adding example for customer history to docs

README.md

@@ -181,7 +181,7 @@ With this dataset, we can perform fitting on the `_cal` columns, and test on the
 
 #### Customer Predicitions
 
-Basic on customer history, we can predict what an individuals future purchases might look like:
+Based on customer history, we can predict what an individuals future purchases might look like:
 
     t = 10 #predict purchases in 10 periods
     individual = summary.iloc[20]
@@ -190,6 +190,21 @@ Basic on customer history, we can predict what an individuals future purchases m
     # 0.0576511
 
 
+### Customer Probability Histories
+
+Given a customer transaction history, we can calculate their historical probability of being alive, according to 
+our trained model. For example:
+
+    from lifetimes.plotting import plot_history_alive
+
+    id = 35
+    days_since_birth = 200
+    sp_trans = transaction_data.ix[transaction_data['id'] == id]
+    plot_history_alive(bgf, days_since_birth, sp_trans, 'date')
+
+![history](http://i.imgur.com/y45tum4.png)
+
+
 ## Questions? Comments? 
 
 Drop me a line at [`@cmrn_dp`](https://twitter.com/Cmrn_DP)! 

lifetimes/plotting.py

@@ -198,7 +198,7 @@ def plot_history_alive(model, t, transactions, datetime_col, freq='D', **kwargs)
     customer_history = customer_history.resample(freq, how='sum').reset_index()
 
     # plot alive_path
-    path = calculate_alive_path(model, transactions, datetime_col, units, freq)
+    path = calculate_alive_path(model, transactions, datetime_col, t, freq)
     path_dates = pd.date_range(start=min(transactions[datetime_col]), periods=len(path), freq=freq)
     plt.plot(path_dates, path, '-', label='P_alive')
 

lifetimes/utils.py

@@ -16,7 +16,7 @@ def coalesce(*args):
 
 
 def calibration_and_holdout_data(transactions, customer_id_col, datetime_col, calibration_period_end,
-                                 observation_period_end=datetime.today(), freq='D', datetime_format=None,):
+                                 observation_period_end=datetime.today(), freq='D', datetime_format=None):
     """
     This function creates a summary of each customer over a calibration and holdout period (training and testing, respectively).
     It accepts transition data, and returns a Dataframe of sufficient statistics.

tests/test_plotting.py

@@ -6,9 +6,9 @@
 from lifetimes import BetaGeoFitter
 
 
-bfg = BetaGeoFitter()
+bgf = BetaGeoFitter()
 data = pd.read_csv('lifetimes/datasets/cdnow_customers.csv', index_col=[0])
-bfg.fit(data['frequency'], data['recency'], data['T'], iterative_fitting=0)
+bgf.fit(data['frequency'], data['recency'], data['T'], iterative_fitting=0)
 
 @pytest.mark.plottest
 @pytest.mark.skipif("DISPLAY" not in os.environ, reason="requires display")
@@ -18,34 +18,34 @@ def test_plot_period_transactions(self):
         from matplotlib import pyplot as plt
 
         plt.figure()
-        plotting.plot_period_transactions(bfg)
+        plotting.plot_period_transactions(bgf)
 
         plt.figure()
-        plotting.plot_period_transactions(bfg, bins=range(5))
+        plotting.plot_period_transactions(bgf, bins=range(5))
 
         plt.figure()
-        plotting.plot_period_transactions(bfg, label=['A', 'B'])
+        plotting.plot_period_transactions(bgf, label=['A', 'B'])
         plt.show()
 
     def test_plot_frequency_recency_matrix(self):
         from matplotlib import pyplot as plt
 
         plt.figure()
-        plotting.plot_frequency_recency_matrix(bfg)
+        plotting.plot_frequency_recency_matrix(bgf)
 
         plt.figure()
-        plotting.plot_frequency_recency_matrix(bfg, max_recency=100, max_frequency=50)
+        plotting.plot_frequency_recency_matrix(bgf, max_recency=100, max_frequency=50)
 
         plt.show()
 
     def test_plot_expected_repeat_purchases(self):
         from matplotlib import pyplot as plt
 
         plt.figure()
-        plotting.plot_expected_repeat_purchases(bfg)
+        plotting.plot_expected_repeat_purchases(bgf)
 
         plt.figure()
-        plotting.plot_expected_repeat_purchases(bfg, label='test label')
+        plotting.plot_expected_repeat_purchases(bgf, label='test label')
 
         plt.show()