VerticaPy

Python API for Vertica Data Science at Scale

Example: Methods in an Unsupervised Model¶

In this example, we use the 'Iris' dataset to demonstrate the methods available to unsupervised models.

In [1]:

from verticapy.datasets import load_iris
iris = load_iris()
display(iris)

	123 SepalLengthCm Numeric(5,2)	123 SepalWidthCm Numeric(5,2)	123 PetalLengthCm Numeric(5,2)	123 PetalWidthCm Numeric(5,2)	Abc Species Varchar(30)
1	4.30	3.00	1.10	0.10	Iris-setosa
2	4.40	2.90	1.40	0.20	Iris-setosa
3	4.40	3.00	1.30	0.20	Iris-setosa
4	4.40	3.20	1.30	0.20	Iris-setosa
5	4.50	2.30	1.30	0.30	Iris-setosa
6	4.60	3.10	1.50	0.20	Iris-setosa
7	4.60	3.20	1.40	0.20	Iris-setosa
8	4.60	3.40	1.40	0.30	Iris-setosa
9	4.60	3.60	1.00	0.20	Iris-setosa
10	4.70	3.20	1.30	0.20	Iris-setosa
11	4.70	3.20	1.60	0.20	Iris-setosa
12	4.80	3.00	1.40	0.10	Iris-setosa
13	4.80	3.00	1.40	0.30	Iris-setosa
14	4.80	3.10	1.60	0.20	Iris-setosa
15	4.80	3.40	1.60	0.20	Iris-setosa
16	4.80	3.40	1.90	0.20	Iris-setosa
17	4.90	2.40	3.30	1.00	Iris-versicolor
18	4.90	2.50	4.50	1.70	Iris-virginica
19	4.90	3.00	1.40	0.20	Iris-setosa
20	4.90	3.10	1.50	0.10	Iris-setosa
21	4.90	3.10	1.50	0.10	Iris-setosa
22	4.90	3.10	1.50	0.10	Iris-setosa
23	5.00	2.00	3.50	1.00	Iris-versicolor
24	5.00	2.30	3.30	1.00	Iris-versicolor
25	5.00	3.00	1.60	0.20	Iris-setosa
26	5.00	3.20	1.20	0.20	Iris-setosa
27	5.00	3.30	1.40	0.20	Iris-setosa
28	5.00	3.40	1.50	0.20	Iris-setosa
29	5.00	3.40	1.60	0.40	Iris-setosa
30	5.00	3.50	1.30	0.30	Iris-setosa
31	5.00	3.50	1.60	0.60	Iris-setosa
32	5.00	3.60	1.40	0.20	Iris-setosa
33	5.10	2.50	3.00	1.10	Iris-versicolor
34	5.10	3.30	1.70	0.50	Iris-setosa
35	5.10	3.40	1.50	0.20	Iris-setosa
36	5.10	3.50	1.40	0.20	Iris-setosa
37	5.10	3.50	1.40	0.30	Iris-setosa
38	5.10	3.70	1.50	0.40	Iris-setosa
39	5.10	3.80	1.50	0.30	Iris-setosa
40	5.10	3.80	1.60	0.20	Iris-setosa
41	5.10	3.80	1.90	0.40	Iris-setosa
42	5.20	2.70	3.90	1.40	Iris-versicolor
43	5.20	3.40	1.40	0.20	Iris-setosa
44	5.20	3.50	1.50	0.20	Iris-setosa
45	5.20	4.10	1.50	0.10	Iris-setosa
46	5.30	3.70	1.50	0.20	Iris-setosa
47	5.40	3.00	4.50	1.50	Iris-versicolor
48	5.40	3.40	1.50	0.40	Iris-setosa
49	5.40	3.40	1.70	0.20	Iris-setosa
50	5.40	3.70	1.50	0.20	Iris-setosa
51	5.40	3.90	1.30	0.40	Iris-setosa
52	5.40	3.90	1.70	0.40	Iris-setosa
53	5.50	2.30	4.00	1.30	Iris-versicolor
54	5.50	2.40	3.70	1.00	Iris-versicolor
55	5.50	2.40	3.80	1.10	Iris-versicolor
56	5.50	2.50	4.00	1.30	Iris-versicolor
57	5.50	2.60	4.40	1.20	Iris-versicolor
58	5.50	3.50	1.30	0.20	Iris-setosa
59	5.50	4.20	1.40	0.20	Iris-setosa
60	5.60	2.50	3.90	1.10	Iris-versicolor
61	5.60	2.70	4.20	1.30	Iris-versicolor
62	5.60	2.80	4.90	2.00	Iris-virginica
63	5.60	2.90	3.60	1.30	Iris-versicolor
64	5.60	3.00	4.10	1.30	Iris-versicolor
65	5.60	3.00	4.50	1.50	Iris-versicolor
66	5.70	2.50	5.00	2.00	Iris-virginica
67	5.70	2.60	3.50	1.00	Iris-versicolor
68	5.70	2.80	4.10	1.30	Iris-versicolor
69	5.70	2.80	4.50	1.30	Iris-versicolor
70	5.70	2.90	4.20	1.30	Iris-versicolor
71	5.70	3.00	4.20	1.20	Iris-versicolor
72	5.70	3.80	1.70	0.30	Iris-setosa
73	5.70	4.40	1.50	0.40	Iris-setosa
74	5.80	2.60	4.00	1.20	Iris-versicolor
75	5.80	2.70	3.90	1.20	Iris-versicolor
76	5.80	2.70	4.10	1.00	Iris-versicolor
77	5.80	2.70	5.10	1.90	Iris-virginica
78	5.80	2.70	5.10	1.90	Iris-virginica
79	5.80	2.80	5.10	2.40	Iris-virginica
80	5.80	4.00	1.20	0.20	Iris-setosa
81	5.90	3.00	4.20	1.50	Iris-versicolor
82	5.90	3.00	5.10	1.80	Iris-virginica
83	5.90	3.20	4.80	1.80	Iris-versicolor
84	6.00	2.20	4.00	1.00	Iris-versicolor
85	6.00	2.20	5.00	1.50	Iris-virginica
86	6.00	2.70	5.10	1.60	Iris-versicolor
87	6.00	2.90	4.50	1.50	Iris-versicolor
88	6.00	3.00	4.80	1.80	Iris-virginica
89	6.00	3.40	4.50	1.60	Iris-versicolor
90	6.10	2.60	5.60	1.40	Iris-virginica
91	6.10	2.80	4.00	1.30	Iris-versicolor
92	6.10	2.80	4.70	1.20	Iris-versicolor
93	6.10	2.90	4.70	1.40	Iris-versicolor
94	6.10	3.00	4.60	1.40	Iris-versicolor
95	6.10	3.00	4.90	1.80	Iris-virginica
96	6.20	2.20	4.50	1.50	Iris-versicolor
97	6.20	2.80	4.80	1.80	Iris-virginica
98	6.20	2.90	4.30	1.30	Iris-versicolor
99	6.20	3.40	5.40	2.30	Iris-virginica
100	6.30	2.30	4.40	1.30	Iris-versicolor

