如何使用 sklearn 提高 MultinomialNB() 的准确度分数并使用 matplotlib 在图形中可视化结果？
How can i increase MultinomialNB()'s accuracy score using sklearn and visualize the result in graph using matplotlib?
我正在处理一个如下所示的数据集：
在我附上的屏幕截图中，您可以看到我的数据集包含 16 行和 12 个元组，但实际上它包含 521 行和 12 个元组。
- 第 1 列是："Menarche start early"
- 第 2 列："Oral Contraception"
- 第 3 列："Diet Maintain"
- 第 4 列："Affected By Breast Cancer"
- 第 5 列："Affected By cervical Cancer?"
- 第 6 列："Cancer History In family?"
- 第 7 列："Education?"
- 第 8 列："Age of Husband"
- 第 9 列："Menopause End age?"
- 第 10 列："Food contains high fat?"
- 第 11 列："Abortion?"
- 第 12 列："Affected by ovarian Cancer?".
此处所有列都包含分类变量。因此，我使用 LabelEncoder 和 OneHotEncoder 对数据集进行了预处理，为了避免虚拟变量陷阱，我删除了创建超过 2 列的虚拟变量的第一列。
然后我将数据集分成 test_size = 0.25 和 random_state = 18 的两部分，然后我将 X_train 和 y_train 安装到 MultinomialNB( ) 并得到准确分数 0.7938931297709924。
然后我构建了一些看起来像这样的学习曲线
和
这个
但最重要的是，我的模型给出了 R 平方的值：0.557 和 Adj。 R 平方：0.543，我认为这不是很好。
这是我的混淆矩阵
我希望 r 平方和 adj r 平方值都在 1 左右，但我不明白如何有效地做到这一点，因为我是这个领域的新手，之前没有使用过包含所有分类变量但没有值的任何数据集，请帮助我使用朴素贝叶斯算法使我的模型更好，如果您在我的模型中发现任何错误，请告诉我并提供帮助，也请通过提供资源和教程+代码示例来帮助我构建数据可视化图我的 model.here 是我为这个项目编写的代码：
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#Importing the dataset
dataset = pd.read_csv('RiskFactor.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 11].values
#dummy_x = dataset.iloc[:, [0,6,7,8]].values
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
label_x = LabelEncoder()
X[:,0] = label_x.fit_transform(X[:,0] ) #Menarche start early
label_x = LabelEncoder()
X[:,1] = label_x.fit_transform(X[:,1] )
label_x = LabelEncoder()
X[:,2] = label_x.fit_transform(X[:,2] )
label_x = LabelEncoder()
X[:,3] = label_x.fit_transform(X[:,3] )
label_x = LabelEncoder()
X[:,4] = label_x.fit_transform(X[:,4] )
label_x = LabelEncoder()
X[:,5] = label_x.fit_transform(X[:,5] )
label_x = LabelEncoder()
X[:,6] = label_x.fit_transform(X[:,6] ) #Education
label_x = LabelEncoder()
X[:,7] = label_x.fit_transform(X[:,7] ) #Age of Husband
label_x = LabelEncoder()
X[:,8] = label_x.fit_transform(X[:,8] ) #Menopause End age?
label_x = LabelEncoder()
X[:,9] = label_x.fit_transform(X[:,9] )
label_x = LabelEncoder()
X[:,10] = label_x.fit_transform(X[:,10] )
onehotencoder = OneHotEncoder(categorical_features = "all")
X = onehotencoder.fit_transform(X).toarray()
#avoiding dummy variable trap by removing extra columns
X = X[: ,[1,2,3,4,5,6,7,8,9,10,11,12,14,15,17,18,20,21,22,23,24,25,26]]
# Encoding the Dependent Variable
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.25,
random_state = 18)
from sklearn.naive_bayes import GaussianNB,BernoulliNB,MultinomialNB
classifier = MultinomialNB()
classifier.fit(X_train, y_train)
print(classifier)
y_expect = y_test
#predicting the test set result
y_pred = classifier.predict(X_test)
#Making the Confusion Matrix
from sklearn.metrics import confusion_matrix,accuracy_score
cm = confusion_matrix (y_test, y_pred)
print(accuracy_score(y_expect,y_pred))
# finding P value from statsmodels
import statsmodels.formula.api as sm
regressor_OLS = sm.OLS(endog=y,exog = X).fit()
regressor_OLS.summary()
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
"""
Generate a simple plot of the test and training learning curve.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
title : string
Title for the chart.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
ylim : tuple, shape (ymin, ymax), optional
Defines minimum and maximum yvalues plotted.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, if ``y`` is binary or multiclass,
:param train_sizes:
:class:`StratifiedKFold` used. If the estimator is not a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validators that can be used here.
