python/pysarplus/SARPlus.py
def fit(
        self,
        df,
        similarity_type="jaccard",
        time_decay_coefficient=30,
        time_now=None,
        timedecay_formula=False,
        threshold=1,
    ):
        """Main fit method for SAR. Expects the dataframes to have row_id, col_id columns which are indexes,
        i.e. contain the sequential integer index of the original alphanumeric user and item IDs.
        Dataframe also contains rating and timestamp as floats; timestamp is in seconds since Epoch by default.

        Arguments:
            df (pySpark.DataFrame): input dataframe which contains the index of users and items. """

        # threshold - items below this number get set to zero in coocurrence counts
        assert threshold > 0

        df.createOrReplaceTempView("{prefix}df_train_input".format(**self.header))

        if timedecay_formula:
            # WARNING: previously we would take the last value in training dataframe and set it
            # as a matrix U element
            # for each user-item pair. Now with time decay, we compute a sum over ratings given
            # by a user in the case
            # when T=np.inf, so user gets a cumulative sum of ratings for a particular item and
            # not the last rating.
            # Time Decay
            # do a group by on user item pairs and apply the formula for time decay there
            # Time T parameter is in days and input time is in seconds
            # so we do dt/60/(T*24*60)=dt/(T*24*3600)
            # the folling is the query which we want to run

            query = self.f(
            """
            SELECT
                 {col_user}, {col_item}, 
                 SUM({col_rating} * EXP(-log(2) * (latest_timestamp - CAST({col_timestamp} AS long)) / ({time_decay_coefficient} * 3600 * 24))) as {col_rating}
            FROM {prefix}df_train_input,
                 (SELECT CAST(MAX({col_timestamp}) AS long) latest_timestamp FROM {prefix}df_train_input)
            GROUP BY {col_user}, {col_item} 
            CLUSTER BY {col_user} 
            """,
                time_now=time_now,
                time_decay_coefficient=time_decay_coefficient,
            )

            # replace with timedecayed version
            df = self.spark.sql(query)
        else:
            # since SQL is case insensitive, this check needs to be performed similar
            if self.header['col_timestamp'].lower() in [s.name.lower() for s in df.schema]:
                # we need to de-duplicate items by using the latest item
                query = self.f(
                """
                SELECT {col_user}, {col_item}, {col_rating}
                FROM
                (
                SELECT
                    {col_user}, {col_item}, {col_rating}, 
                    ROW_NUMBER() OVER (PARTITION BY {col_user}, {col_item} ORDER BY {col_timestamp} DESC) latest
                FROM {prefix}df_train_input
                )
                WHERE latest = 1
                """
                )
                
                df = self.spark.sql(query)

        df.createOrReplaceTempView(self.f("{prefix}df_train"))

        log.info("sarplus.fit 1/2: compute item cooccurences...")

        # compute cooccurrence above minimum threshold
        query = self.f(
            """
        SELECT A.{col_item} i1, B.{col_item} i2, COUNT(*) value
        FROM   {prefix}df_train A INNER JOIN {prefix}df_train B
               ON A.{col_user} = B.{col_user} AND A.{col_item} <= b.{col_item}  
        GROUP  BY A.{col_item}, B.{col_item}
        HAVING COUNT(*) >= {threshold}
        CLUSTER BY i1, i2
        """,
            threshold=threshold,
        )

        item_cooccurrence = self.spark.sql(query)
        item_cooccurrence.write.mode("overwrite").saveAsTable(
            self.f("{prefix}item_cooccurrence")
        )

        # compute the diagonal used later for Jaccard and Lift
        if similarity_type == SIM_LIFT or similarity_type == SIM_JACCARD:
            item_marginal = self.spark.sql(
                self.f(
                    "SELECT i1 i, value AS margin FROM {prefix}item_cooccurrence WHERE i1 = i2"
                )
            )
            item_marginal.createOrReplaceTempView(self.f("{prefix}item_marginal"))

        if similarity_type == SIM_COOCCUR:
            self.item_similarity = item_cooccurrence
        elif similarity_type == SIM_JACCARD:
            query = self.f(
            """
            SELECT i1, i2, value / (M1.margin + M2.margin - value) AS value
            FROM {prefix}item_cooccurrence A 
                INNER JOIN {prefix}item_marginal M1 ON A.i1 = M1.i 
                INNER JOIN {prefix}item_marginal M2 ON A.i2 = M2.i
            CLUSTER BY i1, i2
            """
            )
            self.item_similarity = self.spark.sql(query)
        elif similarity_type == SIM_LIFT:
            query = self.f(
            """
            SELECT i1, i2, value / (M1.margin * M2.margin) AS value
            FROM {prefix}item_cooccurrence A 
                INNER JOIN {prefix}item_marginal M1 ON A.i1 = M1.i 
                INNER JOIN {prefix}item_marginal M2 ON A.i2 = M2.i
            CLUSTER BY i1, i2
            """
            )
            self.item_similarity = self.spark.sql(query)
        else:
            raise ValueError("Unknown similarity type: {0}".format(similarity_type))