Rows: 1-100 of 150 | Columns: 5

Let's create a k-means model to segment the data into clusters.

In [2]:

from verticapy.learn.cluster import KMeans
model = KMeans("public.KMeans_iris", n_cluster = 3)
model.fit("public.iris", ["PetalWidthCm", "PetalLengthCm"])

Out[2]:


=======
centers
=======
petalwidthcm|petallengthcm
------------+-------------
   2.04783  |   5.62609   
   1.35926  |   4.29259   
   0.24400  |   1.46400   


=======
metrics
=======
Evaluation metrics:
     Total Sum of Squares: 550.64347
     Within-Cluster Sum of Squares: 
         Cluster 0: 15.163478
         Cluster 1: 14.227407
         Cluster 2: 2.0384
     Total Within-Cluster Sum of Squares: 31.429286
     Between-Cluster Sum of Squares: 519.21418
     Between-Cluster SS / Total SS: 94.29%
 Number of iterations performed: 8
 Converged: True
 Call:
kmeans('public.KMeans_iris', 'public.iris', '"PetalWidthCm", "PetalLengthCm"', 3
USING PARAMETERS max_iterations=10, epsilon=0.0001, init_method='kmeanspp', distance_method='euclidean')

Fitting the model creates new model attributes.

In [3]:

model.X

Out[3]:

['"PetalWidthCm"', '"PetalLengthCm"']

In [4]:

model.input_relation

Out[4]:

'public.iris'

These attributes will be used when invoking the different model abstractions.

Models have many useful attributes. For a k-means model, the 'clustercenters' and 'metrics_' attributes can give you an idea of the quality of the model.