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).
"""
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
label="Cross-validation score")
plt.legend(loc="best")
return plt
estimator = MultinomialNB()
title = "Learning Curves (Naive Bayes classifier ALGORITHM)"
# Cross validation with 100 iterations to get smoother mean test and train
# score curves, each time with 20% data randomly selected as a validation
#set.
cv = ShuffleSplit(n_splits=100, test_size=0.25, random_state=17)
#cv = None
plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv,
n_jobs=1)
plt.show()
I've solved this problem by using PCA ,here is the code :
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 31 22:38:32 2018
@author: MOBASSIR
"""
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#Importing the dataset
dataset = pd.read_csv('ovarian.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 11].values
#dummy_x = dataset.iloc[:, [0,6,7,8]].values
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
label_x1 = LabelEncoder()
X[:,0] = label_x1.fit_transform(X[:,0] ) #Menarche start early
label_x2 = LabelEncoder()
X[:,1] = label_x2.fit_transform(X[:,1] )
label_x3 = LabelEncoder()
X[:,2] = label_x3.fit_transform(X[:,2] )
label_x4 = LabelEncoder()
X[:,3] = label_x4.fit_transform(X[:,3] )
label_x5 = LabelEncoder()
X[:,4] = label_x5.fit_transform(X[:,4] )
label_x6 = LabelEncoder()
X[:,5] = label_x6.fit_transform(X[:,5] )
label_x7 = LabelEncoder()
X[:,6] = label_x7.fit_transform(X[:,6] ) #Education
label_x8 = LabelEncoder()
X[:,7] = label_x8.fit_transform(X[:,7] ) #Age of Husband
label_x9 = LabelEncoder()
X[:,8] = label_x9.fit_transform(X[:,8] ) #Menopause End age?
label_x10 = LabelEncoder()
X[:,9] = label_x10.fit_transform(X[:,9] )
label_x11 = LabelEncoder()
X[:,10] = label_x11.fit_transform(X[:,10] )
onehotencoder = OneHotEncoder(categorical_features = [0,6,7,8])
X = onehotencoder.fit_transform(X).toarray()
# Avoiding the Dummy Variable Trap
"""
idx_to_delete = [0, 13, 16, 19]
X = [i for i in range(X.shape[-1]) if i not in idx_to_delete]
X = X[:, 1:]
df = pd.DataFrame(X, dtype='float64')
df = pd.to_numeric(X)
"""
#avoiding dummy variable trap by removing extra columns
#X = X[: ,[1,2,3,4,5,6,7,8,9,10,11,12,14,15,17,18,20,21,22,23,24,25,26]]
"""
#4,8,10,12,18,21,22,23 for dropped columns
#5,9,11,13,19,22,23,24 for dropped columns
#1,4,5,6 == 2,5,6,7
X = X[: ,[9,11,23,24]]
"""
#24,21,19,18,17,14,12,10,8,7,6 ,4 ,3 ,2,1 for undropped column
#25,22,20,19,18,15,13,11,9,8,7 ,5 ,4 ,3,2
#2,5,6,8,12,15
X = X[: ,[9,13,16,18,19]]
# Encoding the Dependent Variable
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)
"""
onehotencoder = OneHotEncoder()
y= onehotencoder.fit_transform(y).toarray()
"""
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Applying PCA
from sklearn.decomposition import PCA
pca = PCA(n_components = 2)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)
explained_variance = pca.explained_variance_ratio_
#Applying naive bayes classifier
from sklearn.naive_bayes import GaussianNB,BernoulliNB,MultinomialNB
classifier = BernoulliNB()
classifier.fit(X_train, y_train)
print(classifier)
y_expect = y_test
#predicting the test set result
y_pred = classifier.predict(X_test)
#Making the Confusion Matrix
from sklearn.metrics import confusion_matrix,accuracy_score
cm = confusion_matrix (y_test, y_pred)
print(accuracy_score(y_expect,y_pred))
# finding P value from statsmodels
import statsmodels.formula.api as sm
regressor_OLS = sm.OLS(endog=y,exog = X).fit()
regressor_OLS.summary()
from sklearn.cross_validation import cross_val_score
ck = BernoulliNB()
scores = cross_val_score(ck,X,y,cv=10, scoring='accuracy')
