ML model examples

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_context('notebook', font_scale=1.5)

Dimension reduction

from sklearn.datasets import load_breast_cancer

bc = load_breast_cancer(as_frame=True)

bc.data.head()

	mean radius	mean texture	mean perimeter	mean area	mean smoothness	mean compactness	mean concavity	mean concave points	mean symmetry	mean fractal dimension	...	worst radius	worst texture	worst perimeter	worst area	worst smoothness	worst compactness	worst concavity	worst concave points	worst symmetry	worst fractal dimension
0	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	0.2419	0.07871	...	25.38	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	0.1812	0.05667	...	24.99	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902
2	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	0.2069	0.05999	...	23.57	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758
3	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	0.2597	0.09744	...	14.91	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300
4	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	0.1809	0.05883	...	22.54	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678

5 rows × 30 columns

bc.target_names

array(['malignant', 'benign'], dtype='<U9')

bc.target.head()

0    0
1    0
2    0
3    0
4    0
Name: target, dtype: int64

! python3 -m pip install --quiet umap-learn
! python3 -m pip install --quiet phate

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from umap import UMAP
from phate import PHATE

dr_models = {
    'PCA': PCA(),
    't-SNE': TSNE(),
    'UMAP': UMAP(),
    'PHATE': PHATE(verbose=0),
}

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

fig, axes = plt.subplots(2,2,figsize=(8,8))
axes = axes.ravel()

for i, (k, v) in enumerate(dr_models.items()):
    X = v.fit_transform(scaler.fit_transform(bc.data))
    target = bc.target
    ax = axes[i]
    ax.scatter(X[:, 0], X[:, 1], c=target)
    ax.set_xlabel(f'{k}1')
    ax.set_ylabel(f'{k}2')
    ax.set_xticks([])
    ax.set_yticks([])

A3.2 Clustering

• K-means

• Agglomerative hierarchical clustering

• Mixture models

from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN
from sklearn.mixture import GaussianMixture

cl_models = {
    'true': None,
    'k-means': KMeans(n_clusters=2),
    'ahc': AgglomerativeClustering(n_clusters=2),
    'gmm': GaussianMixture(n_components=2),
}

pca = PCA()
X = pca.fit_transform(scaler.fit_transform(bc.data))

fig, axes = plt.subplots(2,2,figsize=(8, 8))
axes = axes.ravel()

for i, (k, v) in enumerate(cl_models.items()):
    if i == 0:
        y = bc.target
    else:
        y = v.fit_predict(scaler.fit_transform(bc.data))
    target = y
    ax = axes[i]
    ax.scatter(X[:, 0], X[:, 1], c=target)
    ax.set_xlabel('PC1')
    ax.set_ylabel('PC2')
    ax.set_xticks([])
    ax.set_yticks([])
    ax.set_title(k)

A3.3 Supervised learning

• Nearest neighbor

• Linear models

• Support vector machines

• Trees

• Neural networks

from sklearn.model_selection import train_test_split

from sklearn.dummy import DummyClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier

Proprocess data

X = bc.data
y = bc.target

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, stratify=y)

X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

pd.Series(y_test).value_counts(normalize=True)

1    0.629371
0    0.370629
Name: target, dtype: float64

sl_modles = dict(
    dummy = DummyClassifier(strategy='prior'),
    knn = KNeighborsClassifier(),
    lr = LogisticRegression(),
    svc = SVC(),
    nn = MLPClassifier(max_iter=500),
)

for name, clf in sl_modles.items():
    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)
    print(f'{name}: {score:.3f}')

dummy: 0.629
knn: 0.951
lr: 0.958
svc: 0.958
nn: 0.958