DOC-5225 testable probabilistic data type examples

doctests/home_prob_dts.py

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+# EXAMPLE: home_prob_dts
+"""
+Probabilistic data type examples:
+ https://redis.io/docs/latest/develop/connect/clients/python/redis-py/prob
+"""
+
+# HIDE_START
+import redis
+r = redis.Redis(decode_responses=True)
+# HIDE_END
+# REMOVE_START
+r.delete(
+    "recorded_users", "other_users",
+    "group:1", "group:2", "both_groups",
+    "items_sold",
+    "male_heights", "female_heights", "all_heights",
+    "top_3_songs"
+)
+# REMOVE_END
+
+# STEP_START bloom
+res1 = r.bf().madd("recorded_users", "andy", "cameron", "david", "michelle")
+print(res1)  # >>> [1, 1, 1, 1]
+
+res2 = r.bf().exists("recorded_users", "cameron")
+print(res2)  # >>> 1
+
+res3 = r.bf().exists("recorded_users", "kaitlyn")
+print(res3)  # >>> 0
+# STEP_END
+# REMOVE_START
+assert res1 == [1, 1, 1, 1]
+assert res2 == 1
+assert res3 == 0
+# REMOVE_END
+
+# STEP_START cuckoo
+res4 = r.cf().add("other_users", "paolo")
+print(res4)  # >>> 1
+
+res5 = r.cf().add("other_users", "kaitlyn")
+print(res5)  # >>> 1
+
+res6 = r.cf().add("other_users", "rachel")
+print(res6)  # >>> 1
+
+res7 = r.cf().mexists("other_users", "paolo", "rachel", "andy")
+print(res7)  # >>> [1, 1, 0]
+
+res8 = r.cf().delete("other_users", "paolo")
+print(res8)  # >>> 1
+
+res9 = r.cf().exists("other_users", "paolo")
+print(res9)  # >>> 0
+# STEP_END
+# REMOVE_START
+assert res4 == 1
+assert res5 == 1
+assert res6 == 1
+assert res7 == [1, 1, 0]
+assert res8 == 1
+assert res9 == 0
+# REMOVE_END
+
+# STEP_START hyperloglog
+res10 = r.pfadd("group:1", "andy", "cameron", "david")
+print(res10)  # >>> 1
+
+res11 = r.pfcount("group:1")
+print(res11)  # >>> 3
+
+res12 = r.pfadd("group:2", "kaitlyn", "michelle", "paolo", "rachel")
+print(res12)  # >>> 1
+
+res13 = r.pfcount("group:2")
+print(res13)  # >>> 4
+
+res14 = r.pfmerge("both_groups", "group:1", "group:2")
+print(res14)  # >>> True
+
+res15 = r.pfcount("both_groups")
+print(res15)  # >>> 7
+# STEP_END
+# REMOVE_START
+assert res10 == 1
+assert res11 == 3
+assert res12 == 1
+assert res13 == 4
+assert res14
+assert res15 == 7
+# REMOVE_END
+
+# STEP_START cms
+# Specify that you want to keep the counts within 0.01
+# (1%) of the true value with a 0.005 (0.5%) chance
+# of going outside this limit.
+res16 = r.cms().initbyprob("items_sold", 0.01, 0.005)
+print(res16)  # >>> True
+
+# The parameters for `incrby()` are two lists. The count
+# for each item in the first list is incremented by the
+# value at the same index in the second list.
+res17 = r.cms().incrby(
+    "items_sold",
+    ["bread", "tea", "coffee", "beer"],  # Items sold
+    [300, 200, 200, 100]
+)
+print(res17)  # >>> [300, 200, 200, 100]
+
+res18 = r.cms().incrby(
+    "items_sold",
+    ["bread", "coffee"],
+    [100, 150]
+)
+print(res18)  # >>> [400, 350]
+
+res19 = r.cms().query("items_sold", "bread", "tea", "coffee", "beer")
+print(res19)  # >>> [400, 200, 350, 100]
+# STEP_END
+# REMOVE_START
+assert res16
+assert res17 == [300, 200, 200, 100]
+assert res18 == [400, 350]
+assert res19 == [400, 200, 350, 100]
+# REMOVE_END
+
+# STEP_START tdigest
+res20 = r.tdigest().create("male_heights")
+print(res20)  # >>> True
+
+res21 = r.tdigest().add(
+    "male_heights",
+    [175.5, 181, 160.8, 152, 177, 196, 164]
+)
+print(res21)  # >>> OK
+
+res22 = r.tdigest().min("male_heights")
+print(res22)  # >>> 152.0
+
+res23 = r.tdigest().max("male_heights")
+print(res23)  # >>> 196.0
+
+res24 = r.tdigest().quantile("male_heights", 0.75)
+print(res24)  # >>> 181
+
+# Note that the CDF value for 181 is not exactly
+# 0.75. Both values are estimates.
+res25 = r.tdigest().cdf("male_heights", 181)
+print(res25)  # >>> [0.7857142857142857]
+
+res26 = r.tdigest().create("female_heights")
+print(res26)  # >>> True
+
+res27 = r.tdigest().add(
+    "female_heights",
+    [155.5, 161, 168.5, 170, 157.5, 163, 171]
+)
+print(res27)  # >>> OK
+
+res28 = r.tdigest().quantile("female_heights", 0.75)
+print(res28)  # >>> [170]
+
+res29 = r.tdigest().merge(
+    "all_heights", 2, "male_heights", "female_heights"
+)
+print(res29)  # >>> OK
+
+res30 = r.tdigest().quantile("all_heights", 0.75)
+print(res30)  # >>> [175.5]
+# STEP_END
+# REMOVE_START
+assert res20
+assert res21 == "OK"
+assert res22 == 152.0
+assert res23 == 196.0
+assert res24 == [181]
+assert res25 == [0.7857142857142857]
+assert res26
+assert res27 == "OK"
+assert res28 == [170]
+assert res29 == "OK"
+assert res30 == [175.5]
+# REMOVE_END
+
+# STEP_START topk
+# The `reserve()` method creates the Top-K object with
+# the given key. The parameters are the number of items
+# in the ranking and values for `width`, `depth`, and
+# `decay`, described in the Top-K reference page.
+res31 = r.topk().reserve("top_3_songs", 3, 7, 8, 0.9)
+print(res31)  # >>> True
+
+# The parameters for `incrby()` are two lists. The count
+# for each item in the first list is incremented by the
+# value at the same index in the second list.
+res32 = r.topk().incrby(
+    "top_3_songs",
+    [
+        "Starfish Trooper",
+        "Only one more time",
+        "Rock me, Handel",
+        "How will anyone know?",
+        "Average lover",
+        "Road to everywhere"
+    ],
+    [
+        3000,
+        1850,
+        1325,
+        3890,
+        4098,
+        770
+    ]
+)
+print(res32)
+# >>> [None, None, None, 'Rock me, Handel', 'Only one more time', None]
+
+res33 = r.topk().list("top_3_songs")
+print(res33)
+# >>> ['Average lover', 'How will anyone know?', 'Starfish Trooper']
+
+res34 = r.topk().query(
+    "top_3_songs", "Starfish Trooper", "Road to everywhere"
+)
+print(res34)  # >>> [1, 0]
+# STEP_END
+# REMOVE_START
+assert res31
+assert res32 == [None, None, None, 'Rock me, Handel', 'Only one more time', None]
+assert res33 == ['Average lover', 'How will anyone know?', 'Starfish Trooper']
+assert res34 == [1, 0]
+# REMOVE_END