In [5]:

model.cluster_centers_

	123 petalwidthcm Float	123 petallengthcm Float
1	2.04782608695652	5.62608695652174
2	1.35925925925926	4.29259259259259
3	0.244	1.464

Out[5]:

In [6]:

model.metrics_

	value
	519.21418
	550.64347
	31.429286
	0.9429226137921877
	✅

Out[6]:

You can use the 'get_attr' method to view other attributes.

In [8]:

model.get_attr()

	Abc attr_name Varchar(128)	Abc attr_fields Long varchar(32000000)	123 #_of_rows Integer
1	centers	petalwidthcm, petallengthcm	3
2	metrics	metrics	1

Out[8]:

Let's look at the SQL query for our model.

In [7]:

display(model.deploySQL())

APPLY_KMEANS("PetalWidthCm", "PetalLengthCm" USING PARAMETERS model_name = 'public.KMeans_iris', match_by_pos = 'true')

You can add the prediction to your vDataFrame.

In [9]:

model.predict(iris, name = "pred_Species")

	123 SepalLengthCm Numeric(5,2)	123 SepalWidthCm Numeric(5,2)	123 PetalLengthCm Numeric(5,2)	123 PetalWidthCm Numeric(5,2)	Abc Species Varchar(30)	123 pred_Species Integer
1	4.30	3.00	1.10	0.10	Iris-setosa	2
2	4.40	2.90	1.40	0.20	Iris-setosa	2
3	4.40	3.00	1.30	0.20	Iris-setosa	2
4	4.40	3.20	1.30	0.20	Iris-setosa	2
5	4.50	2.30	1.30	0.30	Iris-setosa	2
6	4.60	3.10	1.50	0.20	Iris-setosa	2
7	4.60	3.20	1.40	0.20	Iris-setosa	2
8	4.60	3.40	1.40	0.30	Iris-setosa	2
9	4.60	3.60	1.00	0.20	Iris-setosa	2
10	4.70	3.20	1.30	0.20	Iris-setosa	2
11	4.70	3.20	1.60	0.20	Iris-setosa	2
12	4.80	3.00	1.40	0.10	Iris-setosa	2
13	4.80	3.00	1.40	0.30	Iris-setosa	2
14	4.80	3.10	1.60	0.20	Iris-setosa	2
15	4.80	3.40	1.60	0.20	Iris-setosa	2
16	4.80	3.40	1.90	0.20	Iris-setosa	2
17	4.90	2.40	3.30	1.00	Iris-versicolor	1
18	4.90	2.50	4.50	1.70	Iris-virginica	1
19	4.90	3.00	1.40	0.20	Iris-setosa	2
20	4.90	3.10	1.50	0.10	Iris-setosa	2
21	4.90	3.10	1.50	0.10	Iris-setosa	2
22	4.90	3.10	1.50	0.10	Iris-setosa	2
23	5.00	2.00	3.50	1.00	Iris-versicolor	1
24	5.00	2.30	3.30	1.00	Iris-versicolor	1
25	5.00	3.00	1.60	0.20	Iris-setosa	2
26	5.00	3.20	1.20	0.20	Iris-setosa	2
27	5.00	3.30	1.40	0.20	Iris-setosa	2
28	5.00	3.40	1.50	0.20	Iris-setosa	2
29	5.00	3.40	1.60	0.40	Iris-setosa	2
30	5.00	3.50	1.30	0.30	Iris-setosa	2
31	5.00	3.50	1.60	0.60	Iris-setosa	2
32	5.00	3.60	1.40	0.20	Iris-setosa	2
33	5.10	2.50	3.00	1.10	Iris-versicolor	1
34	5.10	3.30	1.70	0.50	Iris-setosa	2
35	5.10	3.40	1.50	0.20	Iris-setosa	2
36	5.10	3.50	1.40	0.20	Iris-setosa	2
37	5.10	3.50	1.40	0.30	Iris-setosa	2
38	5.10	3.70	1.50	0.40	Iris-setosa	2
39	5.10	3.80	1.50	0.30	Iris-setosa	2
40	5.10	3.80	1.60	0.20	Iris-setosa	2
41	5.10	3.80	1.90	0.40	Iris-setosa	2
42	5.20	2.70	3.90	1.40	Iris-versicolor	1
