self.weights) - y_train)))

            self.weights = self.weights - learning_rate * slope

    def predict(self, x):
        x = self._insert_bias(x)
        return np.dot(x, self.weights)

    def score(self, x, y):
        x = self._insert_bias(x)
        x, y = self._checks(x, y)
        y_cap = np.dot(x, self.weights)
        return LossFunctions.MSE(y, y_cap)

    def plotLossGraph(self):
        plotLossGraph(self.history, self.iter_count)

    def _initialize_weights(self, input_shape):
        return np.zeros((input_shape[1], 1))

    def _insert_bias(self, x):
        return np.insert(x, 0, 1, axis=1)

    def _checks(self, x, y):
        if y.shape is not tuple((len(y), 1)):
            y = np.reshape(y, (y.shape[0], 1))

        if self.normalize is True:
            x = normalize(x)

        return x, y


class BinaryLogisticRegression(LinearModel):
    def __init__(self):
        self.weights = None
        self.bias = 0
        self.history = []
        self.iter_count = 0
        self.normalize = False

    def fit(self, x_train, y_train, epochs=10, learning_rate=0.01):
        x_train, y_train = self._checks(x_train, y_train)

        x_train = self._insert_bias(x_train)

        self.weights = self._initialize_weights(x_train.shape)

        for _ in progressbar.progressbar(range(epochs)):

            y_cap, _ = ActivationFunctions.Sigmoid(
                np.dot(x_train, self.weights))

            self.history.append([LossFunctions.CrossEntropyLoss(
                y_train, y_cap), accuracy(y_train, y_cap)])

            print("Accuracy : " + str(self.history[len(self.history) - 1][1]))

            self.iter_count += 1

            N = x_train.shape[1]

            grad = (1 / N) * np.dot(x_train.T, (y_cap - y_train))

            self.weights = self.weights - learning_rate * grad

    def predict(self, x):
        x = self._insert_bias(x)
        out, _ = ActivationFunctions.Sigmoid(
            np.dot(x, self.weights) + self.bias)
        return out

    def plotLossGraph(self):
        plotLossGraph(self.history, self.iter_count)

    def _initialize_weights(self, input_shape):
        return np.zeros((input_shape[1], 1))

    def _insert_bias(self, x):
        return np.insert(x, 0, 1, axis=1)

    def _checks(self, x, y):
        if y.shape is not tuple((len(y), 1)):
            y = np.reshape(y, (y.shape[0], 1))

        if self.normalize is True:
            x = normalize(x)

        return x, y