verticapy.machine_learning.vertica.decomposition.SVD.plot_circle¶
- SVD.plot_circle(dimensions: tuple = (1, 2), chart: PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure | None = None, **style_kwargs) PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure¶
Draws a decomposition circle.
Parameters¶
- dimensions: tuple, optional
Tuple of two elements representing the IDs of the model’s components.
- chart: PlottingObject, optional
The chart object to plot on.
- **style_kwargs
Any optional parameter to pass to the Plotting functions.
Returns¶
- obj
Plotting Object.
Examples¶
For this example, we will use the winequality dataset.
import verticapy.datasets as vpd data = vpd.load_winequality()
123fixed_acidity123volatile_acidity123citric_acid123residual_sugar123chlorides123free_sulfur_dioxide123total_sulfur_dioxide123density123pH123sulphates123alcohol123quality123goodAbccolor1 3.9 0.225 0.4 4.2 0.03 29.0 118.0 0.989 3.57 0.36 12.8 8 1 white 2 4.7 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 10.5 5 0 white 3 4.7 0.455 0.18 1.9 0.036 33.0 106.0 0.98746 3.21 0.83 14.0 7 1 white 4 4.7 0.785 0.0 3.4 0.036 23.0 134.0 0.98981 3.53 0.92 13.8 6 0 white 5 4.9 0.345 0.34 1.0 0.068 32.0 143.0 0.99138 3.24 0.4 10.1 5 0 white 6 4.9 0.345 0.34 1.0 0.068 32.0 143.0 0.99138 3.24 0.4 10.1 5 0 white 7 4.9 0.42 0.0 2.1 0.048 16.0 42.0 0.99154 3.71 0.74 14.0 7 1 red 8 5.0 0.27 0.4 1.2 0.076 42.0 124.0 0.99204 3.32 0.47 10.1 6 0 white 9 5.0 0.31 0.0 6.4 0.046 43.0 166.0 0.994 3.3 0.63 9.9 6 0 white 10 5.0 0.4 0.5 4.3 0.046 29.0 80.0 0.9902 3.49 0.66 13.6 6 0 red 11 5.0 0.44 0.04 18.6 0.039 38.0 128.0 0.9985 3.37 0.57 10.2 6 0 white 12 5.1 0.11 0.32 1.6 0.028 12.0 90.0 0.99008 3.57 0.52 12.2 6 0 white 13 5.1 0.14 0.25 0.7 0.039 15.0 89.0 0.9919 3.22 0.43 9.2 6 0 white 14 5.1 0.165 0.22 5.7 0.047 42.0 146.0 0.9934 3.18 0.55 9.9 6 0 white 15 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 12.5 7 1 white 16 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 12.5 7 1 white 17 5.1 0.33 0.22 1.6 0.027 18.0 89.0 0.9893 3.51 0.38 12.5 7 1 white 18 5.1 0.39 0.21 1.7 0.027 15.0 72.0 0.9894 3.5 0.45 12.5 6 0 white 19 5.2 0.2 0.27 3.2 0.047 16.0 93.0 0.99235 3.44 0.53 10.1 7 1 white 20 5.2 0.21 0.31 1.7 0.048 17.0 61.0 0.98953 3.24 0.37 12.0 7 1 white 21 5.2 0.22 0.46 6.2 0.066 41.0 187.0 0.99362 3.19 0.42 9.73333333333333 5 0 white 22 5.2 0.31 0.2 2.4 0.027 27.0 117.0 0.98886 3.56 0.45 13.0 7 1 white 23 5.2 0.32 0.25 1.8 0.103 13.0 50.0 0.9957 3.38 0.55 9.2 5 0 red 24 5.2 0.34 0.37 6.2 0.031 