import pandas #ipython notebook
titanic = pandas.read_csv("titanic_train.csv")
titanic.head(5)
#print (titanic.describe())
titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())
print titanic.describe()
print titanic["Sex"].unique()
# Replace all the occurences of male with the number 0.
titanic.loc[titanic["Sex"] == "male", "Sex"] = 0
titanic.loc[titanic["Sex"] == "female", "Sex"] = 1
输出 ： ['male' 'female']
print titanic["Embarked"].unique()
titanic["Embarked"] = titanic["Embarked"].fillna('S')
titanic.loc[titanic["Embarked"] == "S", "Embarked"] = 0
titanic.loc[titanic["Embarked"] == "C", "Embarked"] = 1
titanic.loc[titanic["Embarked"] == "Q", "Embarked"] = 2
输出 ： ['S' 'C' 'Q' nan]
# Import the linear regression class
from sklearn.linear_model import LinearRegression
# Sklearn also has a helper that makes it easy to do cross validation
from sklearn.cross_validation import KFold
# The columns we'll use to predict the target
predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]
# Initialize our algorithm class
alg = LinearRegression()
# Generate cross validation folds for the titanic dataset. It return the row indices corresponding to train and test.
# We set random_state to ensure we get the same splits every time we run this.
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
# The predictors we're using the train the algorithm. Note how we only take the rows in the train folds.
train_predictors = (titanic[predictors].iloc[train,:])
# The target we're using to train the algorithm.
train_target = titanic["Survived"].iloc[train]
# Training the algorithm using the predictors and target.
alg.fit(train_predictors, train_target)
# We can now make predictions on the test fold
test_predictions = alg.predict(titanic[predictors].iloc[test,:])
predictions.append(test_predictions)
import numpy as np
# The predictions are in three separate numpy arrays. Concatenate them into one.
# We concatenate them on axis 0, as they only have one axis.
predictions = np.concatenate(predictions, axis=0)
# Map predictions to outcomes (only possible outcomes are 1 and 0)
predictions[predictions > .5] = 1
predictions[predictions <=.5] = 0
accuracy = sum(predictions[predictions == titanic["Survived"]]) / len(predictions)
print accuracy
输出 ： 0.783389450056
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
# Initialize our algorithm
alg = LogisticRegression(random_state=1)
# Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=3)
# Take the mean of the scores (because we have one for each fold)
print(scores.mean())
输出 ： 0.787878787879
titanic_test = pandas.read_csv("test.csv")
titanic_test["Age"] = titanic_test["Age"].fillna(titanic["Age"].median())
titanic_test["Fare"] = titanic_test["Fare"].fillna(titanic_test["Fare"].median())
titanic_test.loc[titanic_test["Sex"] == "male", "Sex"] = 0
titanic_test.loc[titanic_test["Sex"] == "female", "Sex"] = 1
titanic_test["Embarked"] = titanic_test["Embarked"].fillna("S")
titanic_test.loc[titanic_test["Embarked"] == "S", "Embarked"] = 0
titanic_test.loc[titanic_test["Embarked"] == "C", "Embarked"] = 1
titanic_test.loc[titanic_test["Embarked"] == "Q", "Embarked"] = 2
from sklearn import cross_validation
from sklearn.ensemble import RandomForestClassifier
predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]
# Initialize our algorithm with the default paramters
# n_estimators is the number of trees we want to make
# min_samples_split is the minimum number of rows we need to make a split
# min_samples_leaf is the minimum number of samples we can have at the place where a tree branch ends (the bottom points of the tree)
alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=2, min_samples_leaf=1)
# Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)
kf = cross_validation.KFold(titanic.shape[0], n_folds=3, random_state=1)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=kf)
# Take the mean of the scores (because we have one for each fold)
print(scores.mean())
输出 ： 0.785634118967
alg = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2)
# Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)
kf = cross_validation.KFold(titanic.shape[0], 3, random_state=1)
scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=kf)
# Take the mean of the scores (because we have one for each fold)
print(scores.mean())
输出 ： 0.814814814815
# Generating a familysize column
titanic["FamilySize"] = titanic["SibSp"] + titanic["Parch"]
# The .apply method generates a new series
titanic["NameLength"] = titanic["Name"].apply(lambda x: len(x))
import re
# A function to get the title from a name.
def get_title(name):
# Use a regular expression to search for a title. Titles always consist of capital and lowercase letters, and end with a period.
