!pip install xgboost==1.4.2
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!pip install lightgbm==3.2.1
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!pip install catboost==0.26.1
Collecting catboost==0.26.1
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#linear algebra
import numpy as np
# data processing
import pandas as pd
# data visualization
import seaborn as sns
%matplotlib inline
from matplotlib import pyplot as plt
from matplotlib import style
# Algorithms
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC, LinearSVC
from sklearn.naive_bayes import GaussianNB
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from catboost import Pool, CatBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import precision_score, recall_score, classification_report
test_df = pd.read_csv(r"/work/titanic/test.csv")
train_df = pd.read_csv(r"/work/titanic/train.csv")
!pip install dataprep
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Not uninstalling jinja2 at /shared-libs/python3.7/py-core/lib/python3.7/site-packages, outside environment /root/venv
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from dataprep.eda import create_report
report = create_report(train_df)
IPyWidgets are not supported
#Getting info about the training dataset
train_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 PassengerId 891 non-null int64
1 Survived 891 non-null int64
2 Pclass 891 non-null int64
3 Name 891 non-null object
4 Sex 891 non-null object
5 Age 714 non-null float64
6 SibSp 891 non-null int64
7 Parch 891 non-null int64
8 Ticket 891 non-null object
9 Fare 891 non-null float64
10 Cabin 204 non-null object
11 Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
#Getting important statistics about the data
train_df.describe()
train_df.head() # Show the first 5 columns of the dataset
train_df.corr()
total = train_df.isnull().sum().sort_values(ascending=False)
percent_1 = train_df.isnull().sum()/train_df.isnull().count()*100
percent_2 = (round(percent_1, 1)).sort_values(ascending=False)
missing_data = pd.concat([total, percent_2], axis=1, keys=['Total', '%'])
missing_data.head(5)
train_df.columns.values
survived = 'survived'
not_survived = 'not survived'
fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(10, 4))
women = train_df[train_df['Sex']=='female']
men = train_df[train_df['Sex']=='male']
ax = sns.distplot(women[women['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[0], kde =False)
ax = sns.distplot(women[women['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[0], kde =False)
ax.legend()
ax.set_title('Female')
ax = sns.distplot(men[men['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[1], kde = False)
ax = sns.distplot(men[men['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[1], kde = False)
ax.legend()
_ = ax.set_title('Male')
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
FacetGrid = sns.FacetGrid(train_df, row='Embarked', size=4.5, aspect=1.6)
FacetGrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette=None, order=None, hue_order=None )
FacetGrid.add_legend()
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
warnings.warn(msg, UserWarning)
sns.barplot(x="Pclass", y="Survived", data = train_df)
grid = sns.FacetGrid(train_df, col="Survived", row="Pclass",size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=0.5, bins=20)
grid.add_legend()
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
warnings.warn(msg, UserWarning)
data = [train_df, test_df]
for dataset in data:
dataset['relatives'] = dataset['SibSp'] + dataset['Parch']
dataset.loc[dataset['relatives'] > 0, 'not_alone'] = 0
dataset.loc[dataset['relatives'] == 0, 'not_alone'] = 1
dataset['not_alone'] = dataset['not_alone'].astype(int)
train_df['not_alone'].value_counts()
axes = sns.factorplot('relatives', 'Survived', data=train_df, aspect=20.5)
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/categorical.py:3717: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.
warnings.warn(msg)
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
train_df = train_df.drop(['PassengerId'], axis=1)
import re
deck = {"A": 1, "B": 2, "C": 3, "D": 4, "E": 5, "F": 6, "G": 7, "U": 8}
data = [train_df, test_df]
for dataset in data:
dataset['Cabin'] = dataset['Cabin'].fillna("U0")
dataset['Deck'] = dataset['Cabin'].map(lambda x: re.compile("([a-zA-Z]+)").search(x).group())
dataset['Deck'] = dataset['Deck'].map(deck)
dataset['Deck'] = dataset['Deck'].fillna(0)
dataset['Deck'] = dataset['Deck'].astype(int)
# we can now drop the cabin feature
train_df = train_df.drop(['Cabin'], axis=1)
test_df = test_df.drop(['Cabin'], axis=1)
data = [train_df, test_df]
for dataset in data:
mean = train_df["Age"].mean()
std = test_df["Age"].std()
is_null = dataset["Age"].isnull().sum()
# compute random numbers between the mean, std and is_null
rand_age = np.random.randint(mean - std, mean + std, size = is_null)
# fill NaN values in Age column with random values generated
age_slice = dataset["Age"].copy()
age_slice[np.isnan(age_slice)] = rand_age
dataset["Age"] = age_slice
