268 KiB
268 KiB
In [1]:
import pandas as pd
data = pd.read_csv("data-turbine/gt_full.csv", index_col=0)
data
Out[1]:
| AT | AP | AH | AFDP | GTEP | TIT | TAT | TEY | CDP | CO | NOX | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 4.5878 | 1018.7 | 83.675 | 3.5758 | 23.979 | 1086.2 | 549.83 | 134.67 | 11.898 | 0.32663 | 81.952 |
| 2 | 4.2932 | 1018.3 | 84.235 | 3.5709 | 23.951 | 1086.1 | 550.05 | 134.67 | 11.892 | 0.44784 | 82.377 |
| 3 | 3.9045 | 1018.4 | 84.858 | 3.5828 | 23.990 | 1086.5 | 550.19 | 135.10 | 12.042 | 0.45144 | 83.776 |
| 4 | 3.7436 | 1018.3 | 85.434 | 3.5808 | 23.911 | 1086.5 | 550.17 | 135.03 | 11.990 | 0.23107 | 82.505 |
| 5 | 3.7516 | 1017.8 | 85.182 | 3.5781 | 23.917 | 1085.9 | 550.00 | 134.67 | 11.910 | 0.26747 | 82.028 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 36729 | 3.6268 | 1028.5 | 93.200 | 3.1661 | 19.087 | 1037.0 | 541.59 | 109.08 | 10.411 | 10.99300 | 89.172 |
| 36730 | 4.1674 | 1028.6 | 94.036 | 3.1923 | 19.016 | 1037.6 | 542.28 | 108.79 | 10.344 | 11.14400 | 88.849 |
| 36731 | 5.4820 | 1028.5 | 95.219 | 3.3128 | 18.857 | 1038.0 | 543.48 | 107.81 | 10.462 | 11.41400 | 96.147 |
| 36732 | 5.8837 | 1028.7 | 94.200 | 3.9831 | 23.563 | 1076.9 | 550.11 | 131.41 | 11.771 | 3.31340 | 64.738 |
| 36733 | 6.0392 | 1028.8 | 94.547 | 3.8752 | 22.524 | 1067.9 | 548.23 | 125.41 | 11.462 | 11.98100 | 109.240 |
36733 rows × 11 columns
In [2]:
data.describe().transpose()
Out[2]:
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| AT | 36733.0 | 17.712726 | 7.447451 | -6.234800 | 11.7810 | 17.8010 | 23.6650 | 37.1030 |
| AP | 36733.0 | 1013.070165 | 6.463346 | 985.850000 | 1008.8000 | 1012.6000 | 1017.0000 | 1036.6000 |
| AH | 36733.0 | 77.867015 | 14.461355 | 24.085000 | 68.1880 | 80.4700 | 89.3760 | 100.2000 |
| AFDP | 36733.0 | 3.925518 | 0.773936 | 2.087400 | 3.3556 | 3.9377 | 4.3769 | 7.6106 |
| GTEP | 36733.0 | 25.563801 | 4.195957 | 17.698000 | 23.1290 | 25.1040 | 29.0610 | 40.7160 |
| TIT | 36733.0 | 1081.428084 | 17.536373 | 1000.800000 | 1071.8000 | 1085.9000 | 1097.0000 | 1100.9000 |
| TAT | 36733.0 | 546.158517 | 6.842360 | 511.040000 | 544.7200 | 549.8800 | 550.0400 | 550.6100 |
| TEY | 36733.0 | 133.506404 | 15.618634 | 100.020000 | 124.4500 | 133.7300 | 144.0800 | 179.5000 |
| CDP | 36733.0 | 12.060525 | 1.088795 | 9.851800 | 11.4350 | 11.9650 | 12.8550 | 15.1590 |
| CO | 36733.0 | 2.372468 | 2.262672 | 0.000388 | 1.1824 | 1.7135 | 2.8429 | 44.1030 |
| NOX | 36733.0 | 65.293067 | 11.678357 | 25.905000 | 57.1620 | 63.8490 | 71.5480 | 119.9100 |
In [3]:
import seaborn as sns
sns.heatmap(data.corr(), annot=True)
Out[3]:
<Axes: >
In [4]:
data.drop(["GTEP", "TEY", "CDP", "NOX"], axis=1, inplace=True)
data
Out[4]:
| AT | AP | AH | AFDP | TIT | TAT | CO | |
|---|---|---|---|---|---|---|---|
| 1 | 4.5878 | 1018.7 | 83.675 | 3.5758 | 1086.2 | 549.83 | 0.32663 |
| 2 | 4.2932 | 1018.3 | 84.235 | 3.5709 | 1086.1 | 550.05 | 0.44784 |
| 3 | 3.9045 | 1018.4 | 84.858 | 3.5828 | 1086.5 | 550.19 | 0.45144 |
| 4 | 3.7436 | 1018.3 | 85.434 | 3.5808 | 1086.5 | 550.17 | 0.23107 |
| 5 | 3.7516 | 1017.8 | 85.182 | 3.5781 | 1085.9 | 550.00 | 0.26747 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 36729 | 3.6268 | 1028.5 | 93.200 | 3.1661 | 1037.0 | 541.59 | 10.99300 |
| 36730 | 4.1674 | 1028.6 | 94.036 | 3.1923 | 1037.6 | 542.28 | 11.14400 |
| 36731 | 5.4820 | 1028.5 | 95.219 | 3.3128 | 1038.0 | 543.48 | 11.41400 |
| 36732 | 5.8837 | 1028.7 | 94.200 | 3.9831 | 1076.9 | 550.11 | 3.31340 |
| 36733 | 6.0392 | 1028.8 | 94.547 | 3.8752 | 1067.9 | 548.23 | 11.98100 |
36733 rows × 7 columns
In [5]:
sns.heatmap(data.corr(), annot=True)
Out[5]:
<Axes: >
In [6]:
from sklearn.model_selection import train_test_split
random_state = 9
y = data["CO"]
X = data.drop(["CO"], axis=1).copy()
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=random_state
)
display(X_train, y_train, X_test, y_test)
| AT | AP | AH | AFDP | TIT | TAT | |
|---|---|---|---|---|---|---|
| 4481 | 26.6350 | 1009.7 | 83.256 | 4.4137 | 1100.0 | 540.65 |
| 24884 | 20.2280 | 1016.2 | 73.583 | 4.6238 | 1099.8 | 538.53 |
| 21558 | 15.6330 | 1018.5 | 81.089 | 4.0899 | 1100.0 | 534.04 |
| 1706 | 16.6540 | 1020.2 | 64.757 | 4.5755 | 1086.6 | 549.76 |
| 21389 | 21.0020 | 1004.3 | 75.645 | 4.1101 | 1100.0 | 534.21 |
| ... | ... | ... | ... | ... | ... | ... |
| 25726 | 17.5190 | 1015.9 | 85.663 | 3.6809 | 1072.2 | 549.82 |
| 5015 | 21.9780 | 1014.4 | 75.280 | 3.1246 | 1058.0 | 549.86 |
| 22585 | 4.7103 | 1003.0 | 92.874 | 3.2741 | 1067.2 | 550.15 |
| 502 | 6.7758 | 1008.3 | 93.029 | 5.1192 | 1099.9 | 524.78 |
| 20829 | 17.6730 | 1020.7 | 88.840 | 3.0370 | 1079.9 | 550.02 |
29386 rows × 6 columns
4481 0.3527
24884 1.2522
21558 1.4718
1706 1.3117
21389 1.7835
...