42.0 133.0 0.99076 3.25 0.41 12.5 6 0 white 25 5.2 0.36 0.02 1.6 0.031 24.0 104.0 0.9896 3.44 0.35 12.2 6 0 white 26 5.2 0.365 0.08 13.5 0.041 37.0 142.0 0.997 3.46 0.39 9.9 6 0 white 27 5.2 0.48 0.04 1.6 0.054 19.0 106.0 0.9927 3.54 0.62 12.2 7 1 red 28 5.2 0.5 0.18 2.0 0.036 23.0 129.0 0.98949 3.36 0.77 13.4 7 1 white 29 5.3 0.16 0.39 1.0 0.028 40.0 101.0 0.99156 3.57 0.59 10.6 6 0 white 30 5.3 0.16 0.39 1.0 0.028 40.0 101.0 0.99156 3.57 0.59 10.6 6 0 white 31 5.3 0.165 0.24 1.1 0.051 25.0 105.0 0.9925 3.32 0.47 9.1 5 0 white 32 5.3 0.23 0.56 0.9 0.041 46.0 141.0 0.99119 3.16 0.62 9.7 5 0 white 33 5.3 0.3 0.3 1.2 0.029 25.0 93.0 0.98742 3.31 0.4 13.6 7 1 white 34 5.3 0.33 0.3 1.2 0.048 25.0 119.0 0.99045 3.32 0.62 11.3 6 0 white 35 5.3 0.36 0.27 6.3 0.028 40.0 132.0 0.99186 3.37 0.4 11.6 6 0 white 36 5.3 0.36 0.27 6.3 0.028 40.0 132.0 0.99186 3.37 0.4 11.6 6 0 white 37 5.3 0.4 0.25 3.9 0.031 45.0 130.0 0.99072 3.31 0.58 11.75 7 1 white 38 5.3 0.47 0.11 2.2 0.048 16.0 89.0 0.99182 3.54 0.88 13.6 7 1 red 39 5.3 0.47 0.11 2.2 0.048 16.0 89.0 0.99182 3.54 0.88 13.5666666666667 7 1 red 40 5.3 0.715 0.19 1.5 0.161 7.0 62.0 0.99395 3.62 0.61 11.0 5 0 red 41 5.4 0.22 0.29 1.2 0.045 69.0 152.0 0.99178 3.76 0.63 11.0 7 1 white 42 5.4 0.595 0.1 2.8 0.042 26.0 80.0 0.9932 3.36 0.38 9.3 5 0 white 43 5.4 0.74 0.09 1.7 0.089 16.0 26.0 0.99402 3.67 0.56 11.6 6 0 red 44 5.5 0.12 0.33 1.0 0.038 23.0 131.0 0.99164 3.25 0.45 9.8 5 0 white 45 5.5 0.12 0.33 1.0 0.038 23.0 131.0 0.99164 3.25 0.45 9.8 5 0 white 46 5.5 0.14 0.27 4.6 0.029 22.0 104.0 0.9949 3.34 0.44 9.0 5 0 white 47 5.5 0.14 0.27 4.6 0.029 22.0 104.0 0.9949 3.34 0.44 9.0 5 0 white 48 5.5 0.16 0.31 1.2 0.026 31.0 68.0 0.9898 3.33 0.44 11.65 6 0 white 49 5.5 0.16 0.31 1.2 0.026 31.0 68.0 0.9898 3.33 0.44 11.6333333333333 6 0 white 50 5.5 0.18 0.22 5.5 0.037 10.0 86.0 0.99156 3.46 0.44 12.2 5 0 white 51 5.5 0.24 0.45 1.7 0.046 22.0 113.0 0.99224 3.22 0.48 10.0 5 0 white 52 5.5 0.29 0.3 1.1 0.022 20.0 110.0 0.98869 3.34 0.38 12.8 7 1 white 53 5.5 0.31 0.29 3.0 0.027 16.0 102.0 0.99067 3.23 0.56 11.2 6 0 white 54 5.5 0.32 0.45 4.9 0.028 25.0 191.0 0.9922 3.51 0.49 11.5 7 1 white 55 5.5 0.35 0.35 1.1 0.045 14.0 167.0 0.992 3.34 0.68 9.9 6 0 white 56 5.5 0.375 0.38 1.7 0.036 17.0 98.0 0.99142 3.29 0.39 10.5 6 0 white 57 5.6 0.15 0.26 5.55 0.051 51.0 139.0 0.99336 3.47 0.5 11.0 6 0 white 58 5.6 0.15 0.31 5.3 0.038 8.0 79.0 0.9923 3.3 0.39 10.5 6 0 white 59 5.6 0.16 0.27 1.4 0.044 53.0 168.0 0.9918 3.28 0.37 10.1 6 0 white 60 5.6 0.175 0.29 0.8 