Efficient Solution for Implementing Stochastic Gradient Design in Programming Assignments

Instructions

Objective

Write a python assignment to implement stochastic gradient descent.

Requirements and Specifications

Source Code

!pip install --upgrade --no-cache-dir gdown ## Download Dataset from drive link !gdown --id 1H7ONGAS2hZgOBIq8csIdjjpNGVs4aWPL import pandas as pd import numpy as np from sklearn import preprocessing from sklearn.metrics import confusion_matrix from sklearn import svm import seaborn import matplotlib.pyplot as plt from sklearn.metrics import plot_confusion_matrix from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import accuracy_score from sklearn.ensemble import RandomForestClassifier from sklearn.decomposition import PCA ## Load Dataset df = pd.read_csv('creditcard.csv') df = df.astype({col: 'float32' for col in df.select_dtypes('float64').columns}) df = df.astype({col: 'int32' for col in df.select_dtypes('int64').columns}) df.head() ## Normalize Dataset scaler = MinMaxScaler(feature_range=(0, 1)) normed = scaler.fit_transform(df) df_normed = pd.DataFrame(data=normed, columns=df.columns) df_normed.head() ## Describe df.describe() ## Check correlation between variables plt.figure() seaborn.heatmap(df.corr(), cmap="YlGnBu") # Displaying the Heatmap #seaborn.set(font_scale=2,style='white') plt.title('Heatmap correlation') plt.show() # Balance df_1 = df[df['Class'] == 1] df_0 = df[df['Class'] == 0].iloc[:len(df_1),:] df = df_0.append(df_1, ignore_index = True) df = df.sample(frac=1) scaler = MinMaxScaler(feature_range=(0, 1)) normed = scaler.fit_transform(df) df_normed = pd.DataFrame(data=normed, columns=df.columns) df_normed.head() ## Split into train and test train = df_normed.sample(frac=0.7) val = df_normed.loc[~df_normed.index.isin(train.index)] train.reset_index(drop=True, inplace=True) val.reset_index(drop=True, inplace=True) ## Split data into X and y y_train = train['Class'] X_train = train.drop(columns = ['Time', 'Amount', 'Class']) y_val = val['Class'] X_val = val.drop(columns = ['Time', 'Amount', 'Class']) ## PCA pca = PCA(n_components = 2) pca.fit(X_train) X_train = pca.transform(X_train) pca = PCA(n_components = 2) pca.fit(X_val) X_val = pca.transform(X_val) ## Create Model model = svm.SVC(kernel = 'linear',C=1.0) model.fit(X_train, y_train) ## Measure Accuracy y_pred = model.predict(X_val) model_acc = accuracy_score(y_val, y_pred) print(f"The accuracy of the model is: {model_acc}") ## Confusion matrix fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize=(8,8)) plot_confusion_matrix(model, X_train, y_train, ax = axes[0]) plot_confusion_matrix(model, X_val, y_val, ax = axes[1]) plt.show() ## Display clustering x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1 y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02)) plt.figure(figsize=(8,8)) ypred = model.predict(np.c_[xx.ravel(), yy.ravel()]) ypred = ypred.reshape(xx.shape) plt.contourf(xx, yy, ypred, cmap = plt.cm.coolwarm, alpha = 0.8) idx = np.where(y_train == 1)[0] plt.scatter(X_train[idx,0], X_train[idx,1], c = 'red',cmap = plt.cm.coolwarm, marker='o',edgecolors='black', label = '1') idx = np.where(y_train == 0)[0] plt.scatter(X_train[idx,0], X_train[idx,1], c = 'blue',cmap = plt.cm.coolwarm, marker='s',edgecolors='black', label = '0') plt.legend() plt.legend() # Part 2) Now cluster for each pair of consecutive features fig, ax = plt.subplots(nrows = 2, ncols = 14, figsize=(30,10)) j = 0 k = 0 for i in range(27): X_train = train.drop(columns = ['Time', 'Amount', 'Class']).iloc[:,i:i+2] X_val = val.drop(columns = ['Time', 'Amount', 'Class']).iloc[:,i:i+2] model2 = KNeighborsClassifier(n_neighbors=2) model2.fit(X_train,y_train) y_min, y_max = X_val.values[:, 1].min() - 1, X_val.values[:, 1].max() + 1 x_min, x_max = X_val.values[:, 0].min() - 1, X_val.values[:, 0].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02)) ypred = model2.predict(np.c_[xx.ravel(), yy.ravel()]) ypred = ypred.reshape(xx.shape) ax[j,k].contourf(xx, yy, ypred, cmap = plt.cm.coolwarm, alpha = 0.8) idx = np.where(y_val == 1)[0] ax[j,k].scatter(X_val.values[idx,0], X_val.values[idx,1], c = 'red',cmap = plt.cm.coolwarm, marker='o',edgecolors='black', label = '1') idx = np.where(y_val == 0)[0] ax[j,k].scatter(X_val.values[idx,0], X_val.values[idx,1], c = 'blue',cmap = plt.cm.coolwarm, marker='s',edgecolors='black', label = '0') ax[j,k].axis('off') ax[j,k].legend() ax[j,k].set_title(f'V{i+1} vs. V{i+2}') k+=1 if k%14 == 0: j += 1 k = 0 plt.show() Now, we see that since we only have two classes, the best number of clusters/neighbors to select is 2. We see how each pair of variables is clustered in the multi-plot figure shown above. # Random Forest fig, ax = plt.subplots(nrows = 2, ncols = 14, figsize=(30,10)) j = 0 k = 0 for i in range(27): X_train = train.drop(columns = ['Time', 'Amount', 'Class']).iloc[:,i:i+2] X_val = val.drop(columns = ['Time', 'Amount', 'Class']).iloc[:,i:i+2] model3 = RandomForestClassifier(max_depth=2, random_state=0) model3.fit(X_train,y_train) y_min, y_max = X_val.values[:, 1].min() - 1, X_val.values[:, 1].max() + 1 x_min, x_max = X_val.values[:, 0].min() - 1, X_val.values[:, 0].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02)) ypred = model3.predict(np.c_[xx.ravel(), yy.ravel()]) ypred = ypred.reshape(xx.shape) ax[j,k].contourf(xx, yy, ypred, cmap = plt.cm.coolwarm, alpha = 0.8) idx = np.where(y_val == 1)[0] ax[j,k].scatter(X_val.values[idx,0], X_val.values[idx,1], c = 'red',cmap = plt.cm.coolwarm, marker='o',edgecolors='black', label = '1') idx = np.where(y_val == 0)[0] ax[j,k].scatter(X_val.values[idx,0], X_val.values[idx,1], c = 'blue',cmap = plt.cm.coolwarm, marker='s',edgecolors='black', label = '0') ax[j,k].axis('off') ax[j,k].legend() ax[j,k].set_title(f'V{i+1} vs. V{i+2}') k+=1 if k%14 == 0: j += 1 k = 0 plt.show()