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explore basic feature.py
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explore basic feature.py
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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn import ensemble
from sklearn import cross_validation
from sklearn.metrics import roc_auc_score as auc
import time
plt.rcParams['figure.figsize'] = (12, 6)
#%% load data and remove constant and duplicate columns (taken from a kaggle script)
trainDataFrame = pd.read_csv('train.csv')
# remove constant columns
colsToRemove = []
for col in trainDataFrame.columns:
if trainDataFrame[col].std() == 0:
colsToRemove.append(col)
trainDataFrame.drop(colsToRemove, axis=1, inplace=True)
# remove duplicate columns
colsToRemove = []
columns = trainDataFrame.columns
for i in range(len(columns)-1):
v = trainDataFrame[columns[i]].values
for j in range(i+1,len(columns)):
if np.array_equal(v,trainDataFrame[columns[j]].values):
colsToRemove.append(columns[j])
trainDataFrame.drop(colsToRemove, axis=1, inplace=True)
trainLabels = trainDataFrame['TARGET']
trainFeatures = trainDataFrame.drop(['ID','TARGET'], axis=1)
#%% look at single feature performance
verySimpleLearner = ensemble.GradientBoostingClassifier(n_estimators=10, max_features=1, max_depth=3,
min_samples_leaf=100,learning_rate=0.3, subsample=0.65,
loss='deviance', random_state=1)
X_train, X_valid, y_train, y_valid = cross_validation.train_test_split(trainFeatures, trainLabels, test_size=0.5, random_state=1)
startTime = time.time()
singleFeatureAUC_list = []
singleFeatureAUC_dict = {}
for feature in X_train.columns:
trainInputFeature = X_train[feature].values.reshape(-1,1)
validInputFeature = X_valid[feature].values.reshape(-1,1)
verySimpleLearner.fit(trainInputFeature, y_train)
trainAUC = auc(y_train, verySimpleLearner.predict_proba(trainInputFeature)[:,1])
validAUC = auc(y_valid, verySimpleLearner.predict_proba(validInputFeature)[:,1])
singleFeatureAUC_list.append(validAUC)
singleFeatureAUC_dict[feature] = validAUC
validAUC = np.array(singleFeatureAUC_list)
timeToTrain = (time.time()-startTime)/60
print("(min,mean,max) AUC = (%.3f,%.3f,%.3f). took %.2f minutes" %(validAUC.min(),validAUC.mean(),validAUC.max(), timeToTrain))
# show the scatter plot of the individual feature performance
plt.figure(); plt.hist(validAUC, 50, normed=1, facecolor='blue', alpha=0.75)
plt.xlabel('AUC'); plt.ylabel('frequency'); plt.title('single feature AUC histogram'); plt.show()