dataset["Age"] = train_df["Age"].astype(int)
train_df["Age"].isnull().sum()
train_df["Embarked"].describe()
common_value = 'S'
data = [train_df, test_df]
for dataset in data:
dataset['Embarked'] = dataset['Embarked'].fillna(common_value)
train_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Survived 891 non-null int64
1 Pclass 891 non-null int64
2 Name 891 non-null object
3 Sex 891 non-null object
4 Age 891 non-null int64
5 SibSp 891 non-null int64
6 Parch 891 non-null int64
7 Ticket 891 non-null object
8 Fare 891 non-null float64
9 Embarked 891 non-null object
10 relatives 891 non-null int64
11 not_alone 891 non-null int64
12 Deck 891 non-null int64
dtypes: float64(1), int64(8), object(4)
memory usage: 90.6+ KB
#converting fare to from float to int64 using astype() function
data= [train_df, test_df]
for dataset in data:
dataset['Fare'] = dataset['Fare'].fillna(0)
dataset['Fare'] = dataset['Fare'].astype(int)
# we will extract the titles from the name and build a new feature from out of that
data = [train_df, test_df]
titles = {'Mr': 1,'Mrs': 3,"Miss" : 2, "Master" : 4, "Rare" : 5}
for dataset in data:
#extract titles
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\.', expand=False)
# replace titles with more common title or as Rare
dataset['Title'] = dataset['Title'].replace(["Lady", 'Countess', 'Capt', 'Col',
'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona', 'Rare'])
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
# convert titles into numbers
dataset['Title'] = dataset['Title'].map(titles)
# filling NaN with 0, to get safe
dataset['Title'] = dataset['Title'].fillna(0)
train_df = train_df.drop(['Name'], axis=1)
test_df = test_df.drop(['Name'], axis=1)
#converting sex into numeric
genders = {"male" : 0, "female": 1}
data = [train_df, test_df]
for dataset in data:
dataset['Sex'] = dataset['Sex'].map(genders)
train_df['Ticket'].describe()
# this feature will be dofficult to `convert to uswful categories so we will drop it
train_df= train_df.drop(['Ticket'], axis=1)
test_df= test_df.drop(['Ticket'], axis=1)
#convert embarked into numeric
ports = {"S":0, "C":1, "Q" :2}
data = [train_df, test_df]
for dataset in data:
dataset['Embarked'] = dataset["Embarked"].map(ports)
data = [train_df, test_df]
for dataset in data:
dataset['Age'] = dataset['Age'].astype(int)
dataset.loc[ dataset['Age'] <= 11, 'Age'] = 0
dataset.loc[(dataset['Age'] > 11) & (dataset['Age'] <= 18), 'Age'] = 1
dataset.loc[(dataset['Age'] > 18) & (dataset['Age'] <= 22), 'Age'] = 2
dataset.loc[(dataset['Age'] > 22) & (dataset['Age'] <= 27), 'Age'] = 3
dataset.loc[(dataset['Age'] > 27) & (dataset['Age'] <= 33), 'Age'] = 4
dataset.loc[(dataset['Age'] > 33) & (dataset['Age'] <= 40), 'Age'] = 5
dataset.loc[(dataset['Age'] > 40) & (dataset['Age'] <= 66), 'Age'] = 6
dataset.loc[ dataset['Age'] > 66, 'Age'] = 6
# let's see how it's distributed
train_df['Age'].value_counts()
data = [train_df, test_df]
for dataset in data:
dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0
dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1
dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2
dataset.loc[(dataset['Fare'] > 31) & (dataset['Fare'] <= 99), 'Fare'] = 3
dataset.loc[(dataset['Fare'] > 99) & (dataset['Fare'] <= 250), 'Fare'] = 4
dataset.loc[ dataset['Fare'] > 250, 'Fare'] = 5
dataset['Fare'] = dataset['Fare'].astype(int)
#Age times class
data = [train_df, test_df]
for dataset in data:
dataset['Age_class'] = dataset['Age'] * dataset['Pclass']
#Fare per person
for dataset in data:
dataset['Fare_Per_Person'] = dataset['Fare']/(dataset['relatives']+1)
dataset['Fare_Per_Person'] = dataset['Fare_Per_Person'].astype(int)
# Let's take a last look at the training set, before we start training the models.
train_df.head(10)
X_train = train_df.drop("Survived", axis=1)
Y_train = train_df["Survived"]
X_test = test_df.drop("PassengerId", axis=1).copy()
train_df.corr()
sgd= linear_model.SGDClassifier(max_iter=5, tol=None)
sgd.fit(X_train, Y_train)
Y_pred = sgd.predict(X_test)
sgd.score(X_train, Y_train)
acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, Y_train)
Y_prediction = random_forest.predict(X_test)
random_forest.score(X_train, Y_train)
acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)
logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
acc_log = round(logreg.score(X_train, Y_train) * 100, 2)
/shared-libs/python3.7/py/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
#KNN
knn = KNeighborsClassifier(n_neighbors = 3)
knn.fit(X_train, Y_train)
Y_pred = knn.predict(X_test)
acc_knn = round(knn.score(X_train, Y_train) * 100, 2)
gaussian = GaussianNB()
gaussian.fit(X_train, Y_train)
Y_pred = gaussian.predict(X_test)
acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)
perceptron = Perceptron(max_iter=5)
perceptron.fit(X_train, Y_train)
Y_pred = perceptron.predict(X_test)
acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)
/shared-libs/python3.7/py/lib/python3.7/site-packages/sklearn/linear_model/_stochastic_gradient.py:577: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.
