PROOF OF CONCEPT: multilabel onevsrest SGDC classification

multilabel-OneVsRest-SGDC-classification.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jun 27 12:47:40 2015
+
+@author: daviz
+"""
+
+import pandas as pd
+import numpy as np
+from sklearn import cross_validation
+
+# Load datasets
+names = ["merchant_zipcode", "date", "category", "age_interval", "gender", "merchants", "cards", "payments", "avg_payment", "max_payment", "min_payment", "std"]
+demo_stats = pd.read_csv("dataset/demographic_distribution000", delim_whitespace=True,
+                         names= names, parse_dates=["date"],
+                         dtype = {'merchant_zipcode': str})
+
+demo_stats = demo_stats.loc[demo_stats["gender"] != 'unknown']
+demo_stats = demo_stats.loc[demo_stats["age_interval"] != 'unknown']
+demo_stats = demo_stats[demo_stats.category == 'es_barsandrestaurants']
+
+bcn_zipcodes = ['08001', '08002', '08003', '08004', '08005', '08006', '08007',
+                '08008', '08009', '08010', '08011', '08012', '08013', '08014',
+                '08015', '08016', '08017', '08018', '08019', '08020', '08021',
+                '08022', '08023', '08024', '08025', '08026', '08027', '08028',
+                '08029', '08030', '08031', '08032', '08033', '08034', '08035',
+                '08036', '08037', '08038', '08039', '08040', '08041', '08042']
+
+demo_stats = demo_stats[demo_stats.merchant_zipcode.apply(lambda zp: zp in bcn_zipcodes)]
+
+demo_stats["weekday"] = demo_stats["date"].map(lambda d: (d.weekday()))
+demo_stats["weekday"] = demo_stats["weekday"].astype('string')
+
+demo_stats["amount"] = demo_stats["payments"] * demo_stats["avg_payment"]
+demo_stats = demo_stats[demo_stats.amount < 500]
+demo_stats = demo_stats[demo_stats.amount > 0]
+
+demo_stats["amount_level"] = pd.cut(demo_stats.amount, 7, labels = ["very-low", "low", "low-medium", "medium", "medium-high", "high", "very-high"])
+demo_stats["max_payment_level"] = pd.cut(demo_stats.max_payment, 7, labels = ["very-low", "low", "low-medium", "medium", "medium-high", "high", "very-high"])
+demo_stats["min_payment_level"] = pd.cut(demo_stats.min_payment, 7, labels = ["very-low", "low", "low-medium", "medium", "medium-high", "high", "very-high"])
+
+demo_stats = demo_stats.groupby(["weekday","age_interval", "gender",  "max_payment_level", "min_payment_level"])['merchant_zipcode'].apply(list)
+demo_stats = demo_stats.reset_index()
+
+demo_stats['merchant_zipcode'] = demo_stats['merchant_zipcode'].map(lambda d: tuple(list(set(d))))
+
+X = demo_stats[["weekday","age_interval", "gender",  "max_payment_level", "min_payment_level"]]
+y = demo_stats[["merchant_zipcode"]]
+
+
+# Allow to use machine learning with categorical features.
+from sklearn.feature_extraction import DictVectorizer
+vec = DictVectorizer()
+X_vectorized = vec.fit_transform(X.to_dict("records")).toarray()
+
+# Multilabel classification
+from sklearn.preprocessing import MultiLabelBinarizer
+mlb = MultiLabelBinarizer()
+y_multilabel = mlb.fit_transform(y.values.ravel())
+
+
+PRC = 0.2
+X_train, X_test, y_train, y_test = cross_validation.train_test_split(X_vectorized, y_multilabel, test_size=PRC)
+
+# OneVsRest
+from sklearn.multiclass import OneVsRestClassifier
+from sklearn.metrics import classification_report
+
+from sklearn import tree
+classifier = OneVsRestClassifier(tree.DecisionTreeClassifier(criterion="entropy",
+                                                             max_features="log2"))
+#from sklearn import ensemble
+#classifier = ensemble.RandomForestClassifier(criterion="entropy",
+#                                             max_features="log2",
+#                                             n_estimators= 500)
+
+#from sklearn import svm
+#classifier = OneVsRestClassifier(svm.SVC(kernel='linear'))
+
+classifier.fit(X_train, y_train)
+
+y_predicted = classifier.predict(X_test)
+
+# Interpret carefully since it is a multilabel classification
+print classification_report(y_test, y_predicted)
+
+# Predict new examples
+example = {
+  'age_interval': '35-44',
+  'max_payment_level': 'low',
+  'min_payment_level': 'low',
+  'gender': 'female',
+  'weekday': '5'
+ }
+
+example_vectorized = vec.transform(example).toarray()
+example_predicted =  classifier.predict(example_vectorized)
+print "Person that can not spend lot of money on restaurant:"
+print mlb.inverse_transform(example_predicted)
+print "\n\n"
+
+example = {
+  'age_interval': '45-54',
+  'max_payment_level': 'high',
+  'min_payment_level': 'high',
+  'gender': 'male',
+  'weekday': '5'
+  }
+example_vectorized = vec.transform(example).toarray()
+example_predicted =  classifier.predict(example_vectorized)
+print "Person that can spend more money on restaurant:"
+print mlb.inverse_transform(example_predicted)