43	5.20	3.40	1.40	0.20	Iris-setosa	2
44	5.20	3.50	1.50	0.20	Iris-setosa	2
45	5.20	4.10	1.50	0.10	Iris-setosa	2
46	5.30	3.70	1.50	0.20	Iris-setosa	2
47	5.40	3.00	4.50	1.50	Iris-versicolor	1
48	5.40	3.40	1.50	0.40	Iris-setosa	2
49	5.40	3.40	1.70	0.20	Iris-setosa	2
50	5.40	3.70	1.50	0.20	Iris-setosa	2
51	5.40	3.90	1.30	0.40	Iris-setosa	2
52	5.40	3.90	1.70	0.40	Iris-setosa	2
53	5.50	2.30	4.00	1.30	Iris-versicolor	1
54	5.50	2.40	3.70	1.00	Iris-versicolor	1
55	5.50	2.40	3.80	1.10	Iris-versicolor	1
56	5.50	2.50	4.00	1.30	Iris-versicolor	1
57	5.50	2.60	4.40	1.20	Iris-versicolor	1
58	5.50	3.50	1.30	0.20	Iris-setosa	2
59	5.50	4.20	1.40	0.20	Iris-setosa	2
60	5.60	2.50	3.90	1.10	Iris-versicolor	1
61	5.60	2.70	4.20	1.30	Iris-versicolor	1
62	5.60	2.80	4.90	2.00	Iris-virginica	0
63	5.60	2.90	3.60	1.30	Iris-versicolor	1
64	5.60	3.00	4.10	1.30	Iris-versicolor	1
65	5.60	3.00	4.50	1.50	Iris-versicolor	1
66	5.70	2.50	5.00	2.00	Iris-virginica	0
67	5.70	2.60	3.50	1.00	Iris-versicolor	1
68	5.70	2.80	4.10	1.30	Iris-versicolor	1
69	5.70	2.80	4.50	1.30	Iris-versicolor	1
70	5.70	2.90	4.20	1.30	Iris-versicolor	1
71	5.70	3.00	4.20	1.20	Iris-versicolor	1
72	5.70	3.80	1.70	0.30	Iris-setosa	2
73	5.70	4.40	1.50	0.40	Iris-setosa	2
74	5.80	2.60	4.00	1.20	Iris-versicolor	1
75	5.80	2.70	3.90	1.20	Iris-versicolor	1
76	5.80	2.70	4.10	1.00	Iris-versicolor	1
77	5.80	2.70	5.10	1.90	Iris-virginica	0
78	5.80	2.70	5.10	1.90	Iris-virginica	0
79	5.80	2.80	5.10	2.40	Iris-virginica	0
80	5.80	4.00	1.20	0.20	Iris-setosa	2
81	5.90	3.00	4.20	1.50	Iris-versicolor	1
82	5.90	3.00	5.10	1.80	Iris-virginica	0
83	5.90	3.20	4.80	1.80	Iris-versicolor	1
84	6.00	2.20	4.00	1.00	Iris-versicolor	1
85	6.00	2.20	5.00	1.50	Iris-virginica	1
86	6.00	2.70	5.10	1.60	Iris-versicolor	0
87	6.00	2.90	4.50	1.50	Iris-versicolor	1
88	6.00	3.00	4.80	1.80	Iris-virginica	1
89	6.00	3.40	4.50	1.60	Iris-versicolor	1
90	6.10	2.60	5.60	1.40	Iris-virginica	0
91	6.10	2.80	4.00	1.30	Iris-versicolor	1
92	6.10	2.80	4.70	1.20	Iris-versicolor	1
93	6.10	2.90	4.70	1.40	Iris-versicolor	1
94	6.10	3.00	4.60	1.40	Iris-versicolor	1
95	6.10	3.00	4.90	1.80	Iris-virginica	1
96	6.20	2.20	4.50	1.50	Iris-versicolor	1
97	6.20	2.80	4.80	1.80	Iris-virginica	1
98	6.20	2.90	4.30	1.30	Iris-versicolor	1
99	6.20	3.40	5.40	2.30	Iris-virginica	0
100	6.30	2.30	4.40	1.30	Iris-versicolor	1

Out[9]:

Rows: 1-100 of 150 | Columns: 6

Let's examine the prediction with a histogram and graph.

In [10]:

iris.hist(["Species", "pred_Species"])

In [12]:

model.plot()

Some algorithms, like DBSCAN and LOF are constructed with SQL queries.

