Predicting the Localization Sites of Proteins

預測蛋白質的細胞定位位點分析(Cellular Localization Sites of Proteins Analysing)和可視化數據集及其特徵，特徵選擇，各種機器學習分類模型/估計器(包括ensemble)。 和深度學習多層感知器架構(人工神經網絡)模型。 所有訓練/擬合交叉驗證。

import numpy
import pandas
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from sklearn.ensemble import ExtraTreesClassifier

import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix

from sklearn.model_selection import cross_val_score

from sklearn.model_selection import KFold
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.utils import np_utils
#from sklearn import preprocessing
from sklearn.model_selection import StratifiedKFold
from keras.constraints import maxnorm

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

# load dataset
dataframe = pandas.read_csv("./input/ecoli.csv", delim_whitespace=True)

# Assign names to Columns
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']

dataframe = dataframe.drop('seq_name', axis=1)

# Encode Data
dataframe.site.replace(('cp', 'im', 'pp', 'imU', 'om', 'omL', 'imL', 'imS'),(1,2,3,4,5,6,7,8), inplace=True)

print("Head:", dataframe.head())

print("Statistical Description:", dataframe.describe())

print("Shape:", dataframe.shape)

print("Data Types:", dataframe.dtypes)

print("Correlation:", dataframe.corr(method='pearson'))

'mcg'（McGeoch的信號序列識別方法）與'site'（蛋白定位位點）具有最高的相關性（這是正相關），其次是'gvh'（von Heijne的信號序列識別方法），它也是 正相關，'alm2'（從序列中排除推定的可切割信號區域後ALOM程序的分數）具有最小的相關性

dataset = dataframe.values


X = dataset[:,0:7]
Y = dataset[:,7] 

print(X.shape)
print(type(X))
print(type(X[0]))
print(type(X[0][0]))

(335, 7)
<class 'numpy.ndarray'>
<class 'numpy.ndarray'>
<class 'numpy.float64'>

#Feature Selection
model = LogisticRegression(multi_class='auto', solver='lbfgs')
rfe = RFE(model, 3)
fit = rfe.fit(X, Y)

print("Number of Features: ", fit.n_features_)
print("Selected Features: ", fit.support_)
print("Feature Ranking: ", fit.ranking_) 

'mcg'，'gvh'和'alm1'（ALOM膜跨越區域預測程序的分數）是使用遞歸特徵消除(Recursive Feature Elimination)來預測“ Income”的前3個選定特徵/特徵組合，第1和2個通常是兩個屬性 與“site”類的最高相關性

plt.hist((dataframe.site))

大多數數據集的樣本都在'cp'(細胞質)，'im'(沒有信號序列的內膜)和'pp'(perisplasm)輸出類別的順序內

dataframe.plot(kind='density', subplots=True, layout=(3,4), sharex=False, sharey=False)

除了'mcg'和'aac'之外，大多數屬性都有正偏差(positive skews)

dataframe.plot(kind='box', subplots=True, layout=(3,4), sharex=False, sharey=False)

fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(dataframe.corr(), vmin=-1, vmax=1)
fig.colorbar(cax)
ticks = numpy.arange(0,7,1)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xticklabels(dataframe.columns)
ax.set_yticklabels(dataframe.columns)

'mcg'具有最高的正相關性

num_instances = len(X)

models = []
models.append(('LR', LogisticRegression(multi_class='auto', solver='lbfgs')))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC(gamma="auto")))
models.append(('L_SVM', LinearSVC()))
models.append(('ETC', ExtraTreesClassifier(n_estimators=100)))
models.append(('RFC', RandomForestClassifier(n_estimators=100)))

# Evaluations
results = []
names = []

for name, model in models:
    # Fit the model
    model.fit(X, Y)
    
    predictions = model.predict(X)
    
    # Evaluate the model
    kfold = cross_validation.KFold(n=num_instances, n_folds=10, random_state=seed)
    cv_results = cross_validation.cross_val_score(model, X, Y, cv=kfold, scoring='accuracy')
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

#boxplot algorithm Comparison
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()

'Naive Bayes'和'Linear Discriminant Analysis'是此數據集的最佳估算器/模型，可以進一步探索它們並調整其超參數

# Define 10-fold Cross Valdation Test Harness
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
cvscores = []
for train, test in kfold.split(X, Y):

    # create model
    model = Sequential()
    model.add(Dense(20, input_dim=7, kernel_initializer='uniform', activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(10, kernel_initializer='uniform', activation='relu', kernel_constraint=maxnorm(3)))
    model.add(Dropout(0.2))
    model.add(Dense(5, kernel_initializer='uniform', activation='relu'))
    model.add(Dense(1, kernel_initializer='uniform', activation='relu'))

    # Compile model
    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])

    # Fit the model
    model.fit(X[train], Y[train], epochs=200, batch_size=10, verbose=0)

    # Evaluate the model
    scores = model.evaluate(X[test], Y[test], verbose=0)
    print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
cvscores.append(scores[1] * 100)
print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores), numpy.std(cvscores)))

參考
https://www.kaggle.com/elikplim/predicting-the-localization-sites-of-proteins