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Update main.py #1

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224 changes: 34 additions & 190 deletions main.py
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import area as area
import inline as inline
import matplotlib



# PREPARATION
import sklearn
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import colors
from numpy import int64, float64
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler
#cleaning data nal values
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
from sklearn.decomposition import PCA
from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import HistGradientBoostingClassifier, VotingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import BernoulliNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
#scaling and normalizing data
from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler
#feature selection from data
from sklearn.feature_selection import SelectFromModel
from sklearn.feature_selection import selectpercentile
from sklearn.feature_selection import GenericUnivariateSelect
from sklearn.feature_selection import selectKbest
from sklearn.feature_selection import chi2



#main
seg = pd.read_csv("G:\\classification\\train.csv")
print(seg.isnull().any())
print(seg.shape())

# Encoding categorical data
encoding = LabelEncoder()
seg['Gender'] = encoding.fit_transform(seg['Gender'])

seg['Ever_Married'] = encoding.fit_transform(seg['Ever_Married'])

seg['Graduated'] = encoding.fit_transform(seg['Graduated'])

seg['Profession'] = encoding.fit_transform(seg['Profession'])

seg['Spending_Score'] = encoding.fit_transform(seg['Spending_Score'])

seg['Var_1'] = encoding.fit_transform(seg['Var_1'])

seg['Segmentation'] = encoding.fit_transform(seg['Segmentation'])

# preprocessing scaling
scale = MinMaxScaler(copy=True, feature_range=(0, 1))
a = np.array(seg['ID'], dtype=int64)
seg['ID'] = scale.fit_transform(a.reshape(-1, 1))
scale = StandardScaler(copy=True, feature_range=(0, 1))
a = np.array(seg['ID'], dtype=int64)
seg['ID'] = scale.fit_transform(a.reshape(-1, 1))

# Data cleaning
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
x1 = np.array(seg['Ever_Married'], dtype=int64)
seg['Ever_Married'] = imp.fit_transform(x1.reshape(-1, 1))

x2 = np.array(seg['Graduated'], dtype=int64)
seg['Graduated'] = imp.fit_transform(x2.reshape(-1, 1))

x3 = np.array(seg['Profession'], dtype=int64)
seg['Profession'] = imp.fit_transform(x3.reshape(-1, 1))

x4 = np.array(seg['Work_Experience'], dtype=int64)
seg['Work_Experience'] = imp.fit_transform(x4.reshape(-1, 1))

x5 = np.array(seg['Family_Size'], dtype=int64)
seg['Family_Size'] = imp.fit_transform(x5.reshape(-1, 1))

x6 = np.array(seg['Var_1'], dtype=int64)
seg['Var_1'] = imp.fit_transform(x6.reshape(-1, 1))

# print(seg.nunique())
# print(seg.shape)
# Declaration
print(seg.isnull().any())
X = seg.iloc[:, :10]
Y = seg.iloc[:, -1:]
# print(Y.head())

# feature selection from model
# select2 = SelectFromModel(RandomForestClassifier())
# Selected = select2.fit_transform(X, Y)
# print(Selected.shape)
# print(select2.get_support())

# # preprocessing MinMaxscaler
scale = MinMaxScaler(copy=True, feature_range=(0, 1))
a = np.array(seg['Age'], dtype=int64)
seg['Age'] = scale.fit_transform(a.reshape(-1, 1))

b = np.array(seg['Family_Size'], dtype=int64)
seg['Family_Size'] = scale.fit_transform(b.reshape(-1, 1))

# Splitting data to train and test
x_train, x_test, y_train, y_test = train_test_split(X, Y, shuffle=False, )
# =================================ENSEMLE==========================================
# Gradient Boosting Classifier
model1 = GradientBoostingClassifier(learning_rate=0.04)
# -------------------------------------------------------------
# Random forest classifier
model2 = RandomForestClassifier()

# =================================NORMAL===========================================
# Logistic regression
model3 = LogisticRegression()
# ---------------------------------------------------------------
# KNN
model4 = KNeighborsClassifier()
# ---------------------------------------------------------------
# Decision tree
model5 = DecisionTreeClassifier(max_depth=10)
# ---------------------------------------------------------------
# naive bayes
model6 = GaussianNB()
model7 = BernoulliNB()
# ---------------------------------------------------------------
# LDA
model8 = LinearDiscriminantAnalysis()
# ====================================
# model8.fit(x_train, y_train)
# # sorted(model11.cv_results_.keys())
# y_pred = model8.predict(x_train)
# print('my predictions:', y_pred)
# print('accuracy score is:', accuracy_score(y_train, y_pred))