print (scores)
print (scores.mean())
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
'''Generate a simple plot of the test and training learning curve.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
title : string
Title for the chart.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
ylim : tuple, shape (ymin, ymax), optional
Defines minimum and maximum yvalues plotted.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, if ``y`` is binary or multiclass,
:param train_sizes:
:class:`StratifiedKFold` used. If the estimator is not a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validators that can be used here.
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).'''
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
label="Cross-validation score")
plt.legend(loc="best")
return plt
estimator = BernoulliNB()
title = "Learning Curves (Naive Bayes classifier ALGORITHM)"
# Cross validation with 100 iterations to get smoother mean test and train
# score curves, each time with 20% data randomly selected as a validation set.
cv = ShuffleSplit(n_splits=100, test_size=0.25, random_state=0)
plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv, n_jobs=1)
plt.show()
#End of Bayes theorem
plt.rcParams['font.size'] = 14
plt.hist(y_pred, bins = 8)
plt.xlim(0, 1)
plt.title('Predicted probabilities')
plt.xlabel('Affected by ovarian cancer?(predicted)')
plt.ylabel('frequency')
from sklearn.metrics import recall_score,precision_score
recall_score(y_test,y_pred,average='macro')
precision_score(y_test, y_pred, average='micro')
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Naive Bayes (Training set)')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()
# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Naive Bayes (Test set)')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()
我正在处理一个如下所示的数据集：
在我附上的屏幕截图中，您可以看到我的数据集包含 16 行和 12 个元组，但实际上它包含 521 行和 12 个元组。
- 第 1 列是："Menarche start early"
- 第 2 列："Oral Contraception"
- 第 3 列："Diet Maintain"
- 第 4 列："Affected By Breast Cancer"
- 第 5 列："Affected By cervical Cancer?"
- 第 6 列："Cancer History In family?"
- 第 7 列："Education?"
- 第 8 列："Age of Husband"
- 第 9 列："Menopause End age?"
- 第 10 列："Food contains high fat?"
- 第 11 列："Abortion?"
- 第 12 列："Affected by ovarian Cancer?".
此处所有列都包含分类变量。因此，我使用 LabelEncoder 和 OneHotEncoder 对数据集进行了预处理，为了避免虚拟变量陷阱，我删除了创建超过 2 列的虚拟变量的第一列。
然后我将数据集分成 test_size = 0.25 和 random_state = 18 的两部分，然后我将 X_train 和 y_train 安装到 MultinomialNB( ) 并得到准确分数 0.7938931297709924。
然后我构建了一些看起来像这样的学习曲线
这是我的混淆矩阵
我希望 r 平方和 adj r 平方值都在 1 左右，但我不明白如何有效地做到这一点，因为我是这个领域的新手，之前没有使用过包含所有分类变量但没有值的任何数据集，请帮助我使用朴素贝叶斯算法使我的模型更好，如果您在我的模型中发现任何错误，请告诉我并提供帮助，也请通过提供资源和教程+代码示例来帮助我构建数据可视化图我的 model.here 是我为这个项目编写的代码：
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#Importing the dataset
dataset = pd.read_csv('RiskFactor.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 11].values
#dummy_x = dataset.iloc[:, [0,6,7,8]].values
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
label_x = LabelEncoder()
X[:,0] = label_x.fit_transform(X[:,0] ) #Menarche start early
label_x = LabelEncoder()
X[:,1] = label_x.fit_transform(X[:,1] )
label_x = LabelEncoder()
X[:,2] = label_x.fit_transform(X[:,2] )
label_x = LabelEncoder()
X[:,3] = label_x.fit_transform(X[:,3] )
label_x = LabelEncoder()
X[:,4] = label_x.fit_transform(X[:,4] )
label_x = LabelEncoder()
X[:,5] = label_x.fit_transform(X[:,5] )
label_x = LabelEncoder()
X[:,6] = label_x.fit_transform(X[:,6] ) #Education
label_x = LabelEncoder()
X[:,7] = label_x.fit_transform(X[:,7] ) #Age of Husband
label_x = LabelEncoder()
X[:,8] = label_x.fit_transform(X[:,8] ) #Menopause End age?
