使用 df.head（）檢視新的 DataFrame，以確保它符合預期。
使用 df.info（）可以瞭解每一列中的資料型別和資料量。可能需要根據需要轉換資料型別。
使用 df.isna（）確保沒有 NaN 值。可能需要根據需要處理缺失值或刪除行。
使用 df.describe（）可以瞭解每列的最小值、最大值、均值、中位數、標準差和四分位數範圍。
import pandas as pdfrom sklearn.datasets import load_breast_cancercolumns = ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension']dataset = load_breast_cancer()data = pd.DataFrame(dataset['data'], columns=columns)data['cancer'] = dataset['target']display(data.head())display(data.info())display(data.isna().sum())display(data.describe())
from sklearn.model_selection import train_test_splitX = data.drop('cancer', axis=1)y = data['cancer']X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.50, random_state = 2020, stratify=y)
import numpy as npfrom sklearn.preprocessing import StandardScalerss = StandardScaler()X_train_scaled = ss.fit_transform(X_train)X_test_scaled = ss.transform(X_test)y_train = np.array(y_train)
from sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import recall_scorerfc = RandomForestClassifier()rfc.fit(X_train_scaled, y_train)display(rfc.score(X_train_scaled, y_train))# 1.0
feats = {}for feature, importance in zip(data.columns, rfc_1.feature_importances_):feats[feature] = importanceimportances = pd.DataFrame.from_dict(feats, orient='index').rename(columns={0: 'Gini-Importance'})importances = importances.sort_values(by='Gini-Importance', ascending=False)importances = importances.reset_index()importances = importances.rename(columns={'index': 'Features'})sns.set(font_scale = 5)sns.set(style="whitegrid", color_codes=True, font_scale = 1.7)fig, ax = plt.subplots()fig.set_size_inches(30,15)sns.barplot(x=importances['Gini-Importance'], y=importances['Features'], data=importances, color='skyblue')plt.xlabel('Importance', fontsize=25, weight = 'bold')plt.ylabel('Features', fontsize=25, weight = 'bold')plt.title('Feature Importance', fontsize=25, weight = 'bold')display(plt.show())display(importances)
import matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.decomposition import PCApca_test = PCA(n_components=30)pca_test.fit(X_train_scaled)sns.set(style='whitegrid')plt.plot(np.cumsum(pca_test.explained_variance_ratio_))plt.xlabel('number of components')plt.ylabel('cumulative explained variance')plt.axvline(linewidth=4, color='r', linestyle = '--', x=10, ymin=0, ymax=1)display(plt.show())evr = pca_test.explained_variance_ratio_cvr = np.cumsum(pca_test.explained_variance_ratio_)pca_df = pd.DataFrame()pca_df['Cumulative Variance Ratio'] = cvrpca_df['Explained Variance Ratio'] = evrdisplay(pca_df.head(10))
pca = PCA(n_components=10)pca.fit(X_train_scaled)X_train_scaled_pca = pca.transform(X_train_scaled)X_test_scaled_pca = pca.transform(X_test_scaled)
pca_dims = []for x in range(0, len(pca_df)):pca_dims.append('PCA Component {}'.format(x))pca_test_df = pd.DataFrame(pca_test.components_, columns=columns, index=pca_dims)pca_test_df.head(10).T
rfc = RandomForestClassifier()rfc.fit(X_train_scaled_pca, y_train)display(rfc.score(X_train_scaled_pca, y_train))# 1.0
n_estimators：隨機森林中「樹」的數量。
max_features：每個分割處的特徵數。
max_depth：每棵樹可以擁有的最大「分裂」數。
min_samples_split：在樹的節點分裂前所需的最少觀察數。
min_samples_leaf：每棵樹末端的葉節點所需的最少觀察數。
bootstrap：是否使用 bootstrapping 來為隨機林中的每棵樹提供資料。（bootstrapping 是從資料集中進行替換的隨機抽樣。）
from sklearn.model_selection import RandomizedSearchCVn_estimators = [int(x) for x in np.linspace(start = 100, stop = 1000, num = 10)]max_features = ['log2', 'sqrt']max_depth = [int(x) for x in np.linspace(start = 1, stop = 15, num = 15)]min_samples_split = [int(x) for x in np.linspace(start = 2, stop = 50, num = 10)]min_samples_leaf = [int(x) for x in np.linspace(start = 2, stop = 50, num = 10)]bootstrap = [True, False]param_dist = {'n_estimators': n_estimators,'max_features': max_features,'max_depth': max_depth,'min_samples_split': min_samples_split,'min_samples_leaf': min_samples_leaf,'bootstrap': bootstrap}rs = RandomizedSearchCV(rfc_2,param_dist,n_iter = 100,cv = 3,verbose = 1,n_jobs=-1,random_state=0)rs.fit(X_train_scaled_pca, y_train)rs.best_params_————————————————————————————————————————————# {'n_estimators': 700,# 'min_samples_split': 2,# 'min_samples_leaf': 2,# 'max_features': 'log2',# 'max_depth': 11,# 'bootstrap': True}