title_search = re.search(' ([A-Za-z]+)\.', name)
# If the title exists, extract and return it.
if title_search:
return title_search.group(1)
return ""
# Get all the titles and print how often each one occurs.
titles = titanic["Name"].apply(get_title)
print(pandas.value_counts(titles))
# Map each title to an integer. Some titles are very rare, and are compressed into the same codes as other titles.
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Dr": 5, "Rev": 6, "Major": 7, "Col": 7, "Mlle": 8, "Mme": 8, "Don": 9, "Lady": 10, "Countess": 10, "Jonkheer": 10, "Sir": 9, "Capt": 7, "Ms": 2}
for k,v in title_mapping.items():
titles[titles == k] = v
# Verify that we converted everything.
print(pandas.value_counts(titles))
# Add in the title column.
titanic["Title"] = titles
import numpy as np
from sklearn.feature_selection import SelectKBest, f_classif
import matplotlib.pyplot as plt
predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked", "FamilySize", "Title", "NameLength"]
# Perform feature selection
selector = SelectKBest(f_classif, k=5)
selector.fit(titanic[predictors], titanic["Survived"])
# Get the raw p-values for each feature, and transform from p-values into scores
scores = -np.log10(selector.pvalues_)
# Plot the scores. See how "Pclass", "Sex", "Title", and "Fare" are the best?
plt.bar(range(len(predictors)), scores)
plt.xticks(range(len(predictors)), predictors, rotation='vertical')
plt.show()
# Pick only the four best features.
predictors = ["Pclass", "Sex", "Fare", "Title"]
alg = RandomForestClassifier(random_state=1, n_estimators=50, min_samples_split=8, min_samples_leaf=4)
from sklearn.ensemble import GradientBoostingClassifier
import numpy as np
# The algorithms we want to ensemble.
# We're using the more linear predictors for the logistic regression, and everything with the gradient boosting classifier.
algorithms = [
[GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3), ["Pclass", "Sex", "Age", "Fare", "Embarked", "FamilySize", "Title",]],
[LogisticRegression(random_state=1), ["Pclass", "Sex", "Fare", "FamilySize", "Title", "Age", "Embarked"]]
]
# Initialize the cross validation folds
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
predictions = []
for train, test in kf:
train_target = titanic["Survived"].iloc[train]
full_test_predictions = []
# Make predictions for each algorithm on each fold
for alg, predictors in algorithms:
# Fit the algorithm on the training data.
alg.fit(titanic[predictors].iloc[train,:], train_target)
# Select and predict on the test fold.
# The .astype(float) is necessary to convert the dataframe to all floats and avoid an sklearn error.
test_predictions = alg.predict_proba(titanic[predictors].iloc[test,:].astype(float))[:,1]
full_test_predictions.append(test_predictions)
# Use a simple ensembling scheme -- just average the predictions to get the final classification.
test_predictions = (full_test_predictions[0] + full_test_predictions[1]) / 2
# Any value over .5 is assumed to be a 1 prediction, and below .5 is a 0 prediction.
test_predictions[test_predictions <= .5] = 0
test_predictions[test_predictions > .5] = 1
predictions.append(test_predictions)
# Put all the predictions together into one array.
predictions = np.concatenate(predictions, axis=0)
# Compute accuracy by comparing to the training data.
accuracy = sum(predictions[predictions == titanic["Survived"]]) / len(predictions)
print(accuracy)
输出 ： 0.821548821549
titles = titanic_test["Name"].apply(get_title)
# We're adding the Dona title to the mapping, because it's in the test set, but not the training set
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Dr": 5, "Rev": 6, "Major": 7, "Col": 7, "Mlle": 8, "Mme": 8, "Don": 9, "Lady": 10, "Countess": 10, "Jonkheer": 10, "Sir": 9, "Capt": 7, "Ms": 2, "Dona": 10}
for k,v in title_mapping.items():
