diff --git a/.gitignore b/.gitignore index 2ffcf97dc..d872c903d 100644 --- a/.gitignore +++ b/.gitignore @@ -132,3 +132,6 @@ dmypy.json .DS_Store .vscode + +# PyCharm .idea files +.idea/ \ No newline at end of file diff --git a/pymc_marketing/clv/utils.py b/pymc_marketing/clv/utils.py index 0ab0cce91..ae7bc5882 100644 --- a/pymc_marketing/clv/utils.py +++ b/pymc_marketing/clv/utils.py @@ -1,9 +1,12 @@ +from datetime import datetime from typing import Union import numpy as np import pandas as pd import xarray +__all__ = ["to_xarray", "customer_lifetime_value", "clv_summary"] + def to_xarray(customer_id, *arrays, dim: str = "customer_id"): """Convert vector arrays to xarray with a common dim (default "customer_id").""" @@ -116,3 +119,219 @@ def customer_lifetime_value( ) return clv + + +def _find_first_transactions( + transactions: pd.DataFrame, + customer_id_col: str, + datetime_col: str, + monetary_value_col: str = None, + datetime_format: str = None, + observation_period_end: Union[str, pd.Period, datetime] = None, + time_unit: str = "D", +) -> pd.DataFrame: + """ + Return dataframe with first transactions. + + This takes a DataFrame of transaction data of the form: + customer_id, datetime [, monetary_value] + and appends a column named 'repeated' to the transaction log which indicates which rows + are repeated transactions for that customer_id. + + Adapted from lifetimes package + https://github.com/CamDavidsonPilon/lifetimes/blob/41e394923ad72b17b5da93e88cfabab43f51abe2/lifetimes/utils.py#L148 + + Parameters + ---------- + transactions: :obj: DataFrame + A Pandas DataFrame that contains the customer_id col and the datetime col. + customer_id_col: string + Column in the transactions DataFrame that denotes the customer_id. + datetime_col: string + Column in the transactions DataFrame that denotes the datetime the purchase was made. + monetary_value_col: string, optional + Column in the transactions DataFrame that denotes the monetary value of the transaction. + Optional; only needed for spend estimation models like the Gamma-Gamma model. + observation_period_end: :obj: datetime + A string or datetime to denote the final date of the study. + Events after this date are truncated. If not given, defaults to the max 'datetime_col'. + datetime_format: string, optional + A string that represents the timestamp format. Useful if Pandas can't understand + the provided format. + time_unit: string, optional + Time granularity for study. + Default: 'D' for days. Possible values listed here: + https://numpy.org/devdocs/reference/arrays.datetime.html#datetime-units + """ + + select_columns = [customer_id_col, datetime_col] + + if monetary_value_col: + select_columns.append(monetary_value_col) + + transactions = transactions[select_columns].sort_values(select_columns).copy() + + # convert date column into a DateTimeIndex for time-wise grouping and truncating + transactions[datetime_col] = pd.to_datetime( + transactions[datetime_col], format=datetime_format + ) + transactions = ( + transactions.set_index(datetime_col).to_period(time_unit).to_timestamp() + ) + + transactions = transactions.loc[ + (transactions.index <= observation_period_end) + ].reset_index() + + period_groupby = transactions.groupby( + [datetime_col, customer_id_col], sort=False, as_index=False + ) + + if monetary_value_col: + # when processing a monetary column, make sure to sum together transactions made in the same period + period_transactions = period_groupby.sum() + else: + # by calling head() on the groupby object, the datetime and customer_id columns + # will be reduced to the first transaction of that time period + period_transactions = period_groupby.head(1) + + # create a new column for flagging first transactions + period_transactions = period_transactions.copy() + period_transactions.loc[:, "first"] = False + # find all first transactions and store as an index + first_transactions = ( + period_transactions.groupby(customer_id_col, sort=True, as_index=False) + .head(1) + .index + ) + # flag first transactions as