-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsklearn_tree_debug_pymfe.py
161 lines (130 loc) · 5.78 KB
/
sklearn_tree_debug_pymfe.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
"""
Graph and explain the output from the RandomForestClassifier.
Also is able to tune the hyperparameters of the RandomForestClassifier.
"""
from joblib import dump, load
from pandas import DataFrame, read_csv, Series
from sklearn.model_selection import train_test_split, learning_curve, cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.tree import export_text
import argparse
import numpy as np
import matplotlib.pyplot as plt
import shap
def main(csv_file_name, input_joblib, plot: bool, tune: bool):
combined_df: DataFrame = read_csv(csv_file_name)
class_name = 'class'
print(combined_df)
# drop all NaN lines:
combined_df = combined_df.dropna()
X = combined_df.drop(columns=[class_name])
y = combined_df[class_name]
print(X.shape, y.shape, sep="\t")
# how much of the data is used for training
training_ratio = 0.8
# how much of training data stays labeled
labeled_ratio = 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-training_ratio, random_state=42)
clf: RandomForestClassifier = load(input_joblib)
print(clf.predict_proba(X_test))
print(clf.score(X_test, y_test))
feature_importances(clf, X)
cross_val_score_(clf, X, y)
confusion_matrix_classification_report(clf, X_test, y_test)
# tree_depth(clf, X)
# plots:
# plot_learning_curve(clf, X, y)
if plot:
tree_explainer_shap(clf, X)
graph_importances(clf, X)
if tune:
tune_hyperparams(clf, X_train, y_train)
# best hyperparams cc18+100 landmarking
# {'bootstrap': True, 'max_depth': 30, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}
# best hyperparams cd3 landmarking
# {'bootstrap': True, 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 100}
# best hyperparams cc18+100 model based weighted v2
# {'bootstrap': False, 'class_weight': {'plus': 1, 'minus': 10}, 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}
# best hyperparams cc18+100 clustering
# {'bootstrap': False, 'class_weight': {'plus': 1, 'minus': 10}, 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}
# {'bootstrap': False, 'class_weight': {'plus': 2, 'minus': 1}, 'max_depth': None, 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 200}
# from https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html
def graph_importances(clf, X):
importances = clf.feature_importances_
std = np.std([tree.feature_importances_ for tree in clf.estimators_], axis=0)
feature_names = [f"feature {i}" for i in range(X.shape[1])]
forest_importances = Series(importances, index=feature_names)
fig, ax = plt.subplots()
forest_importances.plot.bar(yerr=std, ax=ax)
ax.set_title("Feature importances using MDI")
ax.set_ylabel("Mean decrease in impurity")
fig.tight_layout()
plt.show()
# started from chatgpt code
def tree_explainer_shap(clf, X):
explainer = shap.TreeExplainer(clf)
shap_values = explainer(X)
shap.plots.bar(shap_values[...,0])
shap.plots.beeswarm(shap_values[...,0])
shap.summary_plot(shap_values[..., 0], X)
# from chatgpt
def tree_depth(clf, X):
for i in range(len(clf.estimators_)):
print(export_text(clf.estimators_[i], feature_names=list(X.columns)))
# from chatgpt
def feature_importances(clf, X):
importances = clf.feature_importances_
indices = np.argsort(importances)[::-1]
for i in range(X.shape[1]):
print(f"{i + 1}. feature {indices[i]} ({importances[indices[i]]})")
# from chatgpt
def cross_val_score_(clf, X, y):
cv_scores = cross_val_score(clf, X, y, cv=5)
print("CV Scores:", cv_scores)
print("Mean CV Score: ", np.mean(cv_scores))
# from chatgpt
def confusion_matrix_classification_report(clf, X_test, y_test):
y_pred = clf.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print(
"""
[[TN FP]
[FN TP]]
"""
)
print(cm)
print(classification_report(y_test, y_pred))
# from chatgpt
def plot_learning_curve(clf, X, y):
train_sizes, train_scores, test_scores = learning_curve(clf, X, y, cv=5)
train_scores_mean = np.mean(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
plt.plot(train_sizes, train_scores_mean, label='Training score')
plt.plot(train_sizes, test_scores_mean, label='Cross-validation score')
plt.xlabel('Training examples')
plt.ylabel('Score')
plt.legend()
plt.show()
# from chatgpt
from sklearn.model_selection import GridSearchCV
def tune_hyperparams(clf, X_train, y_train):
param_grid = {
'n_estimators': [100, 200, 300],
'max_depth': [None, 10, 20, 30],
'min_samples_split': [2, 5, 10],
'min_samples_leaf': [1, 2, 4],
'bootstrap': [False],
'class_weight': [{"plus": 5, "minus":1}, {"plus": 2, "minus":1}, {"plus": 1, "minus":10}, {"plus": 2, "minus":5}, {"plus": 1, "minus":1}, {"plus": 1, "minus":50}, {"plus": 50, "minus":1}]
}
grid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=3, n_jobs=-1, verbose=2)
grid_search.fit(X_train, y_train)
print(f"Best params = {grid_search.best_params_}")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-f', '--file', type=str, help='csv file name', required=True)
parser.add_argument('-i', '--input', type=str, help='input saved sklearn model name (needs joblib extension)', required=True)
parser.add_argument('-p', '--plot', action="store_true", default=False)
parser.add_argument('-t', '--tune', action="store_true", default=False)
args = parser.parse_args()
main(args.file, args.input, args.plot, args.tune)