0.043 20.0 67.0 0.99112 3.28 0.48 9.9 6 0 white 61 5.6 0.185 0.19 7.1 0.048 36.0 110.0 0.99438 3.26 0.41 9.5 6 0 white 62 5.6 0.185 0.19 7.1 0.048 36.0 110.0 0.99438 3.26 0.41 9.5 6 0 white 63 5.6 0.22 0.32 1.2 0.024 29.0 97.0 0.98823 3.2 0.46 13.05 7 1 white 64 5.6 0.26 0.18 1.4 0.034 18.0 135.0 0.99174 3.32 0.35 10.2 6 0 white 65 5.6 0.26 0.26 5.7 0.031 12.0 80.0 0.9923 3.25 0.38 10.8 5 0 white 66 5.6 0.26 0.5 11.4 0.029 25.0 93.0 0.99428 3.23 0.49 10.5 6 0 white 67 5.6 0.28 0.28 4.2 0.044 52.0 158.0 0.992 3.35 0.44 10.7 7 1 white 68 5.6 0.3 0.1 6.4 0.043 34.0 142.0 0.99382 3.14 0.48 9.8 5 0 white 69 5.6 0.35 0.14 5.0 0.046 48.0 198.0 0.9937 3.3 0.71 10.3 5 0 white 70 5.6 0.49 0.13 4.5 0.039 17.0 116.0 0.9907 3.42 0.9 13.7 7 1 white 71 5.6 0.49 0.13 4.5 0.039 17.0 116.0 0.9907 3.42 0.9 13.7 7 1 white 72 5.6 0.66 0.0 2.2 0.087 3.0 11.0 0.99378 3.71 0.63 12.8 7 1 red 73 5.6 0.66 0.0 2.2 0.087 3.0 11.0 0.99378 3.71 0.63 12.8 7 1 red 74 5.7 0.15 0.47 11.4 0.035 49.0 128.0 0.99456 3.03 0.34 10.5 8 1 white 75 5.7 0.18 0.26 2.2 0.023 21.0 95.0 0.9893 3.07 0.54 12.3 6 0 white 76 5.7 0.18 0.36 1.2 0.046 9.0 71.0 0.99199 3.7 0.68 10.9 7 1 white 77 5.7 0.2 0.3 2.5 0.046 38.0 125.0 0.99276 3.34 0.5 9.9 6 0 white 78 5.7 0.21 0.32 0.9 0.038 38.0 121.0 0.99074 3.24 0.46 10.6 6 0 white 79 5.7 0.21 0.37 4.5 0.04 58.0 140.0 0.99332 3.29 0.62 10.6 6 0 white 80 5.7 0.22 0.2 16.0 0.044 41.0 113.0 0.99862 3.22 0.46 8.9 6 0 white 81 5.7 0.22 0.2 16.0 0.044 41.0 113.0 0.99862 3.22 0.46 8.9 6 0 white 82 5.7 0.22 0.2 16.0 0.044 41.0 113.0 0.99862 3.22 0.46 8.9 6 0 white 83 5.7 0.22 0.2 16.0 0.044 41.0 113.0 0.99862 3.22 0.46 8.9 6 0 white 84 5.7 0.22 0.2 16.0 0.044 41.0 113.0 0.99862 3.22 0.46 8.9 6 0 white 85 5.7 0.22 0.29 3.5 0.04 27.0 146.0 0.98999 3.17 0.36 12.1 6 0 white 86 5.7 0.23 0.28 9.65 0.025 26.0 121.0 0.9925 3.28 0.38 11.3 6 0 white 87 5.7 0.25 0.26 12.5 0.049 52.5 106.0 0.99691 3.08 0.45 9.4 6 0 white 88 5.7 0.25 0.26 12.5 0.049 52.5 120.0 0.99691 3.08 0.45 9.4 6 0 white 89 5.7 0.25 0.27 11.5 0.04 24.0 120.0 0.99411 3.33 0.31 10.8 6 0 white 90 5.7 0.26 0.24 17.8 0.059 23.0 124.0 0.99773 3.3 0.5 10.1 5 0 white 91 5.7 0.26 0.24 17.8 0.059 23.0 124.0 0.99773 3.3 0.5 10.1 5 0 white 92 5.7 0.26 0.24 17.8 0.059 23.0 124.0 0.99773 3.3 0.5 10.1 5 0 white 93 5.7 0.27 0.32 1.2 0.046 20.0 155.0 0.9934 3.8 0.41 10.2 6 0 white 94 5.7 0.28 0.24 17.5 0.044 60.0 167.0 0.9989 3.31 0.44 9.4 5 0 white 95 5.7 0.32 0.18 1.4 0.029 26.0 104.0 0.9906 3.44 0.37 11.0 6 0 white 96 5.7 0.32 0.38 4.75 0.033 23.0 94.0 0.991 