ConvergenceWarning)
linear_svc = LinearSVC()
linear_svc.fit(X_train, Y_train)
Y_pred = linear_svc.predict(X_test)
acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)
/shared-libs/python3.7/py/lib/python3.7/site-packages/sklearn/svm/_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
"the number of iterations.", ConvergenceWarning)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, Y_train)
Y_pred = decision_tree.predict(X_test)
acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)
results = pd.DataFrame({
'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression',
'Random Forest', 'Naive Bayes', 'Perceptron',
'Stochastic Gradient Decent',
'Decision Tree'],
'Score': [acc_linear_svc, acc_knn, acc_log,
acc_random_forest, acc_gaussian, acc_perceptron,
acc_sgd, acc_decision_tree]})
result_df = results.sort_values(by='Score', ascending=False)
result_df = result_df.set_index('Score')
result_df.head(9)
from sklearn.model_selection import cross_val_score
rf = RandomForestClassifier(n_estimators=100)
scores = cross_val_score(rf, X_train, Y_train, cv=10, scoring = "accuracy")
print("Scores:", scores)
print("Mean:", scores.mean())
print("Standard Deviation:", scores.std())
Scores: [0.75555556 0.80898876 0.73033708 0.84269663 0.88764045 0.83146067
0.82022472 0.76404494 0.85393258 0.83146067]
Mean: 0.8126342072409487
Standard Deviation: 0.0462674018507007
train_df = train_df.drop("not_alone", axis=1)
test_df = test_df.drop("not_alone", axis=1)
train_df = train_df.drop("Parch", axis=1)
test_df = test_df.drop("Parch", axis=1)
random_forest = RandomForestClassifier(n_estimators=100, oob_score = True)
random_forest.fit(X_train, Y_train)
Y_prediction = random_forest.predict(X_test)
random_forest.score(X_train, Y_train)
acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)
print(round(acc_random_forest,2,), "%")
92.59 %
Modelling with XGBOOST CLASSIFIER
xgb = XGBClassifier()
xgb.fit(X_train, Y_train)
y_pred = xgb.predict(X_test)
acc_xgb = round(xgb.score(X_train, Y_train) * 100, 2)
acc_xgb
[15:42:52] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
/root/venv/lib/python3.7/site-packages/xgboost/sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
warnings.warn(label_encoder_deprecation_msg, UserWarning)
MODELLING WITH LGBM CLASSIFIER
lgbm = LGBMClassifier()
lgbm.fit(X_train, Y_train)
y_pred = lgbm.predict(X_test)
acc_lgbm = round(lgbm.score(X_train, Y_train) * 100, 2)
acc_lgbm
catboost = CatBoostClassifier()
catboost.fit(X_train, Y_train)
y_pred = catboost.predict(X_test)
acc_catboost = round(catboost.score(X_train, Y_train) * 100, 2)
acc_catboost
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from sklearn.metrics import precision_recall_curve
# getting the probabilities of our predictions
y_scores = random_forest.predict_proba(X_train)
y_scores = y_scores[:,1]
precision, recall, threshold = precision_recall_curve(Y_train, y_scores)
def plot_precision_and_recall(precision, recall, threshold):
plt.plot(threshold, precision[:-1], "r-", label="precision", linewidth=3)
plt.plot(threshold, recall[:-1], "b", label="recall", linewidth=2.5)
plt.xlabel("threshold", fontsize=19)
plt.legend(loc="upper right", fontsize=19)
plt.ylim([0, 1])
plt.figure(figsize=(14, 7))
plot_precision_and_recall(precision, recall, threshold)
plt.show()
# another way to plot precision and recall together
def plot_precision_vs_recall(precision, recall):
plt.plot(recall, precision, "g--", linewidth=2.5)
plt.ylabel("recall", fontsize=19)
plt.xlabel("precision", fontsize=19)
plt.axis([0, 1.5, 0, 1.5])
plt.figure(figsize=(14, 7))
plot_precision_vs_recall(precision, recall)
plt.show()
from sklearn.metrics import roc_curve
# compute true positive rate and false positive rate
false_positive_rate, true_positive_rate, thresholds = roc_curve(Y_train, y_scores)
# plotting them against each other
def plot_roc_curve(false_positive_rate, true_positive_rate, label=None):
plt.plot(false_positive_rate, true_positive_rate, linewidth=2, label=label)
plt.plot([0, 1], [0, 1], 'r', linewidth=4)
plt.axis([0, 1, 0, 1])
plt.xlabel('False Positive Rate (FPR)', fontsize=16)
plt.ylabel('True Positive Rate (TPR)', fontsize=16)
plt.figure(figsize=(14, 7))
plot_roc_curve(false_positive_rate, true_positive_rate)
plt.show()
from sklearn.metrics import roc_auc_score
r_a_score = roc_auc_score(Y_train, y_scores)
print("ROC-AUC-Score:", r_a_score)
Execution error
NameError: name 'Y_train' is not defined
submission = pd.DataFrame({'PassengerId':test_df['PassengerId'],'Survived':Y_prediction})
#Visualize the first 5 rows
submission.head(15)