        # store upper triangular
        log.info("sarplus.fit 2/2: compute similiarity metric %s..." % similarity_type)
        self.item_similarity.write.mode("overwrite").saveAsTable(
            self.f("{prefix}item_similarity_upper")
        )

        # expand upper triangular to full matrix

        query = self.f(
        """
        SELECT i1, i2, value
        FROM
        (
          (SELECT i1, i2, value FROM {prefix}item_similarity_upper)
          UNION ALL
          (SELECT i2 i1, i1 i2, value FROM {prefix}item_similarity_upper WHERE i1 <> i2)
        )
        CLUSTER BY i1
        """
        )

        self.item_similarity = self.spark.sql(query)
        self.item_similarity.write.mode("overwrite").saveAsTable(
            self.f("{prefix}item_similarity")
        )

        # free space
        self.spark.sql(self.f("DROP TABLE {prefix}item_cooccurrence"))
        self.spark.sql(self.f("DROP TABLE {prefix}item_similarity_upper"))

        self.item_similarity = self.spark.table(self.f("{prefix}item_similarity"))
def fit(
        self,
        df,
        similarity_type="jaccard",
        time_decay_coefficient=30,
        time_now=None,
        timedecay_formula=False,
        threshold=1,
    ):
        """Main fit method for SAR. Expects the dataframes to have row_id, col_id columns which are indexes,
        i.e. contain the sequential integer index of the original alphanumeric user and item IDs.
        Dataframe also contains rating and timestamp as floats; timestamp is in seconds since Epoch by default.

        Arguments:
            df (pySpark.DataFrame): input dataframe which contains the index of users and items. """

        # threshold - items below this number get set to zero in coocurrence counts
        assert threshold > 0

        df.createOrReplaceTempView("{prefix}df_train_input".format(**self.header))

        if timedecay_formula:
            # WARNING: previously we would take the last value in training dataframe and set it
            # as a matrix U element
            # for each user-item pair. Now with time decay, we compute a sum over ratings given
            # by a user in the case
            # when T=np.inf, so user gets a cumulative sum of ratings for a particular item and
            # not the last rating.
            # Time Decay
            # do a group by on user item pairs and apply the formula for time decay there
            # Time T parameter is in days and input time is in seconds
            # so we do dt/60/(T*24*60)=dt/(T*24*3600)
            # the folling is the query which we want to run

            query = self.f(
            """
            SELECT
                 {col_user}, {col_item}, 
                 SUM({col_rating} * EXP(-log(2) * (latest_timestamp - CAST({col_timestamp} AS long)) / ({time_decay_coefficient} * 3600 * 24))) as {col_rating}
            FROM {prefix}df_train_input,
                 (SELECT CAST(MAX({col_timestamp}) AS long) latest_timestamp FROM {prefix}df_train_input)
            GROUP BY {col_user}, {col_item} 
            CLUSTER BY {col_user} 
            """,
                time_now=time_now,
                time_decay_coefficient=time_decay_coefficient,
            )

            # replace with timedecayed version
            df = self.spark.sql(query)
        else:
            # since SQL is case insensitive, this check needs to be performed similar
            if self.header['col_timestamp'].lower() in [s.name.lower() for s in df.schema]:
                # we need to de-duplicate items by using the latest item
                query = self.f(
                """
                SELECT {col_user}, {col_item}, {col_rating}
                FROM
                (
                SELECT
                    {col_user}, {col_item}, {col_rating}, 
                    ROW_NUMBER() OVER (PARTITION BY {col_user}, {col_item} ORDER BY {col_timestamp} DESC) latest
                FROM {prefix}df_train_input
                )
                WHERE latest = 1
                """
                )
                
                df = self.spark.sql(query)

        df.createOrReplaceTempView(self.f("{prefix}df_train"))

        log.info("sarplus.fit 1/2: compute item cooccurences...")

