コード例 #1
0
ファイル: test_utils.py プロジェクト: Munduruca/lifetimes
def test_customer_lifetime_value_with_known_values(fitted_bg):
    """
    >>> print fitted_bg
    <lifetimes.BetaGeoFitter: fitted with 5000 subjects, r: 0.16, alpha: 1.86, a: 1.85, b: 3.18>
    >>> t = fitted_bg.data.head()
    >>> t
       frequency  recency    T
       0          0        0  298
       1          0        0  224
       2          6      142  292
       3          0        0  147
       4          2        9  183
    >>> print fitted_bg.predict(30, t['frequency'], t['recency'], t['T'])
    0    0.016053
    1    0.021171
    2    0.030461
    3    0.031686
    4    0.001607
    dtype: float64
    """
    t = fitted_bg.data.head()
    expected = np.array([0.016053, 0.021171, 0.030461, 0.031686, 0.001607])
    # discount_rate=0 means the clv will be the same as the predicted
    clv_d0 = utils.customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1,1,1,1,1]), time=1, discount_rate=0.)
    assert_almost_equal(clv_d0.values, expected, decimal=5)
    # discount_rate=1 means the clv will halve over a period
    clv_d1 = utils.customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1,1,1,1,1]), time=1, discount_rate=1.)
    assert_almost_equal(clv_d1.values, expected/2., decimal=5)
    # time=2, discount_rate=0 means the clv will be twice the initial
    clv_t2_d0 = utils.customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1,1,1,1,1]), time=2, discount_rate=0)
    assert_allclose(clv_t2_d0.values, expected*2., rtol=0.1)
    # time=2, discount_rate=1 means the clv will be twice the initial
    clv_t2_d1 = utils.customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1,1,1,1,1]), time=2, discount_rate=1.)
    assert_allclose(clv_t2_d1.values, expected/2. + expected/4., rtol=0.1)
コード例 #2
0
ファイル: test_utils.py プロジェクト: jtatineni/lifetimes
def test_customer_lifetime_value_with_known_values(fitted_bg):
    """
    >>> print fitted_bg
    <lifetimes.BetaGeoFitter: fitted with 5000 subjects, r: 0.16, alpha: 1.86, a: 1.85, b: 3.18>
    >>> t = fitted_bg.data.head()
    >>> t
       frequency  recency    T
       0          0        0  298
       1          0        0  224
       2          6      142  292
       3          0        0  147
       4          2        9  183
    >>> print fitted_bg.predict(30, t['frequency'], t['recency'], t['T'])
    0    0.016053
    1    0.021171
    2    0.030461
    3    0.031686
    4    0.001607
    dtype: float64
    """
    t = fitted_bg.data.head()
    expected = np.array([0.016053, 0.021171, 0.030461, 0.031686, 0.001607])
    # discount_rate=0 means the clv will be the same as the predicted
    clv_d0 = utils.customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1,1,1,1,1]), time=1, discount_rate=0.)
    assert_almost_equal(clv_d0.values, expected, decimal=5)
    # discount_rate=1 means the clv will halve over a period
    clv_d1 = utils.customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1,1,1,1,1]), time=1, discount_rate=1.)
    assert_almost_equal(clv_d1.values, expected/2., decimal=5)
    # time=2, discount_rate=0 means the clv will be twice the initial
    clv_t2_d0 = utils.customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1,1,1,1,1]), time=2, discount_rate=0)
    assert_allclose(clv_t2_d0.values, expected*2., rtol=0.1)
    # time=2, discount_rate=1 means the clv will be twice the initial
    clv_t2_d1 = utils.customer_lifetime_value(fitted_bg, t['frequency'], t['recency'], t['T'], monetary_value=pd.Series([1,1,1,1,1]), time=2, discount_rate=1.)
    assert_allclose(clv_t2_d1.values, expected/2. + expected/4., rtol=0.1)
コード例 #3
0
    def customer_lifetime_value(self,
                                transaction_prediction_model,
                                frequency,
                                recency,
                                T,
                                monetary_value,
                                time=12,
                                discount_rate=0.01):
        """
        This method computes the average lifetime value for a group of one or more customers.