3.42 0.42 11.8 7 1 white 97 5.7 0.36 0.34 4.2 0.026 21.0 77.0 0.9907 3.41 0.45 11.9 6 0 white 98 5.8 0.14 0.15 6.1 0.042 27.0 123.0 0.99362 3.06 0.6 9.9 6 0 white 99 5.8 0.15 0.32 1.2 0.037 14.0 119.0 0.99137 3.19 0.5 10.2 6 0 white 100 5.8 0.17 0.34 1.8 0.045 96.0 170.0 0.99035 3.38 0.9 11.8 8 1 white Rows: 1-100 | Columns: 14We can drop the “color” column as it is varchar type.
data.drop("color")
Let’s import the model:
from verticapy.machine_learning.vertica import PCA
Then we can create the model:
model = PCA( n_components = 3, )
And train it:
model.fit(data) ======= columns ======= index| name | mean | sd -----+--------------------+---------+-------- 1 | fixed_acidity | 7.21531 | 1.29643 2 | volatile_acidity | 0.33967 | 0.16464 3 | citric_acid | 0.31863 | 0.14532 4 | residual_sugar | 5.44324 | 4.75780 5 | chlorides | 0.05603 | 0.03503 6 |free_sulfur_dioxide |30.52532 |17.74940 7 |total_sulfur_dioxide|115.74457|56.52185 8 | density | 0.99470 | 0.00300 9 | ph | 3.21850 | 0.16079 10 | sulphates | 0.53127 | 0.14881 11 | alcohol |10.49180 | 1.19271 12 | quality | 5.81838 | 0.87326 13 | good | 0.19655 | 0.39742 =============== singular_values =============== index| value |explained_variance|accumulated_explained_variance -----+--------+------------------+------------------------------ 1 |58.06985| 0.95351 | 0.95351 2 |11.98567| 0.04062 | 0.99413 3 | 4.13105| 0.00483 | 0.99896 ==================== principal_components ==================== index| PC1 | PC2 | PC3 -----+--------+--------+-------- 1 |-0.00741|-0.00537| 0.02386 2 |-0.00118|-0.00079| 0.00091 3 | 0.00049|-0.00025| 0.00192 4 | 0.04102| 0.01863| 0.99519 5 |-0.00017| 0.00007| 0.00018 6 | 0.23048| 0.97262|-0.02711 7 | 0.97217|-0.23139|-0.03586 8 | 0.00000| 0.00000| 0.00046 9 |-0.00066| 0.00065|-0.00691 10 |-0.00070| 0.00035|-0.00194 11 |-0.00545| 0.00288|-0.08266 12 |-0.00053| 0.00916|-0.00889 13 |-0.00033| 0.00257|-0.00593 ======== counters ======== counter_name |counter_value ------------------+------------- accepted_row_count| 6497 rejected_row_count| 0 iteration_count | 1 =========== call_string =========== SELECT PCA('"public"."_verticapy_tmp_pca_v_mldb_b01f7e50979711efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_b04dea92979711efa8720242ac120002_"', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "free_sulfur_dioxide", "total_sulfur_dioxide", "density", "pH", "sulphates", "alcohol", "quality", "good"' USING PARAMETERS scale=false, num_components=3);
You can plot the Decomposition Circles:
model.plot_circle()