True + period_transactions.loc[first_transactions, "first"] = True + select_columns.append("first") + # reset datetime_col to period + period_transactions.loc[:, datetime_col] = pd.Index( + period_transactions[datetime_col] + ).to_period(time_unit) + + return period_transactions[select_columns] + + +def clv_summary( + transactions: pd.DataFrame, + customer_id_col: str, + datetime_col: str, + monetary_value_col: str = None, + datetime_format: str = None, + observation_period_end: Union[str, pd.Period, datetime] = None, + time_unit: str = "D", + time_scaler: float = 1, +) -> pd.DataFrame: + """ + Summarize transaction data for modeling. + + This transforms a DataFrame of transaction data of the form: + customer_id, datetime [, monetary_value] + to a DataFrame of the form: + customer_id, frequency, recency, T [, monetary_value] + + Adapted from lifetimes package + https://github.com/CamDavidsonPilon/lifetimes/blob/41e394923ad72b17b5da93e88cfabab43f51abe2/lifetimes/utils.py#L230 + + Parameters + ---------- + transactions: :obj: DataFrame + A Pandas DataFrame that contains the customer_id col and the datetime col. + customer_id_col: string + Column in the transactions DataFrame that denotes the customer_id. + datetime_col: string + Column in the transactions DataFrame that denotes the datetime the purchase was made. + monetary_value_col: string, optional + Column in the transactions DataFrame that denotes the monetary value of the transaction. + Optional; only needed for spend estimation models like the Gamma-Gamma model. + observation_period_end: datetime, optional + A string or datetime to denote the final date of the study. + Events after this date are truncated. If not given, defaults to the max 'datetime_col'. + datetime_format: string, optional + A string that represents the timestamp format. Useful if Pandas can't understand + the provided format. + time_unit: string, optional + Time granularity for study. + Default: 'D' for days. Possible values listed here: + https://numpy.org/devdocs/reference/arrays.datetime.html#datetime-units + time_scaler: int, optional + Default: 1. Useful for scaling recency & T to a different time granularity. Example: + With freq='D' and freq_multiplier=1, we get recency=591 and T=632 + With freq='h' and freq_multiplier=24, we get recency=590.125 and T=631.375 + This is useful if predictions in a different time granularity are desired, + and can also help with model convergence for study periods of many years. + + Returns + ------- + :obj: DataFrame: + customer_id, frequency, recency, T [, monetary_value] + """ + + if observation_period_end is None: + observation_period_end = ( + pd.to_datetime(transactions[datetime_col].max(), format=datetime_format) + .to_period(time_unit) + .to_timestamp() + ) + else: + observation_period_end = ( + pd.to_datetime(observation_period_end, format=datetime_format) + .to_period(time_unit) + .to_timestamp() + ) + + # label repeated transactions + repeated_transactions = _find_first_transactions( + transactions, + customer_id_col, + datetime_col, + monetary_value_col, + datetime_format, + observation_period_end, + time_unit, + ) + # reset datetime_col to timestamp + repeated_transactions[datetime_col] = pd.Index( + repeated_transactions[datetime_col] + ).to_timestamp() + + # count all orders by customer + customers = repeated_transactions.groupby(customer_id_col, sort=False)[ + datetime_col + ].agg(["min", "max", "count"]) + + # subtract 1 from count for non-repeat customers + customers["frequency"] = customers["count"] - 1 + + customers["T"] = ( + (observation_period_end - customers["min"]) + / np.timedelta64(1, time_unit) + / time_scaler + ) + customers["recency"] = ( + (customers["max"] - customers["min"]) + / np.timedelta64(1, time_unit) + / time_scaler + ) + + summary_columns = ["frequency", "recency", "T"] + + if monetary_value_col: + # create an index of first purchases + first_purchases = repeated_transactions[repeated_transactions["first"]].index + # Exclude first purchases from the mean value calculation, + # by