Model.to_memmodel¶

In [ ]:

Model.to_memmodel()

Converts a specified Vertica model to a memModel model.

Returns¶

object : memModel model.

Example¶

In [5]:

from verticapy.learn.ensemble import RandomForestClassifier
model = RandomForestClassifier(name = "public.rf_iris",
                               n_estimators = 3,
                               max_depth = 3)
model.fit("public.iris", ["PetalLengthCm", 
                          "SepalLengthCm", 
                          "SepalWidthCm"],
                          "Species")
mmodel = model.to_memmodel()
mmodel

Out[5]:

<RandomForestClassifier>

ntrees = 3

In [6]:

# Scoring using Standard SQL 
mmodel.predict_sql(["PetalLengthCm", 
                    "SepalLengthCm", 
                    "SepalWidthCm"])

Out[6]:

"CASE WHEN PetalLengthCm IS NULL OR SepalLengthCm IS NULL OR SepalWidthCm IS NULL THEN NULL WHEN ((CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN SepalLengthCm < 6.1 THEN (CASE WHEN SepalLengthCm < 5.65 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN SepalWidthCm < 2.525 THEN 0.0 ELSE 1.0 END) END) END) + (CASE WHEN SepalLengthCm < 5.425 THEN (CASE WHEN SepalWidthCm < 2.75 THEN (CASE WHEN PetalLengthCm < 3.95 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END) ELSE (CASE WHEN SepalLengthCm < 6.2125 THEN (CASE WHEN PetalLengthCm < 4.871875 THEN 0.0 ELSE 1.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 1.0 END) END) END) + (CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN PetalLengthCm < 4.871875 THEN (CASE WHEN SepalLengthCm < 4.975 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 1.0 ELSE 1.0 END) END) END)) / 3 >= ((CASE WHEN PetalLengthCm < 1.921875 THEN 1.0 ELSE (CASE WHEN SepalLengthCm < 6.1 THEN (CASE WHEN SepalLengthCm < 5.65 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN SepalWidthCm < 2.525 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN SepalLengthCm < 5.425 THEN (CASE WHEN SepalWidthCm < 2.75 THEN (CASE WHEN PetalLengthCm < 3.95 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END) ELSE (CASE WHEN SepalLengthCm < 6.2125 THEN (CASE WHEN PetalLengthCm < 4.871875 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN PetalLengthCm < 1.921875 THEN 1.0 ELSE (CASE WHEN PetalLengthCm < 4.871875 THEN (CASE WHEN SepalLengthCm < 4.975 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 0.0 END) END) END)) / 3 AND ((CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN SepalLengthCm < 6.1 THEN (CASE WHEN SepalLengthCm < 5.65 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN SepalWidthCm < 2.525 THEN 0.0 ELSE 1.0 END) END) END) + (CASE WHEN SepalLengthCm < 5.425 THEN (CASE WHEN SepalWidthCm < 2.75 THEN (CASE WHEN PetalLengthCm < 3.95 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END) ELSE (CASE WHEN SepalLengthCm < 6.2125 THEN (CASE WHEN PetalLengthCm < 4.871875 THEN 0.0 ELSE 1.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 1.0 END) END) END) + (CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN PetalLengthCm < 4.871875 THEN (CASE WHEN SepalLengthCm < 4.975 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 1.0 ELSE 1.0 END) END) END)) / 3 >= ((CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN SepalLengthCm < 6.1 THEN (CASE WHEN SepalLengthCm < 5.65 THEN 1.0 ELSE 1.0 END) ELSE (CASE WHEN SepalWidthCm < 2.525 THEN 1.0 ELSE 0.0 END) END) END) + (CASE WHEN SepalLengthCm < 5.425 THEN (CASE WHEN SepalWidthCm < 2.75 THEN (CASE WHEN PetalLengthCm < 3.95 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END) ELSE (CASE WHEN SepalLengthCm < 6.2125 THEN (CASE WHEN PetalLengthCm < 4.871875 THEN 1.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 1.0 ELSE 0.0 END) END) END) + (CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN PetalLengthCm < 4.871875 THEN (CASE WHEN SepalLengthCm < 4.975 THEN 1.0 ELSE 1.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 0.0 END) END) END)) / 3 THEN 'Iris-virginica' WHEN ((CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN SepalLengthCm < 6.1 THEN (CASE WHEN SepalLengthCm < 5.65 THEN 1.0 ELSE 1.0 END) ELSE (CASE WHEN SepalWidthCm < 2.525 THEN 1.0 ELSE 0.0 END) END) END) + (CASE WHEN SepalLengthCm < 5.425 THEN (CASE WHEN SepalWidthCm < 2.75 THEN (CASE WHEN PetalLengthCm < 3.95 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END) ELSE (CASE WHEN SepalLengthCm < 6.2125 THEN (CASE WHEN PetalLengthCm < 4.871875 THEN 1.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 1.0 ELSE 0.0 END) END) END) + (CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN PetalLengthCm < 4.871875 THEN (CASE WHEN SepalLengthCm < 4.975 THEN 1.0 ELSE 1.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 0.0 END) END) END)) / 3 >= ((CASE WHEN PetalLengthCm < 1.921875 THEN 1.0 ELSE (CASE WHEN SepalLengthCm < 6.1 THEN (CASE WHEN SepalLengthCm < 5.65 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN SepalWidthCm < 2.525 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN SepalLengthCm < 5.425 THEN (CASE WHEN SepalWidthCm < 2.75 THEN (CASE WHEN PetalLengthCm < 3.95 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END) ELSE (CASE WHEN SepalLengthCm < 6.2125 THEN (CASE WHEN PetalLengthCm < 4.871875 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN PetalLengthCm < 1.921875 THEN 1.0 ELSE (CASE WHEN PetalLengthCm < 4.871875 THEN (CASE WHEN SepalLengthCm < 4.975 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 0.0 END) END) END)) / 3 THEN 'Iris-versicolor' ELSE 'Iris-setosa' END"