        Parameters:
            transaction_prediction_model: the model to predict future transactions, literature uses
                pareto/ndb but we can also use a different model like bg
            frequency: the frequency vector of customers' purchases (denoted x in literature).
            recency: the recency vector of customers' purchases (denoted t_x in literature).
            T: the vector of customers' age (time since first purchase)
            monetary_value: the monetary value vector of customer's purchases (denoted m in literature).
            time: the lifetime expected for the user in months. Default: 12
            discount_rate: the monthly adjusted discount rate. Default: 0.01

        Returns:
            Series object with customer ids as index and the estimated customer lifetime values as values
        """
        # use the Gamma-Gamma estimates for the monetary_values
        adjusted_monetary_value = self.conditional_expected_average_profit(
            frequency, monetary_value)
        return customer_lifetime_value(transaction_prediction_model, frequency,
                                       recency, T, adjusted_monetary_value,
                                       time, discount_rate)
コード例 #4
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    def test_customer_lifetime_value_with_bgf(self):
        from collections import OrderedDict

        ggf = estimation.GammaGammaFitter()
        ggf.params_ = OrderedDict({'p': 6.25, 'q': 3.74, 'v': 15.44})

        bgf = estimation.BetaGeoFitter()
        bgf.fit(cdnow_customers_with_monetary_value['frequency'],
                cdnow_customers_with_monetary_value['recency'],
                cdnow_customers_with_monetary_value['T'],
                iterative_fitting=3)

        ggf_clv = ggf.customer_lifetime_value(
            bgf, cdnow_customers_with_monetary_value['frequency'],
            cdnow_customers_with_monetary_value['recency'],
            cdnow_customers_with_monetary_value['T'],
            cdnow_customers_with_monetary_value['monetary_value'])

        utils_clv = utils.customer_lifetime_value(
            bgf, cdnow_customers_with_monetary_value['frequency'],
            cdnow_customers_with_monetary_value['recency'],
            cdnow_customers_with_monetary_value['T'],
            ggf.conditional_expected_average_profit(
                cdnow_customers_with_monetary_value['frequency'],
                cdnow_customers_with_monetary_value['monetary_value']))
        npt.assert_equal(ggf_clv.values, utils_clv.values)
コード例 #5
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    def test_customer_lifetime_value_with_bgf(self):
        from collections import OrderedDict

        ggf = estimation.GammaGammaFitter()
        ggf.params_ = OrderedDict({'p':6.25, 'q':3.74, 'v':15.44})

        bgf = estimation.BetaGeoFitter()
        bgf.fit(cdnow_customers_with_monetary_value['frequency'], cdnow_customers_with_monetary_value['recency'], cdnow_customers_with_monetary_value['T'], iterative_fitting=3)

        ggf_clv = ggf.customer_lifetime_value(
                bgf,
                cdnow_customers_with_monetary_value['frequency'],
                cdnow_customers_with_monetary_value['recency'],
                cdnow_customers_with_monetary_value['T'],
                cdnow_customers_with_monetary_value['monetary_value']
        )

        utils_clv = utils.customer_lifetime_value(
                bgf,
                cdnow_customers_with_monetary_value['frequency'],
                cdnow_customers_with_monetary_value['recency'],
                cdnow_customers_with_monetary_value['T'],
                ggf.conditional_expected_average_profit(cdnow_customers_with_monetary_value['frequency'],cdnow_customers_with_monetary_value['monetary_value'])
        )
        npt.assert_equal(ggf_clv.values, utils_clv.values)
コード例 #6
0
ファイル: estimation.py プロジェクト: Munduruca/lifetimes
    def customer_lifetime_value(self, transaction_prediction_model, frequency, recency, T, monetary_value, time=12, discount_rate=1):
        """
        This method computes the average lifetime value for a group of one or more customers.
            transaction_prediction_model: the model to predict future transactions, literature uses
                pareto/ndb but we can also use a different model like bg
            frequency: the frequency vector of customers' purchases (denoted x in literature).
            recency: the recency vector of customers' purchases (denoted t_x in literature).
            T: the vector of customers' age (time since first purchase)
            monetary_value: the monetary value vector of customer's purchases (denoted m in literature).
            time: the lifetime expected for the user in months. Default: 12
            discount_rate: the monthly adjusted discount rate. Default: 1

        Returns:
            Series object with customer ids as index and the estimated customer lifetime values as values
        """
        adjusted_monetary_value = self.conditional_expected_average_profit(frequency, monetary_value)  # use the Gamma-Gamma estimates for the monetary_values
        return customer_lifetime_value(transaction_prediction_model, frequency, recency, T, adjusted_monetary_value, time, discount_rate)