# # ========================================PLOT============================================
# eclf = VotingClassifier(
# estimators=[('1', model1), ('2', model2), ('3', model3), ('5', model5), ('6', model6),
# ('7', model7), ('8', model8), ('9', model9), ('10', model10)], voting='hard')
# for clf, label in zip([model1, model2, model3, model5, model6, model7, model8, model9, model10, eclf],
# ['GradientBoostingClassifier', 'RandomForestClassifier','LogisticRegression',
# 'KNN', 'DecisionTreeClassifier', 'GaussianNB',
# 'BernoulliNB', 'LinearDiscriminantAnalysis', 'QuadraticDiscriminantAnalysis']):
# scores = cross_val_score(clf, X, Y, scoring='accuracy')
# print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))


# decision boundray
from sklearn.datasets import make_classification
from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt

X, Y = make_classification(n_samples=7165, n_features=2, n_informative=2, n_redundant=0, n_classes=2)
import matplotlib.gridspec as gridspec

gs = gridspec.GridSpec(4, 2)

fig = plt.figure(figsize=(20, 10))
plt.subplots_adjust(left=0.1,
bottom=0.1,
right=0.9,
top=0.9,
wspace=0.4,
hspace=0.4)

labels = ['GradientBoostingClassifier', 'RandomForestClassifier', 'LogisticRegression', 'KNeighborsClassifier',
'DecisionTreeClassifier', 'GaussianNB', 'BernoulliNB', 'LinearDiscriminantAnalysis']
for clf, lab, grd in zip([model1, model2, model3, model4, model5, model6, model7, model8],
labels,
[(0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1), (3, 0), (3, 1)]):
clf.fit(X, Y)
ax = plt.subplot(gs[grd[0], grd[1]])
fig = plot_decision_regions(X=X, y=Y, clf=clf, legend=2)
plt.title(lab)

plt.show()
# confusion matrix
# cm = confusion_matrix(y_train, y_pred)
# sns.heatmap(cm, center=True)
# plt.title('Segment it')
# plt.show()

# def plotGraph(y_train, y_pred, rand):
# if max(y_train) >= max(y_pred):
# my_range = int(max(y_train))
# else:
# my_range = int(max(y_pred))
# plt.scatter(range(len(y_train)), y_train, color='blue')
# plt.scatter(range(len(y_pred)), y_pred, color='red')
# plt.title(rand)
# plt.show()
# return
#
#
# plotGraph(y_train, y_pred, 'Decision tree')

# ============================================= GET TEST ==============================================
pre = pd.read_csv('G:\\classification\\test.csv')
#feature selection methods
seg = Selectpercentile(select_func = chi2 , percentile = 40)
seg = GenericUnivariateSelect(select_func = chi2 ,mode = 'k_best' , param = 4)
seg = SelectKBest(select_func = chi2 ,k = 5)
seg = SelectFromModel(estimator = LinearRegression(), max_features = none)
Selected = seg.fit_transform(X, Y)
print(Selected.shape)
print(seg.get_support())

# Encoding categorical data
encoding = LabelEncoder()
pre['Gender'] = encoding.fit_transform(pre['Gender'])

pre['Ever_Married'] = encoding.fit_transform(pre['Ever_Married'])

pre['Graduated'] = encoding.fit_transform(pre['Graduated'])

pre['Profession'] = encoding.fit_transform(pre['Profession'])

pre['Spending_Score'] = encoding.fit_transform(pre['Spending_Score'])

pre['Var_1'] = encoding.fit_transform(pre['Var_1'])

# Data cleaning
x11 = np.array(pre['Work_Experience'], dtype=int64)
pre['Work_Experience'] = imp.fit_transform(x11.reshape(-1, 1))

x22 = np.array(pre['Family_Size'], dtype=int64)
pre['Family_Size'] = imp.fit_transform(x22.reshape(-1, 1))

# preprocessing MinMaxscaler
# aa = np.array(pre['Age'], dtype=int64)
# pre['Age'] = scale.fit_transform(aa.reshape(-1, 1))
#
# bb = np.array(pre['Family_Size'], dtype=int64)
# pre['Family_Size'] = scale.fit_transform(bb.reshape(-1, 1))

# Check on the nan cells
# print(pre.columns[pre.isnull().any()].tolist())
# print(pre.isnull().any())
# # printing
# id = pre['ID'].values
# X2 = pre.iloc[:, :]
# y_pred2 = model1.predict(X2)
# final_frame = pd.DataFrame({'ID': id, 'Segmentation': y_pred2})
# print(final_frame)
# final_frame.to_csv('G:\\classifications.csv')
# print('pass')