In [7]:

# Computing probabilites using Standard SQL 
mmodel.predict_proba_sql(["PetalLengthCm", 
                          "SepalLengthCm", 
                          "SepalWidthCm"])

Out[7]:

['((CASE WHEN PetalLengthCm < 1.921875 THEN 1.0 ELSE (CASE WHEN SepalLengthCm < 6.1 THEN (CASE WHEN SepalLengthCm < 5.65 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN SepalWidthCm < 2.525 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN SepalLengthCm < 5.425 THEN (CASE WHEN SepalWidthCm < 2.75 THEN (CASE WHEN PetalLengthCm < 3.95 THEN 0.0 ELSE 0.0 END) ELSE 1.0 END) ELSE (CASE WHEN SepalLengthCm < 6.2125 THEN (CASE WHEN PetalLengthCm < 4.871875 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN PetalLengthCm < 1.921875 THEN 1.0 ELSE (CASE WHEN PetalLengthCm < 4.871875 THEN (CASE WHEN SepalLengthCm < 4.975 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 0.0 END) END) END)) / 3',
 '((CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN SepalLengthCm < 6.1 THEN (CASE WHEN SepalLengthCm < 5.65 THEN 1.0 ELSE 1.0 END) ELSE (CASE WHEN SepalWidthCm < 2.525 THEN 1.0 ELSE 0.0 END) END) END) + (CASE WHEN SepalLengthCm < 5.425 THEN (CASE WHEN SepalWidthCm < 2.75 THEN (CASE WHEN PetalLengthCm < 3.95 THEN 1.0 ELSE 0.0 END) ELSE 0.0 END) ELSE (CASE WHEN SepalLengthCm < 6.2125 THEN (CASE WHEN PetalLengthCm < 4.871875 THEN 1.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 1.0 ELSE 0.0 END) END) END) + (CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN PetalLengthCm < 4.871875 THEN (CASE WHEN SepalLengthCm < 4.975 THEN 1.0 ELSE 1.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 0.0 END) END) END)) / 3',
 '((CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN SepalLengthCm < 6.1 THEN (CASE WHEN SepalLengthCm < 5.65 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN SepalWidthCm < 2.525 THEN 0.0 ELSE 1.0 END) END) END) + (CASE WHEN SepalLengthCm < 5.425 THEN (CASE WHEN SepalWidthCm < 2.75 THEN (CASE WHEN PetalLengthCm < 3.95 THEN 0.0 ELSE 1.0 END) ELSE 0.0 END) ELSE (CASE WHEN SepalLengthCm < 6.2125 THEN (CASE WHEN PetalLengthCm < 4.871875 THEN 0.0 ELSE 1.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 0.0 ELSE 1.0 END) END) END) + (CASE WHEN PetalLengthCm < 1.921875 THEN 0.0 ELSE (CASE WHEN PetalLengthCm < 4.871875 THEN (CASE WHEN SepalLengthCm < 4.975 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN PetalLengthCm < 5.05625 THEN 1.0 ELSE 1.0 END) END) END)) / 3']

In [8]:

# Scoring in-memory
mmodel.predict([[1.2, 5.6, 7.9],
                [2.3, 1.3, 5.5]])

Out[8]:

array(['Iris-setosa', 'Iris-versicolor'], dtype='<U15')

In [9]:

# Computing the probabilities in-memory
mmodel.predict_proba([[1.2, 5.6, 7.9],
                      [2.3, 1.3, 5.5]])

Out[9]:

array([[0.66666667, 0.33333333, 0.        ],
       [0.33333333, 0.66666667, 0.        ]])