boundary_viz_bbb.py

# -*- coding: utf-8 -*-
"""boundary_Viz_bbb.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1rdlb5rlppmGpekp4MwooaNJd9_ZJsCbP
"""

# Commented out IPython magic to ensure Python compatibility.
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import os
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import multilabel_confusion_matrix
from statistics import mean
import math
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve, auc, roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import matthews_corrcoef
import seaborn as sns
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
from sklearn.manifold import TSNE
from __future__ import print_function
import time
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
# %matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns

# from google.colab import drive
# drive.mount('/content/drive/')
# %cd /content/drive/My Drive/urdu sentiments
train=pd.read_csv('/content/train_data77up.csv')
test=pd.read_csv('/content/test_data23up.csv')
df = pd.concat([train,test], axis=0)
df=df.reset_index(drop=True)

# train=pd.read_csv('trainHYBRID.csv')
# test=pd.read_csv('testHYBRID.csv')
# y_train=train['class']
# X_train=train.drop(['Unnamed: 0','class'],axis=1)
# y_test=test['class']
# X_test=test.drop(['Unnamed: 0','class'],axis=1)

from sklearn.preprocessing import StandardScaler
#dataset = pd.read_csv('df.csv', sep=',')
dataset = df
X1 = dataset.drop(['target'],axis=1)
Y1 =dataset['target']
X1 = X1.to_numpy()
Y1 = Y1.to_numpy()
std_scale = StandardScaler().fit(X1)
X1 = std_scale.transform(X1)
X1 = np.nan_to_num(X1.astype('float32'))
X=X1
y=Y1

# train=pd.read_csv('/content/premrna_train.csv')
# test=pd.read_csv('/content/premrna_test.csv')
df = pd.concat([train,test], axis=0)

df=df.reset_index()
# df=df.drop(['index'],axis=1)

# y_train=train['class']
# X_train=train.drop(['class'],axis=1)

# y_test=test['class']
# X_test=test.drop(['class'],axis=1)

# # df = pd.read_csv('ctgan.csv')
# df = df.sample(frac=1)

# y = df['class']

# x=df.drop(['class'],axis=1)

from sklearn.preprocessing import LabelEncoder
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
x = df.apply(le.fit_transform)

from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
scaler.get_params()
x=scaler.fit_transform(x)



pca_50 = PCA(n_components=50)
pca_result_50 = pca_50.fit_transform(X)
print('Cumulative explained variation for 50 principal components: {}'.format(np.sum(pca_50.explained_variance_ratio_)))

pca_result_50

time_start = time.time()
tsne = TSNE(n_components=2, verbose=0, perplexity=50, n_iter=300)
tsne_pca_results = tsne.fit_transform(pca_result_50)
print('t-SNE done! Time elapsed: {} seconds'.format(time.time()-time_start))

# visualise again and highlight actual classes of data

target_ids = range(len(y))

plt.figure(figsize=(10, 8))
colours = ['purple','orange' ]
label = ['TE','NON-TE' ]
for i, c, label in zip(target_ids, colours, label):
    plt.scatter(tsne_pca_results[y == i, 0], tsne_pca_results[y == i, 1], c=c, label=label, alpha=0.3, linewidths = 2 )
    pass

plt.legend()
plt.show()

# visualise again and highlight actual classes of data

target_ids = range(len(y))

plt.figure(figsize=(10, 8))
colours = ['purple','orange','g','yellow','red','black','m','r','y','teal','pink','green','y']
label = ['Copia','Gypsy','Pao','Line','L1','Sine','Harbinger','hAt','Mutator','Tc-Marnier','Cacta','Mite','Heli']
for i, c, label in zip(target_ids, colours, label):
    plt.scatter(tsne_pca_results[y == i, 0], tsne_pca_results[y == i, 1], c=c, label=label, alpha=0.3, linewidths = 2 )
    pass

plt.legend()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
import xgboost as xgb
from xgboost import XGBClassifier

h=0.2
names = [
    "Random Forest",
    "LGBM",
    "ET",
    "XGB",
]
from sklearn.linear_model import LogisticRegression

classifiers = [
    RandomForestClassifier(max_depth=5, n_estimators=100),
    LGBMClassifier(),
    ExtraTreesClassifier(),
    XGBClassifier() ,
LogisticRegression()
]

X, y =  tsne_pca_results,y
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_cnt, ds in enumerate(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=0.4, random_state=42
    )

    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
    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)
    if ds_cnt == 0:
        ax.set_title("Input data")
    # Plot the training points
    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")

    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, x_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=0.8)
# Plot the training points
        ax.scatter(
            X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k"
        )

         # Plot the testing points
        ax.scatter(
            X_test[:, 0],
            X_test[:, 1],
            c=y_test,
            cmap=cm_bright,
            edgecolors="k",
            alpha=0.6,
        )
        ax.set_xlim(xx.min(), xx.max())
        ax.set_ylim(yy.min(), yy.max())
        ax.set_xticks(())
        ax.set_yticks(())
        if ds_cnt == 0:
            ax.set_title(name)
        ax.text(
            xx.max() - 0.3,
            yy.min() + 0.3,
            ("%.2f" % score).lstrip("0"),
            size=15,
            horizontalalignment="right",
        )
        i += 1
plt.figure(figsize=(20, 20), dpi=600)

plt.tight_layout()
plt.show()



sns.scatterplot(
    x=tsne_pca_results[:, 0], y=tsne_pca_results[:, 1],
    hue=y,
    data=X,
    legend="full",
    alpha=0.3
).set(title="Feature Space Visualization of Raw Data")