- For the lab program uploaded in the blackboard, analyze the dataset and generate reports indicating the changes in the value of accuracy when the n_components value ranges from 15 to 20 with different dimensionality reduction techniques along with different classifiers.
- by using this code
- { "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n", "1-PCAn", "2-FAn", "3-LDAn", "4-ISOn", "5-LLEn", "n", "Enter your choice: 1n", "n", "1-NBn", "2-KNNn", "3-LRn", "4-DTn", "5-RFn", "n", "Enter your choice: 3n", "0.9789160401002507n" ] } ], "source": [ "import pandas as pdn", "import numpy as npn", "from pandas import read_csvn", "n", "#from sklearn.feature_selection import SelectKBestn", "#from sklearn.feature_selection import f_classifn", "from sklearn.decomposition import PCAn", "from sklearn.decomposition import FactorAnalysisn", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysisn", "from sklearn.manifold import Isomapn", "from sklearn.manifold import LocallyLinearEmbeddingn", "n", "n", "from sklearn import model_selectionn", "from sklearn.linear_model import LogisticRegressionn", "import mathn", "from sklearn.neighbors import KNeighborsClassifiern", "from sklearn.preprocessing import StandardScalern", "from sklearn.naive_bayes import GaussianNBn", "from sklearn.tree import DecisionTreeClassifiern", "from sklearn.svm import SVCn", "from sklearn.ensemble import RandomForestClassifiern", "#from sklearn.ensemble import AdaBoostClassifiern", "#from sklearn.ensemble import GradientBoostingClassifiern", "n", "#filename = 'pima-indians-diabetes.data.csv'n", "filename = 'wdbc.csv'n", "n", "dataframe = read_csv(filename)n", "array = dataframe.valuesn", "n", "n", "X1 = array[:,:-1]n", "Y1 = array[:,-1]n", "scaler = StandardScaler().fit(X1)n", "rescaledX = scaler.transform(X1)n", "X1= rescaledXn", "n", "def dr_pca():n", " global X1n", " pca = PCA(n_components=18)n", " X1=pca.fit_transform(X1)n", "n", "def dr_fa():n", " global X1n", " fa = FactorAnalysis(n_components=18, random_state=0)n", " X1 = fa.fit_transform(X1)n", " n", "def dr_lda():n", " global X1n", " #lda = LinearDiscriminantAnalysis(n_components=18)n", " #ValueError: n_components cannot be larger than min(n_features, n_classes – 1).n", " #CORRECT ONE BELOWn", " #lda = LinearDiscriminantAnalysis(n_components=1) n", " lda = LinearDiscriminantAnalysis()n", " X1=lda.fit_transform(X1,Y1)n", " n", "def dr_iso():n", " global X1n", " iso = Isomap(n_components=10)n", " X1 = iso.fit_transform(X1)n", " n", "def dr_lle():n", " global X1n", " lle = LocallyLinearEmbedding(n_components=18)n", " X1 = lle.fit_transform(X1)n", " n", "n", "print("""n", "1-PCAn", "2-FAn", "3-LDAn", "4-ISOn", "5-LLEn", """")n", "n", "choice=int(input("Enter your choice: "))n", "n", "Dimensionality_Reduction = [dr_pca,dr_fa,dr_lda,dr_iso,dr_lle]n", "n", "output=Dimensionality_Reduction[choice-1]()n", "n", "n", "kfold = model_selection.KFold(n_splits=10, random_state=None)n", "n", "def cl_NB():n", " global modeln", " model = GaussianNB() n", "n", "def cl_KNN():n", " global modeln", " model = KNeighborsClassifier()n", "n", "def cl_LR():n", " global modeln", " model = LogisticRegression()n", " n", "def cl_DT():n", " global modeln", " model = DecisionTreeClassifier()n", "n", "def cl_RF():n", " global modeln", " model = RandomForestClassifier(n_estimators=150)n", " n", "print("""n", "1-NBn", "2-KNNn", "3-LRn", "4-DTn", "5-RFn", """")n", "n", "choice=int(input("Enter your choice: "))n", "n", "Classifier_Choice = [cl_NB,cl_KNN,cl_LR,cl_DT,cl_RF]n", "n", "output=Classifier_Choice[choice-1]()n", "n", "results = model_selection.cross_val_score(model, X1, Y1, cv=kfold)n", "n", "n", "print (results.mean())n", "n", "n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4}