25726 2.4980
5015 3.2652
22585 1.2630
502 0.7851
20829 2.7272
Name: CO, Length: 29386, dtype: float64
| AT | AP | AH | AFDP | TIT | TAT | |
|---|---|---|---|---|---|---|
| 18247 | 23.4530 | 1006.2 | 84.837 | 3.7535 | 1088.7 | 550.39 |
| 20344 | 28.7090 | 1011.2 | 59.574 | 6.0321 | 1100.0 | 542.01 |
| 2925 | 21.8330 | 1017.0 | 81.262 | 3.9663 | 1092.9 | 544.91 |
| 118 | 7.8167 | 1022.2 | 88.135 | 4.6605 | 1100.0 | 526.21 |
| 5714 | 19.9120 | 1013.1 | 86.846 | 3.6710 | 1080.2 | 550.25 |
| ... | ... | ... | ... | ... | ... | ... |
| 21918 | 9.5791 | 1017.5 | 75.935 | 2.9617 | 1081.1 | 549.66 |
| 13100 | 22.6150 | 1012.1 | 78.314 | 4.2739 | 1089.8 | 550.37 |
| 26705 | 28.4020 | 1004.4 | 79.478 | 4.0643 | 1073.0 | 550.19 |
| 4183 | 31.7400 | 1012.2 | 41.623 | 4.5323 | 1100.2 | 539.10 |
| 2983 | 23.7130 | 1013.5 | 69.233 | 3.7112 | 1091.6 | 549.98 |
7347 rows × 6 columns
18247 1.34970
20344 1.63430
2925 0.78632
118 0.72742
5714 1.35980
...
21918 1.45140
13100 1.00960
26705 2.01190
4183 0.37685
2983 1.15990
Name: CO, Length: 7347, dtype: float64
In [7]:
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model, tree, neighbors, ensemble
models = {
"linear": {"model": linear_model.LinearRegression(n_jobs=-1)},
"linear_poly": {
"model": make_pipeline(
PolynomialFeatures(degree=2),
linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),
)
},
"linear_interact": {
"model": make_pipeline(
PolynomialFeatures(interaction_only=True),
linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),
)
},
"ridge": {"model": linear_model.RidgeCV()},
"decision_tree": {
"model": tree.DecisionTreeRegressor(max_depth=4, random_state=random_state)
},
"knn": {"model": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},
"random_forest": {
"model": ensemble.RandomForestRegressor(
max_depth=7, random_state=random_state, n_jobs=-1
)
},
}
In [8]:
import math
from sklearn import metrics
for model_name in models.keys():
print(f"Model: {model_name}")
fitted_model = models[model_name]["model"].fit(
X_train.values, y_train.values.ravel()
)
y_train_pred = fitted_model.predict(X_train.values)
y_test_pred = fitted_model.predict(X_test.values)
models[model_name]["fitted"] = fitted_model
models[model_name]["train_preds"] = y_train_pred
models[model_name]["preds"] = y_test_pred
models[model_name]["RMSE_train"] = math.sqrt(
metrics.mean_squared_error(y_train, y_train_pred)
)
models[model_name]["RMSE_test"] = math.sqrt(
metrics.mean_squared_error(y_test, y_test_pred)
)
models[model_name]["RMAE_test"] = math.sqrt(
metrics.mean_absolute_error(y_test, y_test_pred)
)
models[model_name]["R2_test"] = metrics.r2_score(y_test, y_test_pred)
Model: linear Model: linear_poly Model: linear_interact Model: ridge Model: decision_tree Model: knn Model: random_forest
In [9]:
reg_metrics = pd.DataFrame.from_dict(models, "index")[
["RMSE_train", "RMSE_test", "RMAE_test", "R2_test"]
]
reg_metrics.sort_values(by="RMSE_test").style.background_gradient(
cmap="viridis", low=1, high=0.3, subset=["RMSE_train", "RMSE_test"]
).background_gradient(cmap="plasma", low=0.3, high=1, subset=["RMAE_test", "R2_test"])
Out[9]:
| RMSE_train | RMSE_test | RMAE_test | R2_test | |
|---|---|---|---|---|
| random_forest | 1.023025 | 1.129226 | 0.782178 | 0.748271 |
| knn | 1.098849 | 1.203358 | 0.787677 | 0.714135 |
| linear_poly | 1.305816 | 1.261614 | 0.829701 | 0.685788 |
| linear_interact | 1.314473 | 1.272674 | 0.838792 | 0.680254 |
| decision_tree | 1.286532 | 1.275301 | 0.845881 | 0.678932 |
| linear | 1.512818 | 1.484769 | 0.935421 | 0.564800 |
| ridge | 1.512818 | 1.484770 | 0.935421 | 0.564800 |
In [10]:
import numpy as np
from sklearn import model_selection
parameters = {
"criterion": ["squared_error", "absolute_error", "friedman_mse", "poisson"],
"max_depth": np.arange(1, 21).tolist()[0::2],
"min_samples_split": np.arange(2, 11).tolist()[0::2],
}
grid = model_selection.GridSearchCV(
tree.DecisionTreeRegressor(random_state=random_state), parameters, cv=5, n_jobs=-1, scoring="r2"
)
grid.fit(X_train, y_train)
grid.best_params_
/Users/user/Projects/python/fuzzy-rules-generator/.venv/lib/python3.12/site-packages/numpy/ma/core.py:2881: RuntimeWarning: invalid value encountered in cast _data = np.array(data, dtype=dtype, copy=copy,
Out[10]:
{'criterion': 'squared_error', 'max_depth': 9, 'min_samples_split': 10}
In [12]:
model = grid.best_estimator_
y_pred = model.predict(X_test)
old_metrics = {
"RMSE_test": models["decision_tree"]["RMSE_test"],
"RMAE_test": models["decision_tree"]["RMAE_test"],
"R2_test": models["decision_tree"]["R2_test"],
}
new_metrics = {}
new_metrics["RMSE_test"] = math.sqrt(metrics.mean_squared_error(y_test, y_pred))
new_metrics["RMAE_test"] = math.sqrt(
metrics.mean_absolute_error(y_test, y_pred)