label_x = LabelEncoder()
X[:,9] = label_x.fit_transform(X[:,9] )
label_x = LabelEncoder()
X[:,10] = label_x.fit_transform(X[:,10] )
onehotencoder = OneHotEncoder(categorical_features = "all")
X = onehotencoder.fit_transform(X).toarray()
#avoiding dummy variable trap by removing extra columns
X = X[: ,[1,2,3,4,5,6,7,8,9,10,11,12,14,15,17,18,20,21,22,23,24,25,26]]
# Encoding the Dependent Variable
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.25,
random_state = 18)
from sklearn.naive_bayes import GaussianNB,BernoulliNB,MultinomialNB
classifier = MultinomialNB()
classifier.fit(X_train, y_train)
print(classifier)
y_expect = y_test
#predicting the test set result
y_pred = classifier.predict(X_test)
#Making the Confusion Matrix
from sklearn.metrics import confusion_matrix,accuracy_score
cm = confusion_matrix (y_test, y_pred)
print(accuracy_score(y_expect,y_pred))
# finding P value from statsmodels
import statsmodels.formula.api as sm
regressor_OLS = sm.OLS(endog=y,exog = X).fit()
regressor_OLS.summary()
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
"""
Generate a simple plot of the test and training learning curve.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
title : string
Title for the chart.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
ylim : tuple, shape (ymin, ymax), optional
Defines minimum and maximum yvalues plotted.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, if ``y`` is binary or multiclass,
:param train_sizes:
:class:`StratifiedKFold` used. If the estimator is not a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validators that can be used here.
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).
"""
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
label="Cross-validation score")
plt.legend(loc="best")
return plt
estimator = MultinomialNB()
title = "Learning Curves (Naive Bayes classifier ALGORITHM)"
# Cross validation with 100 iterations to get smoother mean test and train
# score curves, each time with 20% data randomly selected as a validation
#set.
cv = ShuffleSplit(n_splits=100, test_size=0.25, random_state=17)
#cv = None
plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv,
n_jobs=1)
plt.show()
I've solved this problem by using PCA ,here is the code :
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 31 22:38:32 2018
@author: MOBASSIR
"""
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#Importing the dataset
dataset = pd.read_csv('ovarian.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 11].values
#dummy_x = dataset.iloc[:, [0,6,7,8]].values
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
label_x1 = LabelEncoder()
X[:,0] = label_x1.fit_transform(X[:,0] ) #Menarche start early
label_x2 = LabelEncoder()
X[:,1] = label_x2.fit_transform(X[:,1] )
label_x3 = LabelEncoder()
X[:,2] = label_x3.fit_transform(X[:,2] )
label_x4 = LabelEncoder()
X[:,3] = label_x4.fit_transform(X[:,3] )
label_x5 = LabelEncoder()
X[:,4] = label_x5.fit_transform(X[:,4] )
label_x6 = LabelEncoder()
X[:,5] = label_x6.fit_transform(X[:,5] )
label_x7 = LabelEncoder()
X[:,6] = label_x7.fit_transform(X[:,6] ) #Education
label_x8 = LabelEncoder()
X[:,7] = label_x8.fit_transform(X[:,7] ) #Age of Husband
label_x9 = LabelEncoder()
X[:,8] = label_x9.fit_transform(X[:,8] ) #Menopause End age?