        # compute cooccurrence above minimum threshold
        query = self.f(
            """
        SELECT A.{col_item} i1, B.{col_item} i2, COUNT(*) value
        FROM   {prefix}df_train A INNER JOIN {prefix}df_train B
               ON A.{col_user} = B.{col_user} AND A.{col_item} <= b.{col_item}  
        GROUP  BY A.{col_item}, B.{col_item}
        HAVING COUNT(*) >= {threshold}
        CLUSTER BY i1, i2
        """,
            threshold=threshold,
        )

        item_cooccurrence = self.spark.sql(query)
        item_cooccurrence.write.mode("overwrite").saveAsTable(
            self.f("{prefix}item_cooccurrence")
        )

        # compute the diagonal used later for Jaccard and Lift
        if similarity_type == SIM_LIFT or similarity_type == SIM_JACCARD:
            item_marginal = self.spark.sql(
                self.f(
                    "SELECT i1 i, value AS margin FROM {prefix}item_cooccurrence WHERE i1 = i2"
                )
            )
            item_marginal.createOrReplaceTempView(self.f("{prefix}item_marginal"))

        if similarity_type == SIM_COOCCUR:
            self.item_similarity = item_cooccurrence
        elif similarity_type == SIM_JACCARD:
            query = self.f(
            """
            SELECT i1, i2, value / (M1.margin + M2.margin - value) AS value
            FROM {prefix}item_cooccurrence A 
                INNER JOIN {prefix}item_marginal M1 ON A.i1 = M1.i 
                INNER JOIN {prefix}item_marginal M2 ON A.i2 = M2.i
            CLUSTER BY i1, i2
            """
            )
            self.item_similarity = self.spark.sql(query)
        elif similarity_type == SIM_LIFT:
            query = self.f(
            """
            SELECT i1, i2, value / (M1.margin * M2.margin) AS value
            FROM {prefix}item_cooccurrence A 
                INNER JOIN {prefix}item_marginal M1 ON A.i1 = M1.i 
                INNER JOIN {prefix}item_marginal M2 ON A.i2 = M2.i
            CLUSTER BY i1, i2
            """
            )
            self.item_similarity = self.spark.sql(query)
        else:
            raise ValueError("Unknown similarity type: {0}".format(similarity_type))


        # store upper triangular
        log.info("sarplus.fit 2/2: compute similiarity metric %s..." % similarity_type)
        self.item_similarity.write.mode("overwrite").saveAsTable(
            self.f("{prefix}item_similarity_upper")
        )

        # expand upper triangular to full matrix

        query = self.f(
        """
        SELECT i1, i2, value
        FROM
        (
          (SELECT i1, i2, value FROM {prefix}item_similarity_upper)
          UNION ALL
          (SELECT i2 i1, i1 i2, value FROM {prefix}item_similarity_upper WHERE i1 <> i2)
        )
        CLUSTER BY i1
        """
        )

        self.item_similarity = self.spark.sql(query)
        self.item_similarity.write.mode("overwrite").saveAsTable(
            self.f("{prefix}item_similarity")
        )

        # free space
        self.spark.sql(self.f("DROP TABLE {prefix}item_cooccurrence"))
        self.spark.sql(self.f("DROP TABLE {prefix}item_similarity_upper"))