)
new_metrics["R2_test"] = metrics.r2_score(y_test, y_pred)
display(old_metrics)
display(new_metrics)
{'RMSE_test': 1.2753013703532543,
'RMAE_test': 0.8458813419529052,
'R2_test': 0.6789324577873092}
{'RMSE_test': 1.2544701838600494,
'RMAE_test': 0.7821057715152305,
'R2_test': 0.6893356365847731}
In [13]:
rules = tree.export_text(model, feature_names=X_train.columns.values.tolist())
print(rules)
|--- TIT <= 1058.15 | |--- TAT <= 543.87 | | |--- TAT <= 528.12 | | | |--- AP <= 1023.00 | | | | |--- AT <= 4.38 | | | | | |--- value: [11.50] | | | | |--- AT > 4.38 | | | | | |--- AH <= 86.80 | | | | | | |--- AP <= 1017.20 | | | | | | | |--- value: [34.96] | | | | | | |--- AP > 1017.20 | | | | | | | |--- value: [27.31] | | | | | |--- AH > 86.80 | | | | | | |--- value: [40.94] | | | |--- AP > 1023.00 | | | | |--- value: [11.34] | | |--- TAT > 528.12 | | | |--- TIT <= 1028.85 | | | | |--- AT <= 15.97 | | | | | |--- TIT <= 1018.85 | | | | | | |--- value: [20.73] | | | | | |--- TIT > 1018.85 | | | | | | |--- AP <= 1020.75 | | | | | | | |--- AT <= 14.71 | | | | | | | | |--- AH <= 95.05 | | | | | | | | | |--- value: [11.90] | | | | | | | | |--- AH > 95.05 | | | | | | | | | |--- value: [8.33] | | | | | | | |--- AT > 14.71 | | | | | | | | |--- value: [16.37] | | | | | | |--- AP > 1020.75 | | | | | | | |--- AFDP <= 3.49 | | | | | | | | |--- AT <= 7.53 | | | | | | | | | |--- value: [16.59] | | | | | | | | |--- AT > 7.53 | | | | | | | | | |--- value: [12.82] | | | | | | | |--- AFDP > 3.49 | | | | | | | | |--- value: [27.33] | | | | |--- AT > 15.97 | | | | | |--- value: [21.87] | | | |--- TIT > 1028.85 | | | | |--- AT <= 24.49 | | | | | |--- AP <= 997.04 | | | | | | |--- value: [27.12] | | | | | |--- AP > 997.04 | | | | | | |--- TAT <= 537.71 | | | | | | | |--- TIT <= 1046.95 | | | | | | | | |--- AFDP <= 3.01 | | | | | | | | | |--- value: [12.56] | | | | | | | | |--- AFDP > 3.01 | | | | | | | | | |--- value: [9.29] | | | | | | | |--- TIT > 1046.95 | | | | | | | | |--- value: [19.61] | | | | | | |--- TAT > 537.71 | | | | | | | |--- AFDP <= 3.17 | | | | | | | | |--- TAT <= 542.42 | | | | | | | | | |--- value: [9.93] | | | | | | | | |--- TAT > 542.42 | | | | | | | | | |--- value: [8.71] | | | | | | | |--- AFDP > 3.17 | | | | | | | | |--- TIT <= 1041.75 | | | | | | | | | |--- value: [8.76] | | | | | | | | |--- TIT > 1041.75 | | | | | | | | | |--- value: [6.32] | | | | |--- AT > 24.49 | | | | | |--- value: [35.40] | |--- TAT > 543.87 | | |--- TAT <= 549.23 | | | |--- TIT <= 1049.65 | | | | |--- TAT <= 548.03 | | | | | |--- TIT <= 1034.35 | | | | | | |--- AFDP <= 2.78 | | | | | | | |--- AH <= 89.28 | | | | | | | | |--- value: [9.67] | | | | | | | |--- AH > 89.28 | | | | | | | | |--- value: [6.48] | | | | | | |--- AFDP > 2.78 | | | | | | | |--- value: [11.30] | | | | | |--- TIT > 1034.35 | | | | | | |--- AFDP <= 2.69 | | | | | | | |--- AFDP <= 2.44 | | | | | | | | |--- AP <= 1022.90 | | | | | | | | | |--- value: [6.91] | | | | | | | | |--- AP > 1022.90 | | | | | | | | | |--- value: [5.45] | | | | | | | |--- AFDP > 2.44 | | | | | | | | |--- TAT <= 546.68 | | | | | | | | | |--- value: [8.42] | | | | | | | | |--- TAT > 546.68 | | | | | | | | | |--- value: [7.34] | | | | | | |--- AFDP > 2.69 | | | | | | | |--- AT <= 11.97 | | | | | | | | |--- AP <= 1016.30 | | | | | | | | | |--- value: [4.98] | | | | | | | | |--- AP > 1016.30 | | | | | | | | | |--- value: [6.32] | | | | | | | |--- AT > 11.97 | | | | | | | | |--- AH <= 78.34 | | | | | | | | | |--- value: [5.37] | | | | | | | | |--- AH > 78.34 | | | | | | | | | |--- value: [7.73] | | | | |--- TAT > 548.03 | | | | | |--- AP <= 1024.55 | | | | | | |--- AH <= 99.53 | | | | | | | |--- AH <= 94.23 | | | | | | | | |--- AFDP <= 2.69 | | | | | | | | | |--- value: [6.04] | | | | | | | | |--- AFDP > 2.69 | | | | | | | | | |--- value: [5.51] | | | | | | | |--- AH > 94.23 | | | | | | | | |--- value: [7.39] | | | | | | |--- AH > 99.53 | | | | | | | |--- value: [3.34] | | | | | |--- AP > 1024.55 | | | | | | |--- TIT <= 1046.95 | | | | | | | |--- AH <= 74.83 | | | | | | | | |--- value: [6.56] | | | | | | | |--- AH > 74.83 | | | | | | | | |--- value: [8.46] | | | | | | |--- TIT > 1046.95 | | | | | | | |--- value: [4.51] | | | |--- TIT > 1049.65 | | | | |--- AFDP <= 2.83 | | | | | |--- AP <= 1016.50 | | | | | | |--- TIT <= 1051.65 | | | | | | | |--- TAT <= 548.73 | | | | | | | | |--- AFDP <= 2.61 | | | | | | | | | |--- value: [7.54] | | | | | | | | |--- AFDP > 2.61 | | | | | | | | | |--- value: [6.82] | | | | | | | |--- TAT > 548.73 | | | | | | | | |--- AP <= 998.10 | | | | | | | | | |--- value: [5.18] | | | | | | | | |--- AP > 998.10 | | | | | | | | | |--- value: [6.59] | | | | | | |--- TIT > 1051.65 | | | | | | | |--- AP <= 1007.45 | | | | | | | | |--- value: [5.40] | | | | | | | |--- AP > 1007.45 | | | | | | | | |--- TAT <= 544.26 | | | | | | | | | |--- value: [3.99] | | | | | | | | |--- TAT > 544.26 | | | | | | | | | |--- value: [6.62] | | | | | |--- AP > 1016.50 | | | | | | |--- TAT <= 547.32 | | | | | | | |--- AT <= 8.60 | | | | | | | | |--- value: [6.10] | | | | | | | |--- AT > 8.60 | | | | | | | | |--- AH <= 85.23 | | | | | | | | | |--- value: [7.70] | | | | | | | | |--- AH > 85.23 | | | | | | | | | |--- value: [6.72] | | | | | | |--- TAT > 547.32 | | | | | | | |--- AFDP <= 2.55 | | | | | | | | |--- AP <= 1019.30 | | | | | | | | | |--- value: [5.14] | | | | | | | | |--- AP > 1019.30 | | | | | | | | | |--- value: [3.92] | | | | | | | |--- AFDP > 2.55 | | | | | | | | |--- AP <= 1021.45 | | | | | | | | | |--- value: [6.09] | | | | | | | | |--- AP > 1021.45 | | | | | | | | | |--- value: [3.61] | | | | |--- AFDP > 2.83 | | | | | |--- TAT <= 546.71 | | | | | | |--- AT <= 15.83 | | | | | | | |--- TAT <= 544.64 | | | | | | | | |--- value: [7.07] | | | | | | | |--- TAT > 544.64 | | | | | | | | |--- TAT <= 546.43 | | | | | | | | | |--- value: [4.87] | | | | | | | | |--- TAT > 546.43 | | | | | | | | | |--- value: [6.30] | | | | | | |--- AT > 15.83 | | | | | | | |--- value: [9.77] | | | | | |--- TAT > 546.71 | | | | | | |--- AH <= 82.62 | | | | | | | |--- AP <= 1004.20 | | | | | | | | |--- value: [6.96] | | | | | | | |--- AP > 1004.20 | | | | | | | | |--- AFDP <= 3.01 | | | | | | | | | |--- value: [4.96] | | | | | | | | |--- AFDP > 3.01 | | | | | | | | | |--- value: [3.68] | | | | | | |--- AH > 82.62 | | | | | | | |--- AT <= 12.90 | | | | | | | | |--- AH <= 97.26 | | | | | | | | | |--- value: [4.47] | | | | | | | | |--- AH > 97.26 | | | | | | | | | |--- value: [2.90] | | | | | | | |--- AT > 12.90 | | | | | | | | |--- TIT <= 1053.65 | | | | | | | | | |--- value: [6.45] | | | | | | | | |--- TIT > 1053.65 | | | | | | | | | |--- value: [5.00] | | |--- TAT > 549.23 | | | |--- AFDP <= 2.71 | | | | |--- AFDP <= 2.41 | | | | | |--- TIT <= 1050.75 | | | | | | |--- AP <= 1015.45 | | | | | | | |--- AT <= 10.60 | | | | | | | | |--- value: [5.55] | | | | | | | |--- AT > 10.60 | | | | | | | | |--- TAT <= 550.46 | | | | | | | | | |--- value: [3.90] | | | | | | | | |--- TAT > 550.46 | | | | | | | | | |--- value: [1.99] | | | | | | |--- AP > 1015.45 | | | | | | | |--- AT <= 13.80 | | | | | | | | |--- value: [3.58] | | | | | | | |--- AT > 13.80 | | | | | | | | |--- AT <= 20.03 | | | | | | | | | |--- value: [2.53] | | | | | | | | |--- AT > 20.03 | | | | | | | | | |--- value: [3.39] | | | | | |--- TIT > 1050.75 | | | | | | |--- TIT <= 1053.65 | | | | | | | |--- AFDP <= 2.18 | | | | | | | | |--- value: [5.89] | | | | | | | |--- AFDP > 2.18 | | | | | | | | |--- AP <= 1005.15 | | | | | | | | | |--- value: [4.58] | | | | | | | | |--- AP > 1005.15 | | | | | | | | | |--- value: [5.15] | | | | | | |--- TIT > 1053.65 | | | | | | | |--- AT <= 15.24 | | | | | | | | |--- value: [4.75] | | | | | | | |--- AT > 15.24 | | | | | | | | |--- value: [3.96] | | | | |--- AFDP > 2.41 | | | | | |--- AT <= 14.12 | | | | | | |--- TIT <= 1052.25 | | | | | | | |--- AP <= 1021.85 | | | | | | | | |--- TIT <= 1044.35 | | | | | | | | | |--- value: [5.14] | | | | | | | | |--- TIT > 1044.35 | | | | | | | | | |--- value: [5.86] | | | | | | | |--- AP > 1021.85 | | | | | | | | |--- AT <= 1.44 | | | | | | | | | |--- value: [8.53] | | | | | | | | |--- AT > 1.44 | | | | | | | | | |--- value: [6.58] | | | | | | |--- TIT > 1052.25 | | | | | | | |--- TAT <= 550.09 | | | | | | | | |--- AP <= 1018.45 | | | | | | | | | |--- value: [5.59] | | | | | | | | |--- AP > 1018.45 | | | | | | | | | |--- value: [4.67] | | | | | | | |--- TAT > 550.09 | | | | | | | | |--- AP <= 1025.65 | | | | | | | | | |--- value: [3.92] | | | | | | | | |--- AP > 1025.65 | | | | | | | | | |--- value: [5.60] | | | | | |--- AT > 14.12 | | | | | | |--- TIT <= 1042.80 | | | | | | | |--- AP <= 1012.00 | | | | | | | | |--- value: [5.84] | | | | | | | |--- AP > 1012.00 | | | | | | | | |--- value: [6.98] | | | | | | |--- TIT > 1042.80 | | | | | | | |--- AT <= 15.87 | | | | | | | | |--- AT <= 14.25 | | | | | | | | | |--- value: [3.54] | | | | | | | | |--- AT > 14.25 | | | | | | | | | |--- value: [5.16] | | | | | | | |--- AT > 15.87 | | | | | | | | |--- AH <= 55.76 | | | | | | | | | |--- value: [5.75] | | | | | | | | |--- AH > 55.76 | | | | | | | | | |--- value: [4.72] | | | |--- AFDP > 2.71 | | | | |--- TIT <= 1056.05 | | | | | |--- AT <= 12.67 | | | | | | |--- AH <= 91.10 | | | | | | | |--- AFDP <= 2.86 | | | | | | | | |--- AT <= 11.95 | | | | | | | | | |--- value: [5.53] | | | | | | | | |--- AT > 11.95 | | | | | | | | | |--- value: [3.65] | | | | | | | |--- AFDP > 2.86 | | | | | | | | |--- TIT <= 1045.05 | | | | | | | | | |--- value: [5.65] | | | | | | | | |--- TIT > 1045.05 | | | | | | | | | |--- value: [3.76] | | | | | | |--- AH > 91.10 | | | | | | | |--- AFDP <= 3.01 | | | | | | | | |--- AH <= 99.55 | | | | | | | | | |--- value: [1.88] | | | | | | | | |--- AH > 99.55 | | | | | | | | | |--- value: [2.91] | | | | | | | |--- AFDP > 3.01 | | | | | | | | |--- TIT <= 1047.40 | | | | | | | | | |--- value: [5.59] | | | | | | | | |--- TIT > 1047.40 | | | | | | | | | |--- value: [3.57] | | | | | |--- AT > 12.67 | | | | | | |--- AT <= 23.88 | | | | | | | |--- AFDP <= 3.91 | | | | | | | | |--- AH <= 88.32 | | | | | | | | | |--- value: [4.53] | | | | | | | | |--- AH > 88.32 | | | | | | | | | |--- value: [4.99] | | | | | | | |--- AFDP > 3.91 | | | | | | | | |--- AFDP <= 5.03 | | | | | | | | | |--- value: [3.93] | | | | | | | | |--- AFDP > 5.03 | | | | | | | | | |--- value: [6.07] | | | | | | |--- AT > 23.88 | | | | | | | |--- TIT <= 1050.35 | | | | | | | | |--- TIT <= 1041.45 | | | | | | | | | |--- value: [5.48] | | | | | | | | |--- TIT > 1041.45 | | | | | | | | | |--- value: [4.38] | | | | | | | |--- TIT > 1050.35 | | | | | | | | |--- AP <= 1011.25 | | | | | | | | | |--- value: [4.00] | | | | | | | | |--- AP > 1011.25 | | | | | | | | | |--- value: [3.61] | | | | |--- TIT > 1056.05 | | | | | |--- AFDP <= 3.14 | | | | | | |--- AFDP <= 2.87 | | | | | | | |--- AP <= 999.86 | | | | | | | | |--- value: [5.27] | | | | | | | |--- AP > 999.86 | | | | | | | | |--- TIT <= 1056.50 | | | | | | | | | |--- value: [3.91] | | | | | | | | |--- TIT > 1056.50 | | | | | | | | | |--- value: [4.47] | | | | | | |--- AFDP > 2.87 | | | | | | | |--- AH <= 85.90 | | | | | | | | |--- TIT <= 1056.85 | | | | | | | | | |--- value: [3.86] | | | | | | | | |--- TIT > 1056.85 | | | | | | | | | |--- value: [3.04] | | | | | | | |--- AH > 85.90 | | | | | | | | |--- AFDP <= 3.02 | | | | | | | | | |--- value: [3.18] | | | | | | | | |--- AFDP > 3.02 | | | | | | | | | |--- value: [2.49] | | | | | |--- AFDP > 3.14 | | | | | | |--- AH <= 80.23 | | | | | | | |--- AH <= 59.72 | | | | | | | | |--- AT <= 25.08 | | | | | | | | | |--- value: [2.43] | | | | | | | | |--- AT > 25.08 | | | | | | | | | |--- value: [3.28] | | | | | | | |--- AH > 59.72 | | | | | | | | |--- AFDP <= 3.65 | | | | | | | | | |--- value: [4.16] | | | | | | | | |--- AFDP > 3.65 | | | | | | | | | |--- value: [3.48] | | | | | | |--- AH > 80.23 | | | | | | | |--- AT <= 17.64 | | | | | | | | |--- AP <= 1008.55 | | | | | | | | | |--- value: [3.34] | | | | | | | | |--- AP > 1008.55 | | | | | | | | | |--- value: [4.39] | | | | | | | |--- AT > 17.64 | | | | | | | | |--- AFDP <= 3.14 | | | | | | | | | |--- value: [8.63] | | | | | | | | |--- AFDP > 3.14 | | | | | | | | | |--- value: [4.79] |--- TIT > 1058.15 | |--- TIT <= 1076.55 | | |--- TAT <= 545.34 | | | |--- TIT <= 1076.45 | | | | |--- TAT <= 539.96 | | | | | |--- AP <= 1007.90 | | | | | | |--- value: [33.91] | | | | | |--- AP > 1007.90 | | | | | | |--- TAT <= 539.63 | | | | | | | |--- AT <= 18.28 | | | | | | | | |--- TIT <= 1059.85 | | | | | | | | | |--- value: [13.67] | | | | | | | | |--- TIT > 1059.85 | | | | | | | | | |--- value: [4.79] | | | | | | | |--- AT > 18.28 | | | | | | | | |--- value: [21.75] | | | | | | |--- TAT > 539.63 | | | | | | | |--- value: [29.34] | | | | |--- TAT > 539.96 | | | | | |--- TIT <= 1069.35 | | | | | | |--- AH <= 98.54 | | | | | | | |--- AT <= 4.45 | | | | | | | | |--- value: [7.90] | | | | | | | |--- AT > 4.45 | | | | | | | | |--- TAT <= 542.62 | | | | | | | | | |--- value: [3.97] | | | | | | | | |--- TAT > 542.62 | | | | | | | | | |--- value: [6.26] | | | | | | |--- AH > 98.54 | | | | | | | |--- value: [10.20] | | | | | |--- TIT > 1069.35 | | | | | | |--- AP <= 1013.40 | | | | | | | |--- TIT <= 1069.70 | | | | | | | | |--- value: [2.39] | | | | | | | |--- TIT > 1069.70 | | | | | | | | |--- AH <= 69.87 | | | | | | | | | |--- value: [3.01] | | | | | | | | |--- AH > 69.87 | | | | | | | | | |--- value: [4.22] | | | | | | |--- AP > 1013.40 | | | | | | | |--- value: [2.26] | | | |--- TIT > 1076.45 | | | | |--- value: [30.38] | | |--- TAT > 545.34 | | | |--- TAT <= 549.52 | | | | |--- TIT <= 1068.55 | | | | | |--- AFDP <= 3.20 | | | | | | |--- TAT <= 549.07 | | | | | | | |--- TAT <= 547.68 | | | | | | | | |--- TIT <= 1059.50 | | | | | | | | | |--- value: [6.87] | | | | | | | | |--- TIT > 1059.50 | | | | | | | | | |--- value: [5.32] | | | | | | | |--- TAT > 547.68 | | | | | | | | |--- AH <= 93.33 | | | | | | | | | |--- value: [4.58] | | | | | | | | |--- AH > 93.33 | | | | | | | | | |--- value: [5.40] | | | | | | |--- TAT > 549.07 | | | | | | | |--- AT <= 16.12 | | | | | | | | |--- AH <= 93.78 | | | | | | | | | |--- value: [4.37] | | | | | | | | |--- AH > 93.78 | | | | | | | | | |--- value: [2.69] | | | | | | | |--- AT > 16.12 | | | | | | | | |--- value: [2.60] | | | | | |--- AFDP > 3.20 | | | | | | |--- AP <= 1017.70 | | | | | | | |--- TIT <= 1058.25 | | | | | | | | |--- value: [6.15] | | | | | | | |--- TIT > 1058.25 | | | | | | | | |--- TIT <= 1065.65 | | | | | | | | | |--- value: [3.42] | | | | | | | | |--- TIT > 1065.65 | | | | | | | | | |--- value: [2.82] | | | | | | |--- AP > 1017.70 | | | | | | | |--- TAT <= 548.77 | | | | | | | | |--- AH <= 99.46 | | | | | | | | | |--- value: [5.38] | | | | | | | | |--- AH > 99.46 | | | | | | | | | |--- value: [0.01] | | | | | | | |--- TAT > 548.77 | | | | | | | | |--- AH <= 96.92 | | | | | | | | | |--- value: [3.39] | | | | | | | | |--- AH > 96.92 | | | | | | | | | |--- value: [1.87] | | | | |--- TIT > 1068.55 | | | | | |--- AP <= 993.61 | | | | | | |--- value: [6.68] | | | | | |--- AP > 993.61 | | | | | | |--- AT <= 21.17 | | | | | | | |--- AH <= 69.22 | | | | | | | | |--- AP <= 1023.25 | | | | | | | | | |--- value: [3.35] | | | | | | | | |--- AP > 1023.25 | | | | | | | | | |--- value: [5.32] | | | | | | | |--- AH > 69.22 | | | | | | | | |--- TAT <= 548.49 | | | | | | | | | |--- value: [3.41] | | | | | | | | |--- TAT > 548.49 | | | | | | | | | |--- value: [2.91] | | | | | | |--- AT > 21.17 | | | | | | | |--- AP <= 1002.75 | | | | | | | | |--- value: [1.12] | | | | | | | |--- AP > 1002.75 | | | | | | | | |--- TAT <= 548.43 | | | | | | | | | |--- value: [2.77] | | | | | | | | |--- TAT > 548.43 | | | | | | | | | |--- value: [2.06] | | | |--- TAT > 549.52 | | | | |--- TIT <= 1060.85 | | | | | |--- AFDP <= 3.28 | | | | | | |--- AFDP <= 2.97 | | | | | | | |--- AFDP <= 2.58 | | | | | | | | |--- value: [2.78] | | | | | | | |--- AFDP > 2.58 | | | | | | | | |--- AT <= 18.45 | | | | | | | | | |--- value: [4.28] | | | | | | | | |--- AT > 18.45 | | | | | | | | | |--- value: [3.07] | | | | | | |--- AFDP > 2.97 | | | | | | | |--- AT <= 21.72 | | | | | | | | |--- AT <= 21.70 | | | | | | | | | |--- value: [2.63] | | | | | | | | |--- AT > 21.70 | | | | | | | | | |--- value: [8.44] | | | | | | | |--- AT > 21.72 | | | | | | | | |--- AH <= 75.30 | | | | | | | | | |--- value: [2.61] | | | | | | | | |--- AH > 75.30 | | | | | | | | | |--- value: [2.04] | | | | | |--- AFDP > 3.28 | | | | | | |--- AH <= 75.08 | | | | | | | |--- TIT <= 1058.45 | | | | | | | | |--- AT <= 26.12 | | | | | | | | | |--- value: [2.50] | | | | | | | | |--- AT > 26.12 | | | | | | | | | |--- value: [3.50] | | | | | | | |--- TIT > 1058.45 | | | | | | | | |--- AP <= 1009.95 | | | | | | | | | |--- value: [4.19] | | | | | | | | |--- AP > 1009.95 | | | | | | | | | |--- value: [3.60] | | | | | | |--- AH > 75.08 | | | | | | | |--- AT <= 19.36 | | | | | | | | |--- TAT <= 550.21 | | | | | | | | | |--- value: [3.64] | | | | | | | | |--- TAT > 550.21 | | | | | | | | | |--- value: [2.80] | | | | | | | |--- AT > 19.36 | | | | | | | | |--- AH <= 97.65 | | | | | | | | | |--- value: [4.47] | | | | | | | | |--- AH > 97.65 | | | | | | | | | |--- value: [2.98] | | | | |--- TIT > 1060.85 | | | | | |--- AFDP <= 3.20 | | | | | | |--- AFDP <= 2.74 | | | | | | | |--- AT <= 8.46 | | | | | | | | |--- AH <= 85.16 | | | | | | | | | |--- value: [2.61] | | | | | | | | |--- AH > 85.16 | | | | | | | | | |--- value: [3.57] | | | | | | | |--- AT > 8.46 | | | | | | | | |--- TIT <= 1061.00 | | | | | | | | | |--- value: [4.77] | | | | | | | | |--- TIT > 1061.00 | | | | | | | | | |--- value: [2.22] | | | | | | |--- AFDP > 2.74 | | | | | | | |--- AT <= 14.98 | | | | | | | | |--- AH <= 87.67 | | | | | | | | | |--- value: [3.57] | | | | | | | | |--- AH > 87.67 | | | | | | | | | |--- value: [2.67] | | | | | | | |--- AT > 14.98 | | | | | | | | |--- AT <= 22.93 | | | | | | | | | |--- value: [2.75] | | | | | | | | |--- AT > 22.93 | | | | | | | | | |--- value: [1.86] | | | | | |--- AFDP > 3.20 | | | | | | |--- TIT <= 1069.15 | | | | | | | |--- AFDP <= 3.35 | | | | | | | | |--- AP <= 1027.00 | | | | | | | | | |--- value: [2.14] | | | | | | | | |--- AP > 1027.00 | | | | | | | | | |--- value: [4.33] | | | | | | | |--- AFDP > 3.35 | | | | | | | | |--- TAT <= 550.46 | | | | | | | | | |--- value: [3.16] | | | | | | | | |--- TAT > 550.46 | | | | | | | | | |--- value: [6.89] | | | | | | |--- TIT > 1069.15 | | | | | | | |--- AFDP <= 3.36 | | | | | | | | |--- AT <= 13.18 | | | | | | | | | |--- value: [3.15] | | | | | | | | |--- AT > 13.18 | | | | | | | | | |--- value: [2.48] | | | | | | | |--- AFDP > 3.36 | | | | | | | | |--- AT <= 14.27 | | | | | | | | | |--- value: [1.84] | | | | | | | | |--- AT > 14.27 | | | | | | | | | |--- value: [2.32] | |--- TIT > 1076.55 | | |--- TIT <= 1091.35 | | | |--- TAT <= 532.02 | | | | |--- value: [12.04] | | | |--- TAT > 532.02 | | | | |--- AFDP <= 3.49 | | | | | |--- TIT <= 1085.05 | | | | | | |--- AH <= 81.79 | | | | | | | |--- AH <= 81.72 | | | | | | | | |--- AT <= 16.06 | | | | | | | | | |--- value: [2.69] | | | | | | | | |--- AT > 16.06 | | | | | | | | | |--- value: [2.17] | | | | | | | |--- AH > 81.72 | | | | | | | | |--- value: [34.47] | | | | | | |--- AH > 81.79 | | | | | | | |--- TAT <= 548.82 | | | | | | | | |--- TIT <= 1080.20 | | | | | | | | | |--- value: [3.49] | | | | | | | | |--- TIT > 1080.20 | | | | | | | | | |--- value: [2.17] | | | | | | | |--- TAT > 548.82 | | | | | | | | |--- AFDP <= 3.33 | | | | | | | | | |--- value: [1.95] | | | | | | | | |--- AFDP > 3.33 | | | | | | | | | |--- value: [1.54] | | | | | |--- TIT > 1085.05 | | | | | | |--- AH <= 60.56 | | | | | | | |--- TIT <= 1086.75 | | | | | | | | |--- AH <= 60.29 | | | | | | | | | |--- value: [2.75] | | | | | | | | |--- AH > 60.29 | | | | | | | | | |--- value: [5.23] | | | | | | | |--- TIT > 1086.75 | | | | | | | | |--- AP <= 1025.55 | | | | | | | | | |--- value: [1.53] | | | | | | | | |--- AP > 1025.55 | | | | | | | | | |--- value: [2.80] | | | | | | |--- AH > 60.56 | | | | | | | |--- TIT <= 1086.55 | | | | | | | | |--- AFDP <= 3.41 | | | | | | | | | |--- value: [1.68] | | | | | | | | |--- AFDP > 3.41 | | | | | | | | | |--- value: [2.34] | | | | | | | |--- TIT > 1086.55 | | | | | | | | |--- AP <= 1014.55 | | | | | | | | | |--- value: [1.62] | | | | | | | | |--- AP > 1014.55 | | | | | | | | | |--- value: [1.37] | | | | |--- AFDP > 3.49 | | | | | |--- AT <= 16.36 | | | | | | |--- TAT <= 538.79 | | | | | | | |--- TAT <= 538.74 | | | | | | | | |--- AH <= 68.06 | | | | | | | | | |--- value: [5.82] | | | | | | | | |--- AH > 68.06 | | | | | | | | | |--- value: [2.29] | | | | | | | |--- TAT > 538.74 | | | | | | | | |--- value: [19.14] | | | | | | |--- TAT > 538.79 | | | | | | | |--- AP <= 1026.95 | | | | | | | | |--- TAT <= 549.53 | | | | | | | | | |--- value: [1.67] | | | | | | | | |--- TAT > 549.53 | | | | | | | | | |--- value: [1.37] | | | | | | | |--- AP > 1026.95 | | | | | | | | |--- TIT <= 1078.95 | | | | | | | | | |--- value: [3.04] | | | | | | | | |--- TIT > 1078.95 | | | | | | | | | |--- value: [1.83] | | | | | |--- AT > 16.36 | | | | | | |--- TIT <= 1085.55 | | | | | | | |--- AH <= 100.05 | | | | | | | | |--- AP <= 1013.45 | | | | | | | | | |--- value: [1.82] | | | | | | | | |--- AP > 1013.45 | | | | | | | | | |--- value: [2.04] | | | | | | | |--- AH > 100.05 | | | | | | | | |--- value: [4.52] | | | | | | |--- TIT > 1085.55 | | | | | | | |--- AFDP <= 3.92 | | | | | | | | |--- AFDP <= 3.63 | | | | | | | | | |--- value: [1.62] | | | | | | | | |--- AFDP > 3.63 | | | | | | | | | |--- value: [1.18] | | | | | | | |--- AFDP > 3.92 | | | | | | | | |--- AFDP <= 4.06 | | | | | | | | | |--- value: [2.43] | | | | | | | | |--- AFDP > 4.06 | | | | | | | | | |--- value: [1.69] | | |--- TIT > 1091.35 | | | |--- TAT <= 530.62 | | | | |--- AT <= 3.61 | | | | | |--- AFDP <= 5.31 | | | | | | |--- AP <= 1020.15 | | | | | | | |--- AT <= 3.02 | | | | | | | | |--- TAT <= 528.77 | | | | | | | | | |--- value: [0.89] | | | | | | | | |--- TAT > 528.77 | | | | | | | | | |--- value: [1.91] | | | | | | | |--- AT > 3.02 | | | | | | | | |--- value: [2.81] | | | | | | |--- AP > 1020.15 | | | | | | | |--- AH <= 78.41 | | | | | | | | |--- AH <= 58.82 | | | | | | | | | |--- value: [2.47] | | | | | | | | |--- AH > 58.82 | | | | | | | | | |--- value: [1.70] | | | | | | | |--- AH > 78.41 | | | | | | | | |--- TAT <= 530.51 | | | | | | | | | |--- value: [2.44] | | | | | | | | |--- TAT > 530.51 | | | | | | | | | |--- value: [0.53] | | | | | |--- AFDP > 5.31 | | | | | | |--- AP <= 1014.10 | | | | | | | |--- value: [0.25] | | | | | | |--- AP > 1014.10 | | | | | | | |--- AT <= 3.11 | | | | | | | | |--- TAT <= 524.86 | | | | | | | | | |--- value: [0.86] | | | | | | | | |--- TAT > 524.86 | | | | | | | | | |--- value: [0.36] | | | | | | | |--- AT > 3.11 | | | | | | | | |--- value: [1.57] | | | | |--- AT > 3.61 | | | | | |--- AFDP <= 5.65 | | | | | | |--- AFDP <= 4.00 | | | | | | | |--- AT <= 4.30 | | | | | | | | |--- value: [2.12] | | | | | | | |--- AT > 4.30 | | | | | | | | |--- AFDP <= 3.91 | | | | | | | | | |--- value: [0.60] | | | | | | | | |--- AFDP > 3.91 | | | | | | | | | |--- value: [0.45] | | | | | | |--- AFDP > 4.00 | | | | | | | |--- AFDP <= 4.67 | | | | | | | | |--- AP <= 1018.05 | | | | | | | | | |--- value: [1.09] | | | | | | | | |--- AP > 1018.05 | | | | | | | | | |--- value: [1.36] | | | | | | | |--- AFDP > 4.67 | | | | | | | | |--- AFDP <= 5.35 | | | | | | | | | |--- value: [0.79] | | | | | | | | |--- AFDP > 5.35 | | | | | | | | | |--- value: [1.11] | | | | | |--- AFDP > 5.65 | | | | | | |--- TAT <= 517.90 | | | | | | | |--- value: [1.51] | | | | | | |--- TAT > 517.90 | | | | | | | |--- AT <= 8.05 | | | | | | | | |--- TAT <= 527.54 | | | | | | | | | |--- value: [0.35] | | | | | | | | |--- TAT > 527.54 | | | | | | | | | |--- value: [0.53] | | | | | | | |--- AT > 8.05 | | | | | | | | |--- AP <= 1027.85 | | | | | | | | | |--- value: [0.58] | | | | | | | | |--- AP > 1027.85 | | | | | | | | | |--- value: [0.82] | | | |--- TAT > 530.62 | | | | |--- AP <= 1023.35 | | | | | |--- AFDP <= 4.39 | | | | | | |--- AT <= 11.92 | | | | | | | |--- AH <= 87.51 | | | | | | | | |--- AFDP <= 3.92 | | | | | | | | | |--- value: [1.68] | | | | | | | | |--- AFDP > 3.92 | | | | | | | | | |--- value: [2.37] | | | | | | | |--- AH > 87.51 | | | | | | | | |--- AT <= 3.61 | | | | | | | | | |--- value: [2.34] | | | | | | | | |--- AT > 3.61 | | | | | | | | | |--- value: [1.14] | | | | | | |--- AT > 11.92 | | | | | | | |--- AFDP <= 4.09 | | | | | | | | |--- AT <= 24.52 | | | | | | | | | |--- value: [1.34] | | | | | | | | |--- AT > 24.52 | | | | | | | | | |--- value: [0.88] | | | | | | | |--- AFDP > 4.09 | | | | | | | | |--- TAT <= 537.27 | | | | | | | | | |--- value: [1.25] | | | | | | | | |--- TAT > 537.27 | | | | | | | | | |--- value: [1.50] | | | | | |--- AFDP > 4.39 | | | | | | |--- TAT <= 542.35 | | | | | | | |--- AP <= 1014.35 | | | | | | | | |--- AFDP <= 4.64 | | | | | | | | | |--- value: [0.80] | | | | | | | | |--- AFDP > 4.64 | | | | | | | | | |--- value: [1.12] | | | | | | | |--- AP > 1014.35 | | | | | | | | |--- AH <= 72.22 | | | | | | | | | |--- value: [1.39] | | | | | | | | |--- AH > 72.22 | | | | | | | | | |--- value: [1.08] | | | | | | |--- TAT > 542.35 | | | | | | | |--- TAT <= 542.35 | | | | | | | | |--- value: [11.85] | | | | | | | |--- TAT > 542.35 | | | | | | | | |--- AH <= 39.82 | | | | | | | | | |--- value: [2.36] | | | | | | | | |--- AH > 39.82 | | | | | | | | | |--- value: [1.39] | | | | |--- AP > 1023.35 | | | | | |--- AFDP <= 4.65 | | | | | | |--- AT <= 13.85 | | | | | | | |--- AH <= 56.86 | | | | | | | | |--- TIT <= 1098.10 | | | | | | | | | |--- value: [9.57] | | | | | | | | |--- TIT > 1098.10 | | | | | | | | | |--- value: [2.84] | | | | | | | |--- AH > 56.86 | | | | | | | | |--- AFDP <= 3.87 | | | | | | | | | |--- value: [1.45] | | | | | | | | |--- AFDP > 3.87 | | | | | | | | | |--- value: [2.23] | | | | | | |--- AT > 13.85 | | | | | | | |--- TAT <= 548.59 | | | | | | | | |--- AFDP <= 4.10 | | | | | | | | | |--- value: [0.83] | | | | | | | | |--- AFDP > 4.10 | | | | | | | | | |--- value: [1.13] | | | | | | | |--- TAT > 548.59 | | | | | | | | |--- value: [1.92] | | | | | |--- AFDP > 4.65 | | | | | | |--- AT <= 4.20 | | | | | | | |--- value: [1.76] | | | | | | |--- AT > 4.20 | | | | | | | |--- AFDP <= 5.11 | | | | | | | | |--- AP <= 1027.85 | | | | | | | | | |--- value: [0.99] | | | | | | | | |--- AP > 1027.85 | | | | | | | | | |--- value: [1.58] | | | | | | | |--- AFDP > 5.11 | | | | | | | | |--- AT <= 13.34 | | | | | | | | | |--- value: [0.53] | | | | | | | | |--- AT > 13.34 | | | | | | | | | |--- value: [1.17]
In [14]:
import pickle
pickle.dump(model, open("data-turbine/tree-gs.model.sav", "wb"))
In [15]:
rules2 = tree.export_text(
models["decision_tree"]["fitted"], feature_names=X_train.columns.values.tolist()
)
print(rules2)
|--- TIT <= 1058.15 | |--- TAT <= 543.87 | | |--- TAT <= 528.12 | | | |--- AP <= 1023.00 | | | | |--- value: [27.13] | | | |--- AP > 1023.00 | | | | |--- value: [11.34] | | |--- TAT > 528.12 | | | |--- TIT <= 1028.85 | | | | |--- value: [13.71] | | | |--- TIT > 1028.85 | | | | |--- value: [9.47] | |--- TAT > 543.87 | | |--- TAT <= 549.23 | | | |--- TIT <= 1049.65 | | | | |--- value: [7.00] | | | |--- TIT > 1049.65 | | | | |--- value: [5.61] | | |--- TAT > 549.23 | | | |--- AFDP <= 2.71 | | | | |--- value: [5.19] | | | |--- AFDP > 2.71 | | | | |--- value: [4.34] |--- TIT > 1058.15 | |--- TIT <= 1076.55 | | |--- TAT <= 545.34 | | | |--- TIT <= 1076.45 | | | | |--- value: [6.29] | | | |--- TIT > 1076.45 | | | | |--- value: [30.38] | | |--- TAT > 545.34 | | | |--- TAT <= 549.52 | | | | |--- value: [3.87] | | | |--- TAT > 549.52 | | | | |--- value: [2.88] | |--- TIT > 1076.55 | | |--- TIT <= 1091.35 | | | |--- TAT <= 532.02 | | | | |--- value: [12.04] | | | |--- TAT > 532.02 | | | | |--- value: [1.70] | | |--- TIT > 1091.35 | | | |--- TAT <= 530.62 | | | | |--- value: [0.96] | | | |--- TAT > 530.62 | | | | |--- value: [1.35]
In [16]:
import pickle
pickle.dump(model, open("data-turbine/tree.model.sav", "wb"))
In [17]:
data.to_csv("data-turbine/clear-data.csv", index=False)