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- #!/usr/bin/python
- import sys
- import pickle
- sys.path.append("../tools/")
- from feature_format import featureFormat, targetFeatureSplit
- from tester import dump_classifier_and_data
- ### Task 1: Select what features you'll use.
- ### features_list is a list of strings, each of which is a feature name.
- ### The first feature must be "poi".
- features_list = ['poi','salary'] # You will need to use more features
- ### Load the dictionary containing the dataset
- with open("final_project_dataset.pkl", "r") as data_file:
- data_dict = pickle.load(data_file)
- ### Task 2: Remove outliers
- ### Task 3: Create new feature(s)
- ### Store to my_dataset for easy export below.
- my_dataset = data_dict
- ### Extract features and labels from dataset for local testing
- data = featureFormat(my_dataset, features_list, sort_keys = True)
- labels, features = targetFeatureSplit(data)
- ### Task 4: Try a varity of classifiers
- ### Please name your classifier clf for easy export below.
- ### Note that if you want to do PCA or other multi-stage operations,
- ### you'll need to use Pipelines. For more info:
- ### http://scikit-learn.org/stable/modules/pipeline.html
- # Provided to give you a starting point. Try a variety of classifiers.
- from sklearn.naive_bayes import GaussianNB
- clf = GaussianNB()
- ### Task 5: Tune your classifier to achieve better than .3 precision and recall
- ### using our testing script. Check the tester.py script in the final project
- ### folder for details on the evaluation method, especially the test_classifier
- ### function. Because of the small size of the dataset, the script uses
- ### stratified shuffle split cross validation. For more info:
- ### http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedShuffleSplit.html
- # Example starting point. Try investigating other evaluation techniques!
- from sklearn.cross_validation import train_test_split
- features_train, features_test, labels_train, labels_test = \
- train_test_split(features, labels, test_size=0.3, random_state=42)
- ### Task 6: Dump your classifier, dataset, and features_list so anyone can
- ### check your results. You do not need to change anything below, but make sure
- ### that the version of poi_id.py that you submit can be run on its own and
- ### generates the necessary .pkl files for validating your results.
- dump_classifier_and_data(clf, my_dataset, features_list)
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