简介
通过基因表达监测（DNA微阵列）对新的癌症病例进行分类，从而为鉴定新的癌症类别和将肿瘤分配到已知类别提供了一般方法。这些数据用于对患有急性髓性白血病（AML）和急性淋巴细胞白血病（ALL）的患者进行分类。
代码实例
导入依赖库
import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inlineimport numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline
载入数据
labels_df = pd.read_csv('actual.csv', index_col = 'patient') test_df = pd.read_csv('data_set_ALL_AML_independent.csv') data_df = pd.read_csv('data_set_ALL_AML_train.csv') print('train_data: ', data_df.shape, '\n test_data: ', test_df.shape, '\n labels: ', labels_df.shape) # labels_df.shape data_df.head() #查看前5行（默认前5行）
清理数据
test_cols_to_drop = [c for c in test_df.columns if 'call' in c] test_df = test_df.drop(test_cols_to_drop, axis=1) test_df = test_df.drop(['Gene Description', 'Gene Accession Number'], axis=1 ) data_cols_to_drop = [c for c in data_df.columns if 'call' in c] data_df = data_df.drop(data_cols_to_drop, axis=1) data_df = data_df.drop(['Gene Description', 'Gene Accession Number'], axis=1 ) print('train_data ', data_df.shape, '\n test_data: ', test_df.shape, '\n labels: ', labels_df.shape) data_df.head()
定义'特征'和'样本'
使用基因表达值来预测癌症类型。 因此，特征是患者的基因和样本。 使用X作为输入数据，其中行是样本（患者），列是特征（基因）。
将'ALLL'替换为0，将'AML'替换为1。
labels_df = labels_df.replace({'ALL':0, 'AML':1}) train_labels = labels_df[labels_df.index <= 38] test_labels = labels_df[labels_df.index > 38] print(train_labels.shape, test_labels.shape) # labels_df.index test_df = test_df.T train_df = data_df.T
检查空值
print('Columns containing null values in train and test data are ', data_df.isnull().values.sum(), test_df.isnull().values.sum())
联合训练集和测试集
full_df = train_df.append(test_df, ignore_index=True) print(full_df.shape) full_df.head()
标准化和处理高维度
标准化
所有变量具有非常相似的范围的方式重新调整我们的变量（预测变量）。
高维度
只有72个样本和7000多个变量。 意味着如果采取正确的方法，模型很可能会受到HD的影响。 一个非常常见的技巧是将数据投影到较低维度空间，然后将其用作新变量。 最常见的尺寸减小方法是PCA。
# Standardization from sklearn import preprocessing X_std = preprocessing.StandardScaler().fit_transform(full_df) # Check how the standardized data look like gene_index = 1 print('mean is : ', np.mean(X_std[:, gene_index] ) ) print('std is :', np.std(X_std[:, gene_index])) fig= plt.figure(figsize=(10,10)) plt.hist(X_std[:, gene_index], bins=10) plt.xlim((-4, 4)) plt.xlabel('rescaled expression', size=30) plt.ylabel('frequency', size=30)
PCA(聚类分析)
# PCA from sklearn.decomposition import PCA pca = PCA(n_components=50, random_state=42) X_pca = pca.fit_transform(X_std) print(X_pca.shape)
cum_sum = pca.explained_variance_ratio_.cumsum() cum_sum = cum_sum*100 fig = plt.figure(figsize=(10,10)) plt.bar(range(50), cum_sum) plt.xlabel('PCA', size=30) plt.ylabel('Cumulative Explained Varaince', size=30) plt.title("Around 90% of variance is explained by the First 50 columns ", size=30)
labels = labels_df['cancer'].values colors = np.where(labels==0, 'red', 'blue') from mpl_toolkits.mplot3d import Axes3D plt.clf() fig = plt.figure(1, figsize=(15,15 )) ax = Axes3D(fig, elev=-150, azim=110,) ax.scatter(X_pca[:, 0], X_pca[:, 1], X_pca[:, 2], c=colors, cmap=plt.cm.Paired,linewidths=10) ax.set_title("First three PCA directions") ax.set_xlabel("PCA1") ax.w_xaxis.set_ticklabels([]) ax.set_ylabel("PCA2") ax.w_yaxis.set_ticklabels([]) ax.set_zlabel("PCA3") ax.w_zaxis.set_ticklabels([]) plt.show()
RF分类
X = X_pca y = labels print(X.shape, y.shape)
划分训练集和测试集
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) print(X_train.shape, y_train.shape)
import seaborn as sns from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score rfc = RandomForestClassifier(random_state=42) rfc.fit(X_train, y_train) y_pred = rfc.predict(X_test) print(accuracy_score(y_test, y_pred))
0.7083333333333334
重要特征
labs = ['PCA'+str(i+1) for i in range(X_train.shape[1])] importance_df = pd.DataFrame({ 'feature':labs, 'importance': rfc.feature_importances_ }) importance_df_sorted = importance_df.sort_values('importance', ascending=False) importance_df_sorted.head() fig = plt.figure(figsize=(25,10)) sns.barplot(data=importance_df_sorted, x='feature', y='importance') plt.xlabel('PCAs', size=30) plt.ylabel('Feature Importance', size=30) plt.title('RF Feature Importance', size=30) plt.savefig('RF Feature Importance.png', dpi=300) plt.show
Confusion Matrix（混淆矩阵）
Gradient Boost Classifier（梯度增强分类器）
from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred) ### Normalize cm, np.newaxis makes to devide each row by the sum cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print(np.newaxis) cmap=plt.cm.Blues plt.imshow(cm, interpolation='nearest', cmap=cmap) print(cm)
from sklearn.ensemble import GradientBoostingClassifier gbc = GradientBoostingClassifier(random_state=42) gbc.fit(X_train, y_train) y_gbc_pred = gbc.predict(X_test) print(accuracy_score(y_test, y_gbc_pred))
0.7083333333333334