In [17]:

from verticapy.learn.cluster import DBSCAN
model = DBSCAN("public.DBSCAN_iris")
model.fit("public.iris", ["PetalWidthCm", "PetalLengthCm"])

Out[17]:

<DBSCAN>
Number of Clusters: 6
Number of Outliers: 0

In [18]:

from verticapy.learn.neighbors import LocalOutlierFactor
model2 = LocalOutlierFactor("public.LocalOutlierFactor_iris")
model2.fit("public.iris", ["PetalWidthCm", "PetalLengthCm"])

Out[18]:

<LocalOutlierFactor>

These models store the results in a table. We can make predictions with this model using the 'predict' method.

In [19]:

model.predict()

	123 PetalWidthCm Numeric(5,2)	123 PetalLengthCm Numeric(5,2)	123 dbscan_cluster Int
1	0.10	1.10	0
2	0.10	1.40	0
3	0.10	1.50	0
4	0.10	1.50	0
5	0.10	1.50	0
6	0.10	1.50	0
7	0.20	1.00	0
8	0.20	1.20	0
9	0.20	1.20	0
10	0.20	1.30	0
11	0.20	1.30	0
12	0.20	1.30	0
13	0.20	1.30	0
14	0.20	1.40	0
15	0.20	1.40	0
16	0.20	1.40	0
17	0.20	1.40	0
18	0.20	1.40	0
19	0.20	1.40	0
20	0.20	1.40	0
21	0.20	1.40	0
22	0.20	1.50	0
23	0.20	1.50	0
24	0.20	1.50	0
25	0.20	1.50	0
26	0.20	1.50	0
27	0.20	1.50	0
28	0.20	1.60	0
29	0.20	1.60	0
30	0.20	1.60	0
31	0.20	1.60	0
32	0.20	1.60	0
33	0.20	1.70	0
34	0.20	1.90	0
35	0.30	1.30	0
36	0.30	1.30	0
37	0.30	1.40	0
38	0.30	1.40	0
39	0.30	1.40	0
40	0.30	1.50	0
41	0.30	1.70	0
42	0.40	1.30	0
43	0.40	1.50	0
44	0.40	1.50	0
45	0.40	1.50	0
46	0.40	1.60	0
47	0.40	1.70	0
48	0.40	1.90	0
49	0.50	1.70	0
50	0.60	1.60	0
51	1.00	3.30	1
52	1.00	3.30	1
53	1.00	3.50	1
54	1.00	3.50	1
55	1.00	3.70	1
56	1.00	4.00	2
57	1.00	4.10	2
58	1.10	3.00	1
59	1.10	3.80	1
60	1.10	3.90	1
61	1.20	3.90	1
62	1.20	4.00	2
63	1.20	4.20	2
64	1.20	4.40	2
65	1.20	4.70	2
66	1.30	3.60	1
67	1.30	4.00	2
68	1.30	4.00	2
69	1.30	4.00	2
70	1.30	4.10	2
71	1.30	4.10	2
72	1.30	4.20	2
73	1.30	4.20	2
74	1.30	4.30	2
75	1.30	4.30	2
76	1.30	4.40	2
77	1.30	4.50	2
78	1.30	4.60	2
79	1.40	3.90	2
80	1.40	4.40	2
81	1.40	4.40	2
82	1.40	4.60	2
83	1.40	4.70	2
84	1.40	4.70	2
85	1.40	4.80	2
86	1.40	5.60	4
87	1.50	4.20	2
88	1.50	4.50	2
89	1.50	4.50	2
90	1.50	4.50	2
91	1.50	4.50	2
92	1.50	4.50	2
93	1.50	4.60	2
94	1.50	4.70	2
95	1.50	4.90	2
96	1.50	4.90	2
97	1.50	5.00	3
98	1.50	5.10	3
99	1.60	4.50	2
100	1.60	4.70	2

Out[19]:

Rows: 1-100 of 150 | Columns: 3

In [20]:

model2.predict()