setting as null, then imputing with zero + repeated_transactions.loc[first_purchases, monetary_value_col] = np.nan + customers["monetary_value"] = ( + repeated_transactions.groupby(customer_id_col)[monetary_value_col] + .mean() + .fillna(0) + ) + summary_columns.append("monetary_value") + + summary_df = customers[summary_columns].astype(float) + + return summary_df.reset_index() diff --git a/tests/clv/datasets/cdnow_transactions.csv b/tests/clv/datasets/cdnow_transactions.csv new file mode 100644 index 000000000..7f723f404 --- /dev/null +++ b/tests/clv/datasets/cdnow_transactions.csv @@ -0,0 +1,6920 @@ +_id,id,date,cds_bought,spent +4,1,19970101,2,29.33 +4,1,19970118,2,29.73 +4,1,19970802,1,14.96 +4,1,19971212,2,26.48 +21,2,19970101,3,63.34 +21,2,19970113,1,11.77 +50,3,19970101,1,6.79 +71,4,19970101,1,13.97 +86,5,19970101,2,23.94 +111,6,19970101,1,35.99 +111,6,19970111,1,32.99 +111,6,19970315,4,77.96 +111,6,19970416,3,59.3 +111,6,19970424,2,134.98 +111,6,19970623,1,91.92 +111,6,19970722,3,47.08 +111,6,19970726,2,71.96 +111,6,19971025,3,78.47 +111,6,19971206,3,83.47 +111,6,19980118,4,84.46 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a/tests/clv/test_utils.py +++ b/tests/clv/test_utils.py @@ -3,9 +3,15 @@ import pymc as pm import pytest import xarray +from pandas.testing import assert_frame_equal from pymc_marketing.clv import BetaGeoModel, GammaGammaModel -from pymc_marketing.clv.utils import customer_lifetime_value, to_xarray +from pymc_marketing.clv.utils import ( + _find_first_transactions, + clv_summary, + customer_lifetime_value, + to_xarray, +) def test_to_xarray(): @@ -89,6 +95,26 @@ def fitted_gg(test_summary_data) -> GammaGammaModel: return model +@pytest.fixture(scope="module") +def transaction_data() -> pd.DataFrame: + d = [ + [1, "2015-01-01", 1], + [1, "2015-02-06", 2], + [2, "2015-01-01", 2], + [3, "2015-01-01", 3], + [3, "2015-01-02", 1], + [3, "2015-01-05", 5], + [4, "2015-01-16", 6], + [4, "2015-02-02", 3], + [4, "2015-02-05", 3], + [5, "2015-01-16", 3], + [5, "2015-01-17", 1], + [5, "2015-01-18", 8], + [6, "2015-02-02", 5], + ] + return pd.DataFrame(d, columns=["id", "date", "monetary_value"]) + + def test_customer_lifetime_value_with_known_values(test_summary_data, fitted_bg): # Test borrowed from # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L527 @@ -188,3 +214,325 @@ def test_customer_lifetime_value_gg_with_bgf(test_summary_data, fitted_gg, fitte ), ) np.testing.assert_equal(ggf_clv.values, utils_clv.values) + + +def test_find_first_transactions_returns_correct_results(transaction_data): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L137 + + today = "2015-02-07" + actual = _find_first_transactions( + transaction_data, "id", "date", observation_period_end=today + ) + expected = pd.DataFrame( + [ + [1, pd.Period("2015-01-01", "D"), True], + [1, pd.Period("2015-02-06", "D"), False], + [2, pd.Period("2015-01-01", "D"), True], + [3, pd.Period("2015-01-01", "D"), True], + [3, pd.Period("2015-01-02", "D"), False], + [3, pd.Period("2015-01-05", "D"), False], + [4, pd.Period("2015-01-16", "D"), True], + [4, pd.Period("2015-02-02", "D"), False], + [4, pd.Period("2015-02-05", "D"), False], + [5, pd.Period("2015-01-16", "D"), True], + [5, pd.Period("2015-01-17", "D"), False], + [5, pd.Period("2015-01-18", "D"), False], + [6, pd.Period("2015-02-02", "D"), True], + ], + columns=["id", "date", "first"], + ) + assert_frame_equal(actual, expected) + + +def test_find_first_transactions_with_specific_non_daily_frequency( + transaction_data, +): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L161 + + today = "2015-02-07" + actual = _find_first_transactions( + transaction_data, + "id", + "date", + observation_period_end=today, + time_unit="W", + ) + expected = pd.DataFrame( + [ + [1, pd.Period("2014-12-29/2015-01-04", "W-SUN"), True], + [1, pd.Period("2015-02-02/2015-02-08", "W-SUN"), False], + [2, pd.Period("2014-12-29/2015-01-04", "W-SUN"), True], + [3, pd.Period("2014-12-29/2015-01-04", "W-SUN"), True], + [3, pd.Period("2015-01-05/2015-01-11", "W-SUN"), False], + [4, pd.Period("2015-01-12/2015-01-18", "W-SUN"), True], + [4, pd.Period("2015-02-02/2015-02-08", "W-SUN"), False], + [5, pd.Period("2015-01-12/2015-01-18", "W-SUN"), True], + [6, pd.Period("2015-02-02/2015-02-08", "W-SUN"), True], + ], + columns=["id", "date", "first"], + index=actual.index, + ) # we shouldn't really care about row ordering or indexing, but assert_frame_equals is strict about it + assert_frame_equal(actual, expected) + + +def test_find_first_transactions_with_monetary_values( + transaction_data, +): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L184 + + today = "2015-02-07" + actual = _find_first_transactions( + transaction_data, + "id", + "date", + "monetary_value", + observation_period_end=today, + ) + expected = pd.DataFrame( + [ + [1, pd.Period("2015-01-01", "D"), 1, True], + [1, pd.Period("2015-02-06", "D"), 2, False], + [2, pd.Period("2015-01-01", "D"), 2, True], + [3, pd.Period("2015-01-01", "D"), 3, True], + [3, pd.Period("2015-01-02", "D"), 1, False], + [3, pd.Period("2015-01-05", "D"), 5, False], + [4, pd.Period("2015-01-16", "D"), 6, True], + [4, pd.Period("2015-02-02", "D"), 3, False], + [4, pd.Period("2015-02-05", "D"), 3, False], + [5, pd.Period("2015-01-16", "D"), 3, True], + [5, pd.Period("2015-01-17", "D"), 1, False], + [5, pd.Period("2015-01-18", "D"), 8, False], + [6, pd.Period("2015-02-02", "D"), 5, True], + ], + columns=["id", "date", "monetary_value", "first"], + ) + assert_frame_equal(actual, expected) + + +def test_find_first_transactions_with_monetary_values_with_specific_non_daily_frequency( + transaction_data, +): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L210 + + today = "2015-02-07" + actual = _find_first_transactions( + transaction_data, + "id", + "date", + "monetary_value", + observation_period_end=today, + time_unit="W", + ) + expected = pd.DataFrame( + [ + [1, pd.Period("2014-12-29/2015-01-04", "W-SUN"), 1, True], + [1, pd.Period("2015-02-02/2015-02-08", "W-SUN"), 2, False], + [2, pd.Period("2014-12-29/2015-01-04", "W-SUN"), 2, True], + [3, pd.Period("2014-12-29/2015-01-04", "W-SUN"), 4, True], + [3, pd.Period("2015-01-05/2015-01-11", "W-SUN"), 5, False], + [4, pd.Period("2015-01-12/2015-01-18", "W-SUN"), 6, True], + [4, pd.Period("2015-02-02/2015-02-08", "W-SUN"), 6, False], + [5, pd.Period("2015-01-12/2015-01-18", "W-SUN"), 12, True], + [6, pd.Period("2015-02-02/2015-02-08", "W-SUN"), 5, True], + ], + columns=["id", "date", "monetary_value", "first"], + ) + assert_frame_equal(actual, expected) + + +def test_clv_summary_returns_correct_results( + transaction_data, +): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L239 + + today = "2015-02-07" + actual = clv_summary(transaction_data, "id", "date", observation_period_end=today) + expected = pd.DataFrame( + [ + [1, 1.0, 36.0, 37.0], + [2, 0.0, 0.0, 37.0], + [3, 2.0, 4.0, 37.0], + [4, 2.0, 20.0, 22.0], + [5, 2.0, 2.0, 22.0], + [6, 0.0, 0.0, 5.0], + ], + columns=["id", "frequency", "recency", "T"], + ) + assert_frame_equal(actual, expected) + + +def test_clv_summary_works_with_string_customer_ids(): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L250 + + d = [ + ["X", "2015-02-01"], + ["X", "2015-02-06"], + ["Y", "2015-01-01"], + ["Y", "2015-01-01"], + ["Y", "2015-01-02"], + ["Y", "2015-01-05"], + ] + df = pd.DataFrame(d, columns=["id", "date"]) + clv_summary(df, "id", "date") + + +def test_clv_summary_works_with_int_customer_ids_and_doesnt_coerce_to_float(): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L263 + + d = [ + [1, "2015-02-01"], + [1, "2015-02-06"], + [1, "2015-01-01"], + [2, "2015-01-01"], + [2, "2015-01-02"], + [2, "2015-01-05"], + ] + df = pd.DataFrame(d, columns=["id", "date"]) + actual = clv_summary(df, "id", "date") + assert actual.index.dtype == "int64" + + +def test_clv_summary_with_specific_datetime_format( + transaction_data, +): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L279 + + transaction_data["date"] = transaction_data["date"].map( + lambda x: x.replace("-", "") + ) + format = "%Y%m%d" + today = "20150207" + actual = clv_summary( + transaction_data, + "id", + "date", + observation_period_end=today, + datetime_format=format, + ) + expected = pd.DataFrame( + [ + [1, 1.0, 36.0, 37.0], + [2, 0.0, 0.0, 37.0], + [3, 2.0, 4.0, 37.0], + [4, 2.0, 20.0, 22.0], + [5, 2.0, 2.0, 22.0], + [6, 0.0, 0.0, 5.0], + ], + columns=["id", "frequency", "recency", "T"], + ) + assert_frame_equal(actual, expected) + + +def test_summary_date_from_transaction_data_with_specific_non_daily_frequency( + transaction_data, +): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L292 + + today = "20150207" + actual = clv_summary( + transaction_data, + "id", + "date", + observation_period_end=today, + time_unit="W", + ) + expected = pd.DataFrame( + [ + [1, 1.0, 5.0, 5.0], + [2, 0.0, 0.0, 5.0], + [3, 1.0, 1.0, 5.0], + [4, 1.0, 3.0, 3.0], + [5, 0.0, 0.0, 3.0], + [6, 0.0, 0.0, 0.0], + ], + columns=["id", "frequency", "recency", "T"], + ) + assert_frame_equal(actual, expected) + + +def test_summary_date_from_transaction_with_monetary_values( + transaction_data, +): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L311 + + today = "20150207" + actual = clv_summary( + transaction_data, + "id", + "date", + monetary_value_col="monetary_value", + observation_period_end=today, + ) + expected = pd.DataFrame( + [ + [1, 1.0, 36.0, 37.0, 2], + [2, 0.0, 0.0, 37.0, 0], + [3, 2.0, 4.0, 37.0, 3], + [4, 2.0, 20.0, 22.0, 3], + [5, 2.0, 2.0, 22.0, 4.5], + [6, 0.0, 0.0, 5.0, 0], + ], + columns=["id", "frequency", "recency", "T", "monetary_value"], + ) + assert_frame_equal(actual, expected) + + +def test_clv_summary_will_choose_the_correct_first_order_to_drop_in_monetary_transactions(): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L334 + + cust = pd.Series([2, 2, 2]) + dates_ordered = pd.to_datetime( + pd.Series(["2014-03-14 00:00:00", "2014-04-09 00:00:00", "2014-05-21 00:00:00"]) + ) + sales = pd.Series([10, 20, 25]) + transaction_data = pd.DataFrame({"date": dates_ordered, "id": cust, "sales": sales}) + summary_ordered_data = clv_summary(transaction_data, "id", "date", "sales") + + dates_unordered = pd.to_datetime( + pd.Series(["2014-04-09 00:00:00", "2014-03-14 00:00:00", "2014-05-21 00:00:00"]) + ) + sales = pd.Series([20, 10, 25]) + transaction_data = pd.DataFrame( + {"date": dates_unordered, "id": cust, "sales": sales} + ) + summary_unordered_data = clv_summary(transaction_data, "id", "date", "sales") + + assert_frame_equal(summary_ordered_data, summary_unordered_data) + assert summary_ordered_data["monetary_value"].loc[0] == 22.5 + + +def test_summary_statistics_are_identical_to_hardie_paper_confirming_correct_aggregations( + cdnow_trans, +): + # Test borrowed from + # https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/tests/test_utils.py#L353 + + # see http://brucehardie.com/papers/rfm_clv_2005-02-16.pdf + # RFM and CLV: Using Iso-value Curves for Customer Base Analysis + summary = clv_summary( + cdnow_trans, + "id", + "date", + "spent", + observation_period_end="19971001", + datetime_format="%Y%m%d", + ) + results = summary[summary["frequency"] > 0]["monetary_value"].describe() + + assert np.round(results.loc["mean"]) == 35 + assert np.round(results.loc["std"]) == 30 + assert np.round(results.loc["min"]) == 3 + assert np.round(results.loc["50%"]) == 27 + assert np.round(results.loc["max"]) == 300 + assert np.round(results.loc["count"]) == 946 diff --git a/conftest.py b/tests/conftest.py similarity index 66% rename from conftest.py rename to tests/conftest.py index e446d0a1c..a750dade2 100644 --- a/conftest.py +++ b/tests/conftest.py @@ -1,3 +1,4 @@ +import pandas as pd import pytest @@ -19,3 +20,13 @@ def pytest_collection_modifyitems(config, items): for item in items: if "slow" in item.keywords: item.add_marker(skip_slow) + + +@pytest.fixture(scope="module") +def cdnow_trans() -> pd.DataFrame: + """ + Load CDNOW sample transaction data into a Pandas dataframe. + + Data source: https://www.brucehardie.com/datasets/ + """ + return pd.read_csv("tests/clv/datasets/cdnow_transactions.csv")