titles[titles == k] = v
titanic_test["Title"] = titles
# Check the counts of each unique title.
print(pandas.value_counts(titanic_test["Title"]))
# Now, we add the family size column.
titanic_test["FamilySize"] = titanic_test["SibSp"] + titanic_test["Parch"]
predictors = ["Pclass", "Sex", "Age", "Fare", "Embarked", "FamilySize", "Title"]
algorithms = [
[GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3), predictors],
[LogisticRegression(random_state=1), ["Pclass", "Sex", "Fare", "FamilySize", "Title", "Age", "Embarked"]]
]
full_predictions = []
for alg, predictors in algorithms:
# Fit the algorithm using the full training data.
alg.fit(titanic[predictors], titanic["Survived"])
# Predict using the test dataset. We have to convert all the columns to floats to avoid an error.
predictions = alg.predict_proba(titanic_test[predictors].astype(float))[:,1]
full_predictions.append(predictions)
# The gradient boosting classifier generates better predictions, so we weight it higher.
predictions = (full_predictions[0] * 3 + full_predictions[1]) / 4
predictions
输出 ：
array([ 0.11682912, 0.47835566, 0.12614824, 0.13098157, 0.52105874,
0.1435209 , 0.64085331, 0.18003152, 0.67801353, 0.12111118,
0.12105181, 0.20902118, 0.91068381, 0.1089127 , 0.89142102,
0.87713474, 0.16349859, 0.13907791, 0.54103238, 0.55661006,
0.22420875, 0.5372079 , 0.90572223, 0.38890588, 0.88384752,
0.10357315, 0.90909441, 0.13746454, 0.31046249, 0.12665718,
0.11663767, 0.18274855, 0.55220994, 0.49648575, 0.42415297,
0.14191051, 0.50973638, 0.52452209, 0.13270506, 0.28366691,
0.11145281, 0.46618807, 0.09996501, 0.83420617, 0.89959119,
0.14983417, 0.31593419, 0.13789623, 0.89104185, 0.54189565,
0.35666363, 0.17718135, 0.8307195 , 0.87995521, 0.1755907 ,
0.13741805, 0.10667279, 0.1234385 , 0.12099736, 0.91285169,
0.13099159, 0.15341948, 0.12993967, 0.66573206, 0.66343836,
0.87272604, 0.67238712, 0.288265 , 0.35236574, 0.85565507,
0.6622414 , 0.12701993, 0.55390065, 0.36740462, 0.91110312,
0.41201902, 0.13014004, 0.83671279, 0.15614414, 0.6622414 ,
0.68129213, 0.20605719, 0.20382623, 0.12105181, 0.18486634,
0.13130212, 0.65680539, 0.53029858, 0.65489631, 0.79881212,
0.53764546, 0.12104028, 0.8913725 , 0.13014004, 0.28406245,
0.12345367, 0.86792484, 0.14666337, 0.58599461, 0.12260781,
0.90433464, 0.14730817, 0.13789623, 0.12262433, 0.62257491,
0.13155874, 0.14607753, 0.13789623, 0.13020336, 0.17473033,
0.14286392, 0.65490316, 0.89528117, 0.67146758, 0.88346017,
0.13992078, 0.11805064, 0.69612515, 0.36668939, 0.86241698,
0.87649291, 0.12609327, 0.90276371, 0.12099027, 0.13789623,
0.56971935, 0.12608181, 0.63733743, 0.13339996, 0.13340574,
0.12723637, 0.51609607, 0.23921874, 0.10791695, 0.09896737,
0.12431124, 0.13346495, 0.16214099, 0.52029433, 0.12232635,
0.20712059, 0.90529649, 0.19747926, 0.16153716, 0.42927593,
0.10487176, 0.33642492, 0.13518414, 0.46618807, 0.34478758,
0.91431377, 0.13214999, 0.10690998, 0.48983645, 0.11274825,
0.12427868, 0.9107016 , 0.57991631, 0.42927593, 0.51274048,
0.65489239, 0.57884522, 0.82113381, 0.12096648, 0.28979611,
