Example Uses LPCA algorithm and compare with sklearn:
- TSNE
- KMeans
- PCA
# Import Data
# - Iris import
import pandas as pd
data_iris = pd.read_csv(filepath_or_buffer='https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',
header= None,
sep= ',')
data_iris.columns=['sepal_len', 'sepal_wid', 'petal_len', 'petal_wid', 'class']
data_iris.dropna(how="all", inplace=True) # drops the empty line at file-end
data_iris.info(verbose=True)
X_iris = data_iris.iloc[:,0:4].values
y_iris = data_iris.iloc[:,4].values
print("\nShape of X_iris: {} Shape of y_iris: {} \n".format(X_iris.shape, y_iris.shape))
# - Bank data import from file
data_bank = pd.read_csv('data/bank.csv',
sep=';')
# Columns is build in file no need to add to data
data_bank.dropna(how="all", inplace=True) # drops the empty line at file-end
data_bank.info(verbose=True)
X_bank = data_bank.iloc[:,0:16].values
y_bank = data_bank.iloc[:,16].values
print("\nShape of X_bank: {} Shape of y_bank: {} \n".format(X_bank.shape, y_bank.shape))<class 'pandas.core.frame.DataFrame'>
Int64Index: 150 entries, 0 to 149
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 sepal_len 150 non-null float64
1 sepal_wid 150 non-null float64
2 petal_len 150 non-null float64
3 petal_wid 150 non-null float64
4 class 150 non-null object
dtypes: float64(4), object(1)
memory usage: 7.0+ KB
Shape of X_iris: (150, 4) Shape of y_iris: (150,)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 4521 entries, 0 to 4520
Data columns (total 17 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 age 4521 non-null int64
1 job 4521 non-null object
2 marital 4521 non-null object
3 education 4521 non-null object
4 default 4521 non-null object
5 balance 4521 non-null int64
6 housing 4521 non-null object
7 loan 4521 non-null object
8 contact 4521 non-null object
9 day 4521 non-null int64
10 month 4521 non-null object
11 duration 4521 non-null int64
12 campaign 4521 non-null int64
13 pdays 4521 non-null int64
14 previous 4521 non-null int64
15 poutcome 4521 non-null object
16 y 4521 non-null object
dtypes: int64(7), object(10)
memory usage: 635.8+ KB
Shape of X_bank: (4521, 16) Shape of y_bank: (4521,)
# Preprocesin Data
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1, 2, 3, 4, 6, 7, 8, 10, 15])], remainder='passthrough')
X_bank = np.float64(ct.fit_transform(X_bank))
print("\n After Preprocesing data shape is: ")
print("Shape of X_bank: {} Shape of Y_bank: {}".format(X_bank.shape, y_bank.shape)) After Preprocesing data shape is:
Shape of X_bank: (4521, 51) Shape of Y_bank: (4521,)
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from pca.pca import LPCA # OWN Implementation
import seaborn
import time
_s = time.time()
X_tsne_iris = TSNE(n_components=2, n_jobs=-1).fit_transform(X_iris)
X_tsne_bank = TSNE(n_components=2, n_jobs=-1).fit_transform(X_bank)
X_KMns_iris = KMeans(n_clusters=2, random_state=0).fit_transform(X_iris)
X_KMns_bank = KMeans(n_clusters=2, random_state=0).fit_transform(X_bank)
X_pca_iris = PCA(n_components=2, random_state=0).fit_transform(X_iris)
X_pca_bank = PCA(n_components=2, random_state=0).fit_transform(X_bank)
entropy = 0.35
dt=0.05
X_s_iris, m_w_iris = LPCA(entropy, dt, gpu=False).l_pca(X_iris, C=False)
X_s_C_iris, m_w_C_iris, C_x_iris = LPCA(entropy, dt, gpu=False).l_pca(X_iris, C=True)
X_s_bank, m_w_bank = LPCA(entropy, dt, gpu=False).l_pca(X_bank, C=False)
X_s_C_bank, m_w_C_bank, C_x_bank = LPCA(entropy, dt, gpu=False).l_pca(X_bank, C=True)
_time = time.time() - _s
print("\n Finish in {}s".format(_time)) Finish in 91.68692302703857s
import matplotlib.pyplot as plt
import torch as t
def plot_print(X, y, name_plot,category, colors):
with plt.style.context('seaborn-whitegrid'):
plt.figure(figsize=(6, 4))
for lab, col in zip(category,colors):
plt.scatter(X[y==lab, 0],
X[y==lab, 1],
label=lab,
c=col)
plt.xlabel("{}".format(name_plot))
#plt.legend(loc='lower center')
plt.tight_layout()
plt.show()
category = ['Iris-setosa', 'Iris-virginica', 'Iris-versicolor']
colors = ['blue', 'red', 'green']
name_plot = "TSNE - IRIS DataSet"
plot_print(X_tsne_iris, y_iris, name_plot, category, colors)
name_plot = "KMeans - IRIS DataSet"
plot_print(X_KMns_iris, y_iris, name_plot, category, colors)
name_plot = "PCA - IRIS DataSet"
plot_print(X_pca_iris, y_iris, name_plot, category, colors)
name_plot = "LPCA - IRIS DataSet C - False"
X_s_iris = t.mm(X_s_iris, m_w_iris)
plot_print(X_s_iris, y_iris, name_plot, category, colors)
# With C - Correlate all Matrix
name_plot = "LPCA - IRIS DataSet C - True"
X_s_C_iris_pre = t.mm(X_s_C_iris, m_w_C_iris)
X_s_C_iris_out = t.mm(X_s_C_iris_pre, C_x_iris)
plot_print(X_s_C_iris_out, y_iris, name_plot, category, colors)
print("\n For Bank Dataset \n")
category = ['no', 'yes']
colors = ['blue', 'green']
name_plot = "TSNE - Bank DataSet"
plot_print(X_tsne_bank, y_bank, name_plot, category, colors)
name_plot = "KMeans - Bank DataSet"
plot_print(X_KMns_bank, y_bank, name_plot, category, colors)
name_plot = "PCA - Bank DataSet"
plot_print(X_pca_bank, y_bank, name_plot, category, colors)
name_plot = "LPCA - Bank DataSet C - False"
X_s_bank = t.mm(X_s_bank, m_w_bank)
plot_print(X_s_bank, y_bank, name_plot, category, colors)
# With C - Correlate all Matrix
name_plot = "LPCA - Bank DataSet C - True"
X_s_C_bank_pre = t.mm(X_s_C_bank, m_w_C_bank)
X_s_C_bank_out = t.mm(X_s_C_bank_pre, C_x_bank)
plot_print(X_s_C_bank_out, y_bank, name_plot, category, colors)
#Y = t.mm(t.mm(X_s, t.tensor(m_w[-1])), t.tensor(C.T)) IF C is True
For Bank Dataset