        self.item_similarity = self.spark.table(self.f("{prefix}item_similarity"))
def fit ( self , df , similarity_type = "jaccard" , time_decay_coefficient = 30 , time_now = None , timedecay_formula = False , threshold = 1 , ) : # threshold - items below this number get set to zero in coocurrence counts assert threshold > 0 df . createOrReplaceTempView ( "{prefix}df_train_input" . format ( * * self . header ) ) if timedecay_formula : # WARNING: previously we would take the last value in training dataframe and set it # as a matrix U element # for each user-item pair. Now with time decay, we compute a sum over ratings given # by a user in the case # when T=np.inf, so user gets a cumulative sum of ratings for a particular item and # not the last rating. # Time Decay # do a group by on user item pairs and apply the formula for time decay there # Time T parameter is in days and input time is in seconds # so we do dt/60/(T*24*60)=dt/(T*24*3600) # the folling is the query which we want to run query = self . f ( """
            SELECT
                 {col_user}, {col_item}, 
                 SUM({col_rating} * EXP(-log(2) * (latest_timestamp - CAST({col_timestamp} AS long)) / ({time_decay_coefficient} * 3600 * 24))) as {col_rating}
            FROM {prefix}df_train_input,
                 (SELECT CAST(MAX({col_timestamp}) AS long) latest_timestamp FROM {prefix}df_train_input)
            GROUP BY {col_user}, {col_item} 
            CLUSTER BY {col_user} 
            """ , time_now = time_now , time_decay_coefficient = time_decay_coefficient , ) # replace with timedecayed version df = self . spark . sql ( query ) else : # since SQL is case insensitive, this check needs to be performed similar if self . header [ 'col_timestamp' ] . lower ( ) in [ s . name . lower ( ) for s in df . schema ] : # we need to de-duplicate items by using the latest item query = self . f ( """
                SELECT {col_user}, {col_item}, {col_rating}
                FROM
                (
                SELECT
                    {col_user}, {col_item}, {col_rating}, 
                    ROW_NUMBER() OVER (PARTITION BY {col_user}, {col_item} ORDER BY {col_timestamp} DESC) latest
                FROM {prefix}df_train_input
                )
                WHERE latest = 1
                """ ) df = self . spark . sql ( query ) df . createOrReplaceTempView ( self . f ( "{prefix}df_train" ) ) log . info ( "sarplus.fit 1/2: compute item cooccurences..." ) # compute cooccurrence above minimum threshold query = self . f ( """
        SELECT A.{col_item} i1, B.{col_item} i2, COUNT(*) value
        FROM   {prefix}df_train A INNER JOIN {prefix}df_train B
               ON A.{col_user} = B.{col_user} AND A.{col_item} <= b.{col_item}  
        GROUP  BY A.{col_item}, B.{col_item}
        HAVING COUNT(*) >= {threshold}
        CLUSTER BY i1, i2
        """ , threshold = threshold , ) item_cooccurrence = self . spark . sql ( query ) item_cooccurrence . write . mode ( "overwrite" ) . saveAsTable ( self . f ( "{prefix}item_cooccurrence" ) ) # compute the diagonal used later for Jaccard and Lift if similarity_type == SIM_LIFT or similarity_type == SIM_JACCARD : item_marginal = self . spark . sql ( self . f ( "SELECT i1 i, value AS margin FROM {prefix}item_cooccurrence WHERE i1 = i2" ) ) item_marginal . createOrReplaceTempView ( self . f ( "{prefix}item_marginal" ) ) if similarity_type == SIM_COOCCUR : self . item_similarity = item_cooccurrence elif similarity_type == SIM_JACCARD : query = self . f ( """
            SELECT i1, i2, value / (M1.margin + M2.margin - value) AS value
            FROM {prefix}item_cooccurrence A 
                INNER JOIN {prefix}item_marginal M1 ON A.i1 = M1.i 
                INNER JOIN {prefix}item_marginal M2 ON A.i2 = M2.i
            CLUSTER BY i1, i2
            """ ) self . item_similarity = self . spark . sql ( query ) elif similarity_type == SIM_LIFT : query = self . f ( """
            SELECT i1, i2, value / (M1.margin * M2.margin) AS value
            FROM {prefix}item_cooccurrence A 
                INNER JOIN {prefix}item_marginal M1 ON A.i1 = M1.i 
                INNER JOIN {prefix}item_marginal M2 ON A.i2 = M2.i
            CLUSTER BY i1, i2
            """ ) self . item_similarity = self . spark . sql ( query ) else : raise ValueError ( "Unknown similarity type: {0}" . format ( similarity_type ) ) # store upper triangular log . info ( "sarplus.fit 2/2: compute similiarity metric %s..." % similarity_type ) self . item_similarity . write . mode ( "overwrite" ) . saveAsTable ( self . f ( "{prefix}item_similarity_upper" ) ) # expand upper triangular to full matrix query = self . f ( """
        SELECT i1, i2, value
        FROM
        (
          (SELECT i1, i2, value FROM {prefix}item_similarity_upper)
          UNION ALL
          (SELECT i2 i1, i1 i2, value FROM {prefix}item_similarity_upper WHERE i1 <> i2)
        )
        CLUSTER BY i1
        """ ) self . item_similarity = self . spark . sql ( query ) self . item_similarity . write . mode ( "overwrite" ) . saveAsTable ( self . f ( "{prefix}item_similarity" ) ) # free space self . spark . sql ( self . f ( "DROP TABLE {prefix}item_cooccurrence" ) ) self . spark . sql ( self . f ( "DROP TABLE {prefix}item_similarity_upper" ) ) self . item_similarity = self . spark . table ( self . f ( "{prefix}item_similarity" ) )
Main fit method for SAR. Expects the dataframes to have row_id, col_id columns which are indexes,
        i.e. contain the sequential integer index of the original alphanumeric user and item IDs.
        Dataframe also contains rating and timestamp as floats; timestamp is in seconds since Epoch by default.

        Arguments:
            df (pySpark.DataFrame): input dataframe which contains the index of users and items.
Main fit method for SAR . Expects the dataframes to have row_id col_id columns which are indexes i . e . contain the sequential integer index of the original alphanumeric user and item IDs . Dataframe also contains rating and timestamp as floats ; timestamp is in seconds since Epoch by default .