Tatiana belyukina

TatianaBelyukina_code/task1_Titanic/main.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+
+titanic = pd.read_csv('./train.csv')
+titanic.head(10)
+
+print(titanic.shape)
+print(titanic.describe())
+print(titanic['Survived'].value_counts())
+print(titanic.groupby('Sex')[['Survived']].mean())
+print(titanic.pivot_table('Survived', index='Sex', columns='Pclass'))
+titanic.pivot_table('Survived', index='Sex', columns='Pclass').plot()
+plt.show()
+
+age = pd.cut(titanic['Age'], [0, 18, 80])
+print(titanic.pivot_table('Survived', ['Sex', age], 'Pclass'))
+
+print(titanic.isna().sum())
+
+
+def splitColumn(data, feature):
+    vals = set(data[feature])
+    for val in vals:
+        if str(val) == "nan":
+            continue
+        a = []
+        for i in range(data.shape[0]):
+            if data[feature][i] == val:
+                a.append(1)
+            else:
+                a.append(0)
+        data[feature + "_" + str(val)] = a
+    return data.drop([feature], axis=1)
+
+
+def splitFloatColumn(data, feature, diapasones):
+    for i in range(len(diapasones) + 1):
+        a = []
+        for j in range(data.shape[0]):
+            t = True
+            if i > 0:
+                t &= (data[feature][j] >= diapasones[i - 1])
+            if i < len(diapasones):
+                t &= (data[feature][j] < diapasones[i])
+            if t:
+                a.append(1)
+            else:
+                a.append(0)
+        data[feature + "_" + str(i)] = a
+    return data.drop([feature], axis=1)
+
+
+def preprocess(data):
+    for i in range(data.shape[0]):
+        if np.isnan(data["Age"][i]):
+            if "Miss" in data["Name"][i] or "Ms" in data["Name"][i]:
+                data["Age"][i] = 15
+            elif "Mrs" in data["Name"][i] or "Mr" in data["Name"][i]:
+                data["Age"][i] = 30
+            elif "Don." in data["Name"][i]:
+                data["Age"][i] = 40
+            else:
+                data["Age"][i] = 10
+
+    data = data.drop(['PassengerId', 'Ticket', 'Name', 'Cabin'], axis=1)
+    print(data)
+    data = splitColumn(data, "Embarked")
+    data = splitColumn(data, "Sex")
+    data = splitColumn(data, "Pclass")
+    data = splitFloatColumn(data, "Parch", [1, 2, 4])
+    data = splitFloatColumn(data, "SibSp", [1, 2, 4])
+    data = splitFloatColumn(data, "Age", [2, 5, 10, 16, 20, 28, 35, 45])
+    data = splitFloatColumn(data, "Fare", [7, 9, 10, 15, 30, 60, 80])
+
+    for feature in data:
+        print(feature)
+        for i in range(data.shape[0]):
+            if np.isnan(data[feature][i]):
+                data[feature][i] = 0
+    print(data)
+    return data
+
+
+def split_data(data):
+    return (data.iloc[:, 1:].values, data.iloc[:, 0].values)
+
+
+titanic = preprocess(titanic)
+print(titanic.dtypes)
+
+X, Y = split_data(titanic)
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
+
+
+def check_model(x_train, y_train, x_test, y_test, model):
+    print("traindata:", model.score(x_train, y_train))
+    print("testdata:", model.score(x_test, y_test))
+    print()
+
+
+def models(x_train, y_train, x_test, y_test):
+    from sklearn.tree import DecisionTreeClassifier
+    tree = DecisionTreeClassifier(criterion="entropy")
+    tree.fit(x_train, y_train)
+
+    from sklearn.linear_model import LogisticRegression
+    logReg = LogisticRegression(max_iter=1000000)
+    logReg.fit(x_train, y_train)
+
+    from sklearn.neighbors import KNeighborsClassifier
+    neighbors = KNeighborsClassifier(n_neighbors=5, metric="minkowski", p=2)
+    neighbors.fit(x_train, y_train)
+
+    from sklearn.ensemble import RandomForestClassifier
+    forest = RandomForestClassifier(n_estimators=10, criterion="entropy")
+    forest.fit(x_train, y_train)
+
+    from sklearn.naive_bayes import GaussianNB
+    gauss = GaussianNB()
+    gauss.fit(x_train, y_train)
+
+    from sklearn.svm import SVC
+    svc = SVC(kernel="linear")
+    svc.fit(x_train, y_train)
+
+    svcRBF = SVC(kernel="rbf")
+    svcRBF.fit(x_train, y_train)
+
+    print("tree")
+    check_model(x_train, y_train, x_test, y_test, tree)
+    print("logReg")
+    check_model(x_train, y_train, x_test, y_test, logReg)
+    print("neighbors")
+    check_model(x_train, y_train, x_test, y_test, neighbors)
+    print("forest")
+    check_model(x_train, y_train, x_test, y_test, forest)
+    print("gauss")
+    check_model(x_train, y_train, x_test, y_test, gauss)
+    print("svc_linear")
+    check_model(x_train, y_train, x_test, y_test, svc)
+    print("svc_rbf")
+    check_model(x_train, y_train, x_test, y_test, svcRBF)
+    return svcRBF
+
+
+model = models(X_train, Y_train, X_test, Y_test)
+
+
+def generateAns(model):
+    test_ids = pd.read_csv("test.csv")["PassengerId"]
+    test = pd.read_csv("test.csv")
+
+    test = preprocess(test)
+    x, y = split_data(test)
+
+    Y_pred = model.predict(x)
+    print(Y_pred)
+    Y_pred = Y_pred.astype(int) 
+    print(Y_pred)
+
+    submission = pd.DataFrame({
+        "PassengerId": test_ids,
+        "Survived": Y_pred
+    })
+    submission.to_csv('./titanic.csv', index=False)
+    print('Exported!')
+
+
+generateAns(model)