apogeum12/pca_pytorch_implementation

Own implementation PCA algorithm in pytorch with some own change like the automatic feature selection with help PCA method.

Example Uses LPCA

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