label_x10 = LabelEncoder()
X[:,9] = label_x10.fit_transform(X[:,9] )
label_x11 = LabelEncoder()
X[:,10] = label_x11.fit_transform(X[:,10] )
onehotencoder = OneHotEncoder(categorical_features = [0,6,7,8])
X = onehotencoder.fit_transform(X).toarray()
# Avoiding the Dummy Variable Trap
"""
idx_to_delete = [0, 13, 16, 19]
X = [i for i in range(X.shape[-1]) if i not in idx_to_delete]
X = X[:, 1:]
df = pd.DataFrame(X, dtype='float64')
df = pd.to_numeric(X)
"""
#avoiding dummy variable trap by removing extra columns
#X = X[: ,[1,2,3,4,5,6,7,8,9,10,11,12,14,15,17,18,20,21,22,23,24,25,26]]
"""
#4,8,10,12,18,21,22,23 for dropped columns
#5,9,11,13,19,22,23,24 for dropped columns
#1,4,5,6 == 2,5,6,7
X = X[: ,[9,11,23,24]]
"""
#24,21,19,18,17,14,12,10,8,7,6 ,4 ,3 ,2,1 for undropped column
#25,22,20,19,18,15,13,11,9,8,7 ,5 ,4 ,3,2
#2,5,6,8,12,15
X = X[: ,[9,13,16,18,19]]
# Encoding the Dependent Variable
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)
"""
onehotencoder = OneHotEncoder()
y= onehotencoder.fit_transform(y).toarray()
"""
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Applying PCA
from sklearn.decomposition import PCA
pca = PCA(n_components = 2)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)
explained_variance = pca.explained_variance_ratio_
#Applying naive bayes classifier
from sklearn.naive_bayes import GaussianNB,BernoulliNB,MultinomialNB
classifier = BernoulliNB()
classifier.fit(X_train, y_train)
print(classifier)
y_expect = y_test
#predicting the test set result
y_pred = classifier.predict(X_test)
#Making the Confusion Matrix
from sklearn.metrics import confusion_matrix,accuracy_score
cm = confusion_matrix (y_test, y_pred)
print(accuracy_score(y_expect,y_pred))
# finding P value from statsmodels
import statsmodels.formula.api as sm
regressor_OLS = sm.OLS(endog=y,exog = X).fit()
regressor_OLS.summary()
from sklearn.cross_validation import cross_val_score
ck = BernoulliNB()
scores = cross_val_score(ck,X,y,cv=10, scoring='accuracy')
print (scores)
print (scores.mean())
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
'''Generate a simple plot of the test and training learning curve.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
title : string
Title for the chart.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
ylim : tuple, shape (ymin, ymax), optional
Defines minimum and maximum yvalues plotted.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, if ``y`` is binary or multiclass,
:param train_sizes:
:class:`StratifiedKFold` used. If the estimator is not a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validators that can be used here.
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).'''
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
label="Cross-validation score")
plt.legend(loc="best")
return plt
estimator = BernoulliNB()
title = "Learning Curves (Naive Bayes classifier ALGORITHM)"
# Cross validation with 100 iterations to get smoother mean test and train
# score curves, each time with 20% data randomly selected as a validation set.
cv = ShuffleSplit(n_splits=100, test_size=0.25, random_state=0)
plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv, n_jobs=1)
plt.show()
#End of Bayes theorem
plt.rcParams['font.size'] = 14
plt.hist(y_pred, bins = 8)
plt.xlim(0, 1)
plt.title('Predicted probabilities')
plt.xlabel('Affected by ovarian cancer?(predicted)')
plt.ylabel('frequency')
from sklearn.metrics import recall_score,precision_score
recall_score(y_test,y_pred,average='macro')
precision_score(y_test, y_pred, average='micro')
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Naive Bayes (Training set)')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()
# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Naive Bayes (Test set)')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()