0.58587108, 0.30130471, 0.14606803, 0.9025041 , 0.52257377,
0.12101884, 0.13299498, 0.12418534, 0.13207486, 0.1319655 ,
0.8729358 , 0.87633414, 0.29670328, 0.83389526, 0.85558679,
0.15614414, 0.33352246, 0.90219082, 0.13789623, 0.91718918,
0.13603003, 0.85482389, 0.12241402, 0.14217314, 0.13560687,
0.1348803 , 0.25547183, 0.49950989, 0.12729496, 0.71980831,
0.10795469, 0.85516508, 0.58990449, 0.16645668, 0.53980354,
0.64867969, 0.66329187, 0.60981573, 0.87333314, 0.16322638,
0.25696649, 0.63083524, 0.16482591, 0.88984707, 0.12346408,
0.12849653, 0.12097124, 0.24675029, 0.80199995, 0.41248342,
0.29768148, 0.65492663, 0.21860346, 0.90027407, 0.13014004,
0.8137002 , 0.13611142, 0.84275393, 0.12700828, 0.87789288,
0.59807994, 0.12518087, 0.65489631, 0.11487493, 0.1441311 ,
0.25075165, 0.89266286, 0.11622683, 0.1379133 , 0.34224639,
0.12796773, 0.19365861, 0.14018901, 0.80948189, 0.89790832,
0.87598967, 0.82598174, 0.33036559, 0.12105101, 0.33258156,
0.28710745, 0.8790295 , 0.16058987, 0.86241698, 0.59133092,
0.74586492, 0.15434326, 0.39647431, 0.13354268, 0.12701864,
0.12101884, 0.13789623, 0.13014004, 0.83005787, 0.12700585,
0.10894954, 0.12701508, 0.85003763, 0.64929875, 0.16619539,
0.12105181, 0.21821016, 0.12101884, 0.50973638, 0.14016481,
0.34495861, 0.13789623, 0.91564 , 0.6332826 , 0.13207439,
0.85713531, 0.15861636, 0.12500116, 0.14267175, 0.16811853,
0.52045075, 0.66231856, 0.65489631, 0.64136782, 0.71198852,
0.10601085, 0.12099027, 0.3627808 , 0.13207486, 0.13014004,
0.33304456, 0.59319589, 0.13207486, 0.50584352, 0.12081676,
0.12263655, 0.77903176, 0.12665718, 0.33024483, 0.12028976,
0.11813957, 0.17547887, 0.1216941 , 0.13347145, 0.65489631,
0.82133626, 0.33497525, 0.67696014, 0.20916505, 0.42575111,
0.13912869, 0.13799529, 0.12102122, 0.61904744, 0.90111957,
0.67393647, 0.23919457, 0.17328806, 0.12182854, 0.18522951,
0.12262433, 0.13491478, 0.16214099, 0.45541306, 0.90601333,
0.12509883, 0.86563776, 0.34598576, 0.14469719, 0.17034218,
0.82147627, 0.32823572, 0.13207439, 0.64322911, 0.12183262,
0.25111398, 0.15333425, 0.09370087, 0.20950803, 0.35411806,
0.17507148, 0.118123 , 0.1469565 , 0.91556464, 0.33657652,
0.618368 , 0.16214099, 0.62462682, 0.1654289 , 0.85157883,
0.89603825, 0.16322638, 0.24472808, 0.16066609, 0.70031025,
0.15642457, 0.85672648, 0.12105022, 0.13789623, 0.57255235,
0.10418822, 0.87672475, 0.86918839, 0.13098157, 0.91914163,
0.15715004, 0.1313025 , 0.53322127, 0.89562968, 0.17356053,
0.15319843, 0.90891499, 0.16307942, 0.13130575, 0.87654859,
0.90969185, 0.48853359, 0.17002326, 0.19866966, 0.13510974,
0.13789623, 0.14010265, 0.54133852, 0.5949924 , 0.15905635,
0.83276875, 0.12430276, 0.12019388, 0.14606637, 0.18789784,
0.38579307, 0.87750065, 0.56459193, 0.12807839, 0.10318132,
0.91169572, 0.14231524, 0.88773179, 0.12607946, 0.12971145,
0.90753797, 0.12635163, 0.90891637, 0.35988713, 0.30442425,
0.18966803, 0.1501521 , 0.26822399, 0.65488945, 0.64585313,
0.65489631, 0.90711865, 0.56933478, 0.13014004, 0.86010063,
0.10126674, 0.13014004, 0.41850311])
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