rs_df = pd.DataFrame(rs.cv_results_).sort_values('rank_test_score').reset_index(drop=True)rs_df = rs_df.drop(['mean_fit_time','std_fit_time','mean_score_time','std_score_time','params','split0_test_score','split1_test_score','split2_test_score','std_test_score'],axis=1)rs_df.head(10)
fig, axs = plt.subplots(ncols=3, nrows=2)sns.set(style="whitegrid", color_codes=True, font_scale = 2)fig.set_size_inches(30,25)sns.barplot(x='param_n_estimators', y='mean_test_score', data=rs_df, ax=axs[0,0], color='lightgrey')axs[0,0].set_ylim([.83,.93])axs[0,0].set_title(label = 'n_estimators', size=30, weight='bold')sns.barplot(x='param_min_samples_split', y='mean_test_score', data=rs_df, ax=axs[0,1], color='coral')axs[0,1].set_ylim([.85,.93])axs[0,1].set_title(label = 'min_samples_split', size=30, weight='bold')sns.barplot(x='param_min_samples_leaf', y='mean_test_score', data=rs_df, ax=axs[0,2], color='lightgreen')axs[0,2].set_ylim([.80,.93])axs[0,2].set_title(label = 'min_samples_leaf', size=30, weight='bold')sns.barplot(x='param_max_features', y='mean_test_score', data=rs_df, ax=axs[1,0], color='wheat')axs[1,0].set_ylim([.88,.92])axs[1,0].set_title(label = 'max_features', size=30, weight='bold')sns.barplot(x='param_max_depth', y='mean_test_score', data=rs_df, ax=axs[1,1], color='lightpink')axs[1,1].set_ylim([.80,.93])axs[1,1].set_title(label = 'max_depth', size=30, weight='bold')sns.barplot(x='param_bootstrap',y='mean_test_score', data=rs_df, ax=axs[1,2], color='skyblue')axs[1,2].set_ylim([.88,.92])
n_estimators：300、500、700 的平均分數幾乎最高；
min_samples_split：較小的值（如 2 和 7）得分較高。23 處得分也很高。我們可以嘗試一些大於 2 的值，以及 23 附近的值；
min_samples_leaf：較小的值可能得到更高的分，我們可以嘗試使用 2–7 之間的值；
max_features：「sqrt」具有最高平均分；
max_depth：沒有明確的結果，但是 2、3、7、11、15 的效果很好；
bootstrap：「False」具有最高平均分。
from sklearn.model_selection import GridSearchCVn_estimators = [300,500,700]max_features = ['sqrt']max_depth = [2,3,7,11,15]min_samples_split = [2,3,4,22,23,24]min_samples_leaf = [2,3,4,5,6,7]bootstrap = [False]param_grid = {'n_estimators': n_estimators,'max_features': max_features,'max_depth': max_depth,'min_samples_split': min_samples_split,'min_samples_leaf': min_samples_leaf,'bootstrap': bootstrap}gs = GridSearchCV(rfc_2, param_grid, cv = 3, verbose = 1, n_jobs=-1)gs.fit(X_train_scaled_pca, y_train)rfc_3 = gs.best_estimator_gs.best_params_————————————————————————————————————————————# {'bootstrap': False,# 'max_depth': 7,# 'max_features': 'sqrt',# 'min_samples_leaf': 3,# 'min_samples_split': 2,# 'n_estimators': 500}
基線隨機森林
具有 PCA 降維的基線隨機森林
具有 PCA 降維和超引數調優的基線隨機森林
y_pred = rfc.predict(X_test_scaled)y_pred_pca = rfc.predict(X_test_scaled_pca)y_pred_gs = gs.best_estimator_.predict(X_test_scaled_pca)
from sklearn.metrics import confusion_matrixconf_matrix_baseline = pd.DataFrame(confusion_matrix(y_test, y_pred), index = ['actual 0', 'actual 1'], columns = ['predicted 0', 'predicted 1'])conf_matrix_baseline_pca = pd.DataFrame(confusion_matrix(y_test, y_pred_pca), index = ['actual 0', 'actual 1'], columns = ['predicted 0', 'predicted 1'])conf_matrix_tuned_pca = pd.DataFrame(confusion_matrix(y_test, y_pred_gs), index = ['actual 0', 'actual 1'], columns = ['predicted 0', 'predicted 1'])display(conf_matrix_baseline)display('Baseline Random Forest recall score', recall_score(y_test, y_pred))display(conf_matrix_baseline_pca)display('Baseline Random Forest With PCA recall score', recall_score(y_test, y_pred_pca))display(conf_matrix_tuned_pca)display('Hyperparameter Tuned Random Forest With PCA Reduced Dimensionality recall score', recall_score(y_test, y_pred_gs))
