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import csv import numpy as np #素数は2以上なので0から開始lastは任意の数 own_nm = 0 last = 11 #任意の数の整数値を出す a = np.array(np.arange(2,last)) #設定した整数値の数だけ回す for i in range((len(a))-1): #自身の数を削除 own_delated = np.delete(a,own_nm) # for x in range((len(own_delated))): # if own_delated[x] % #次の数値の素数判定 own_nm += 1 print(own_delated) |
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import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split %matplotlib inline #データの読込み df = pd.read_csv('train.csv') #欠損値処理1 Fareは平均値、乗船地は一般的なSを代入 df['Fare'] = df['Fare'].fillna(df['Fare'].median()) df['Embarked'] = df['Embarked'].fillna('S') #欠損地処理2 #年齢をfillnaで平均を取るとランダムフォレストの結果が #訓練スコア 0.96 #テストスコア 0.80 #年齢は学習において重要なのでNaの場合行ごと削除した結果 #訓練スコア 0.98 #テストスコア 0.81に上昇した df=df.dropna(subset=['Age']) #カテゴリ変数の変換 df['Sex'] = df['Sex'].apply(lambda x: 1 if x == 'male' else 0) df['Embarked'] = df['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int) #学習に不要と思われるデータを削除 df = df.drop(['Cabin','Name','PassengerId','Ticket'],axis=1) #訓練データとテストデータに分離 train_X = df.drop('Survived', axis=1) train_y = df.Survived (train_X, test_X ,train_y, test_y) = train_test_split(train_X, train_y, test_size = 0.3, random_state = 666) #複数のモデル構築でどの分類器がベストかを調べる from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.linear_model import LinearRegression from sklearn.linear_model import Ridge #決定木 ki = DecisionTreeClassifier(random_state=0).fit(train_X, train_y) #ランダムフォレスト mori = RandomForestClassifier(random_state=0).fit(train_X,train_y) #ロジスティック回帰 logi = LogisticRegression(C=0.1).fit(train_X,train_y) #KNN KNN = KNeighborsClassifier(4).fit(train_X,train_y) #SVC svc = SVC(probability=True).fit(train_X,train_y) #linear linear = LinearRegression().fit(train_X,train_y) #ridge ridge = Ridge(alpha=1).fit(train_X,train_y) data ={"clf": ["tree", "forest","logistic","KNN","svc","Linear","Ridge"], "traning score":[(ki.score(train_X,train_y)),(mori.score(train_X,train_y)), (logi.score(train_X,train_y)),(KNN.score(train_X,train_y)), (svc.score(train_X,train_y)),(linear.score(train_X,train_y)), (ridge.score(train_X,train_y))], "test score":[(ki.score(test_X,test_y)),(mori.score(test_X,test_y)), logi.score(train_X,train_y),(KNN.score(test_X,test_y)), (svc.score(test_X,test_y)),(linear.score(train_X,train_y)),(ridge.score(train_X,train_y))] } frame = pd.DataFrame(data,index=["tree", "forest","logistic","KNN","svc","Linear","Ridge"]) frame.plot(kind="bar") |
Feature Extraction
複数のグラフを表示
ax=ax1
と
ax=ax2で分ける
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df = pd.read_csv('train.csv') fig = plt.figure() ax1 = fig.add_subplot(1,2,1) ax2 = fig.add_subplot(1,2,2) sns.barplot(x="Age",y="Sex",hue='Survived',data=df, ax=ax1) sns.countplot('Sex',hue='Survived',data=df, ax=ax2) |
matplotlibでグラフをpngに変換
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import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt plt.plot([1,2,3,4,5,4,3,2,1]) plt.savefig("aaa.png") |
‘int’ object is not iterableが出た場合forでin len()を設定すると
オブジェクトの数値が対象となってしまうのでin range()を使用する
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for i in range((len(a))): |