	123 PetalWidthCm Numeric(5,2)	123 PetalLengthCm Numeric(5,2)	123 lof_score Float
1	0.10	1.10	2.08234031003641
2	0.10	1.40	1.05722609447314
3	0.10	1.50	1.10299515545165
4	0.10	1.50	1.10299515545165
5	0.10	1.50	1.10299515545165
6	0.10	1.50	1.10299515545165
7	0.20	1.00	2.43694090797296
8	0.20	1.20	1.53695743415418
9	0.20	1.20	1.53695743415418
10	0.20	1.30	1.14067601173295
11	0.20	1.30	1.14067601173295
12	0.20	1.30	1.14067601173295
13	0.20	1.30	1.14067601173295
14	0.20	1.40	0.982463512587469
15	0.20	1.40	0.982463512587469
16	0.20	1.40	0.982463512587469
17	0.20	1.40	0.982463512587469
18	0.20	1.40	0.982463512587469
19	0.20	1.40	0.982463512587469
20	0.20	1.40	0.982463512587469
21	0.20	1.40	0.982463512587469
22	0.20	1.50	1.00386533089101
23	0.20	1.50	1.00386533089101
24	0.20	1.50	1.00386533089101
25	0.20	1.50	1.00386533089101
26	0.20	1.50	1.00386533089101
27	0.20	1.50	1.00386533089101
28	0.20	1.60	1.16605506750783
29	0.20	1.60	1.16605506750783
30	0.20	1.60	1.16605506750783
31	0.20	1.60	1.16605506750783
32	0.20	1.60	1.16605506750783
33	0.20	1.70	1.47475360743923
34	0.20	1.90	2.20482138359511
35	0.30	1.30	1.25406400900897
36	0.30	1.30	1.25406400900897
37	0.30	1.40	1.06843214709229
38	0.30	1.40	1.06843214709229
39	0.30	1.40	1.06843214709229
40	0.30	1.50	1.10661974516951
41	0.30	1.70	1.4276374283454
42	0.40	1.30	1.38388841756981
43	0.40	1.50	1.36155303325323
44	0.40	1.50	1.36155303325323
45	0.40	1.50	1.36155303325323
46	0.40	1.60	1.40306099177471
47	0.40	1.70	1.51794573888052
48	0.40	1.90	2.09963466437765
49	0.50	1.70	1.81377540597986
50	0.60	1.60	1.93923956424429
51	1.00	3.30	1.70757746613585
52	1.00	3.30	1.70757746613585
53	1.00	3.50	1.46018853313619
54	1.00	3.50	1.46018853313619
55	1.00	3.70	1.28788552390156
56	1.00	4.00	1.16146034202009
57	1.00	4.10	1.1277650089115
58	1.10	3.00	2.12104418437916
59	1.10	3.80	1.21079006485531
60	1.10	3.90	1.14844993391747
61	1.20	3.90	1.13380669424528
62	1.20	4.00	1.09283321298032
63	1.20	4.20	1.04785386369406
64	1.20	4.40	1.03244025717094
65	1.20	4.70	1.1239354494644
66	1.30	3.60	1.30419812752566
67	1.30	4.00	1.09294378681154
68	1.30	4.00	1.09294378681154
69	1.30	4.00	1.09294378681154
70	1.30	4.10	1.06133152082905
71	1.30	4.10	1.06133152082905
72	1.30	4.20	1.0559938901676
73	1.30	4.20	1.0559938901676
74	1.30	4.30	1.00931326766892
75	1.30	4.30	1.00931326766892
76	1.30	4.40	0.999482042167287
77	1.30	4.50	1.03714724993564
78	1.30	4.60	1.04055081597547
79	1.40	3.90	1.13091276494204
80	1.40	4.40	1.02137848230684
81	1.40	4.40	1.02137848230684
82	1.40	4.60	1.04095055778715
83	1.40	4.70	1.038738988165
84	1.40	4.70	1.038738988165
85	1.40	4.80	1.06848706272371
86	1.40	5.60	1.45021529895284
87	1.50	4.20	1.02821064443014
88	1.50	4.50	1.02379130297441
89	1.50	4.50	1.02379130297441
90	1.50	4.50	1.02379130297441
91	1.50	4.50	1.02379130297441
92	1.50	4.50	1.02379130297441
93	1.50	4.60	1.03932658069538
94	1.50	4.70	1.05932519610485
95	1.50	4.90	1.05491614212111
96	1.50	4.90	1.05491614212111
97	1.50	5.00	1.07386060583869
98	1.50	5.10	1.12599071943985
99	1.60	4.50	1.03516259258256
100	1.60	4.70	1.03672676903774

Out[20]:

Rows: 1-100 of 150 | Columns: 3

Let's plot our model.

In [21]:

model.plot()
model2.plot()