AI02, Dimensionality reduction

from sklearn import datasets
from sklearn.decomposition import PCA
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

iris=datasets.load_iris()
X=iris.data[:,[0,2]]
y=iris.target
feature_names=[iris.feature_names[0],iris.feature_names[2]]
df_X=pd.DataFrame(X)
df_Y=pd.DataFrame(y)

pca=PCA(n_components=2)
pca.fit(X)
PCscore=pca.transform(X)
eigens_v=pca.components_.transpose()

mX=np.matrix(X)
for i in range(X.shape[1]):
    mX[:,i]=mX[:,i]-np.mean(X[:,i])
dfmX=pd.DataFrame(mX)

plt.scatter(dfmX[0],dfmX[1])
origin = [0], [0] # origin point
plt.quiver(*origin, eigens_v[0,:], eigens_v[1,:], color=['r','b'], scale=3)
plt.show()

X2 = iris.data
pca2 = PCA(n_components=4)
pca2.fit(X2)
pca2.explained_variance_
PCs=pca2.transform(X2)[:,0:2]


from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix

clf2 = LogisticRegression(solver="sag",multi_class="multinomial").fit(PCs,y)
confusion_matrix(y,clf2.predict(PCs))

import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
y = np.array([1, 1, 1, 2, 2, 2])

clf=LinearDiscriminantAnalysis()
clf.fit(X,y)
print(clf.predict([[-0.8, -1]]))

import numpy as np
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
y = np.array([1, 1, 1, 2, 2, 2])

clf2=QuadraticDiscriminantAnalysis()
clf2.fit(X,y)
print(clf2.predict([[-0.8, -1]]))

import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

from sklearn.datasets import make_moons, make_circles, make_classification
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt

h=0.2
names = ["LDA", "QDA"]
classifiers = [
    LinearDiscriminantAnalysis(),
    QuadraticDiscriminantAnalysis()]

X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                           random_state=1, n_clusters_per_class=1)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)

datasets = [make_moons(noise=0.3, random_state=0),
            make_circles(noise=0.2, factor=0.5, random_state=1),
            linearly_separable
            ]

figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
for ds in datasets:
    # preprocess dataset, split into training and test part
    X, y = ds
    X = StandardScaler().fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)

    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))

    # just plot the dataset first
    cm = plt.cm.RdBu
    cm_bright = ListedColormap(['#FF0000', '#0000FF'])
    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
    # Plot the training points
    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
    # and testing points
    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    ax.set_xticks(())
    ax.set_yticks(())
    i += 1

    # iterate over classifiers
    for name, clf in zip(names, classifiers):
        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
        clf.fit(X_train, y_train)
        score = clf.score(X_test, y_test)

        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, m_max]x[y_min, y_max].
        if hasattr(clf, "decision_function"):
            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
        else:
            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]

        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)

        # Plot also the training points
        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
        # and testing points
        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
                   alpha=0.6)

        ax.set_xlim(xx.min(), xx.max())
        ax.set_ylim(yy.min(), yy.max())
        ax.set_xticks(())
        ax.set_yticks(())
        ax.set_title(name)
        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
                size=15, horizontalalignment='right')
        i += 1

figure.subplots_adjust(left=.02, right=.98)
plt.show()