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stress_at_work_analysis
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Create a classification models class and use it in the ml pipeline script.

ml_pipeline
Primoz 2022-11-25 12:35:45 +01:00
parent 218b684514
commit 98f78d72fc
2 changed files with 79 additions and 56 deletions
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62
exploration/ml_pipeline_classification_with_clustering.py
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@ -26,10 +26,11 @@ import pandas as pd
import seaborn as sns
from scipy import stats
from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
from sklearn.model_selection import LeaveOneGroupOut, cross_validate
from sklearn.dummy import DummyClassifier
from sklearn.impute import SimpleImputer
from sklearn.dummy import DummyClassifier
from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
from lightgbm import LGBMClassifier
import xgboost as xg
@ -44,6 +45,7 @@ if nb_dir not in sys.path:
import machine_learning.labels
import machine_learning.model
from machine_learning.classification_models import ClassificationModels
# %% [markdown]
# # RAPIDS models
@ -92,52 +94,8 @@ model_input.set_index(index_columns, inplace=True)
# %% jupyter={"source_hidden": true}
# Create dict with classification ml models
cmodels = {
'dummy_classifier': {
'model': DummyClassifier(strategy="most_frequent"),
'metrics': [0, 0, 0, 0]
},
'logistic_regression': {
'model': linear_model.LogisticRegression(),
'metrics': [0, 0, 0, 0]
},
'support_vector_machine': {
'model': svm.SVC(),
'metrics': [0, 0, 0, 0]
},
'gaussian_naive_bayes': {
'model': naive_bayes.GaussianNB(),
'metrics': [0, 0, 0, 0]
},
'stochastic_gradient_descent_classifier': {
'model': linear_model.SGDClassifier(),
'metrics': [0, 0, 0, 0]
},
'knn': {
'model': neighbors.KNeighborsClassifier(),
'metrics': [0, 0, 0, 0]
},
'decision_tree': {
'model': tree.DecisionTreeClassifier(),
'metrics': [0, 0, 0, 0]
},
'random_forest_classifier': {
'model': ensemble.RandomForestClassifier(),
'metrics': [0, 0, 0, 0]
},
'gradient_boosting_classifier': {
'model': ensemble.GradientBoostingClassifier(),
'metrics': [0, 0, 0, 0]
},
'lgbm_classifier': {
'model': LGBMClassifier(),
'metrics': [0, 0, 0, 0]
},
'XGBoost_classifier': {
'model': xg.sklearn.XGBClassifier(),
'metrics': [0, 0, 0, 0]
}
}
cm = ClassificationModels()
cmodels = cm.get_cmodels()
# %% jupyter={"source_hidden": true}
for k in range(n_clusters):
@ -223,10 +181,4 @@ for k in range(n_clusters):
# %% jupyter={"source_hidden": true}
# Get overall results
for model_title, model in cmodels.items():
print("\n************************************\n")
print("Current model:", model_title, end="\n")
print("Acc", model['metrics'][0]/n_clusters)
print("Precision", model['metrics'][1]/n_clusters)
print("Recall", model['metrics'][2]/n_clusters)
print("F1", model['metrics'][3]/n_clusters)
cm.get_total_models_scores(n_clusters=n_clusters)

71
machine_learning/classification_models.py 100644
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@ -0,0 +1,71 @@
from sklearn.dummy import DummyClassifier
from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
from lightgbm import LGBMClassifier
import xgboost as xg
class ClassificationModels():
def __init__(self):
self.cmodels = self.init_classification_models()
def get_cmodels(self):
return self.cmodels
def init_classification_models(self):
cmodels = {
'dummy_classifier': {
'model': DummyClassifier(strategy="most_frequent"),
'metrics': [0, 0, 0, 0]
},
'logistic_regression': {
'model': linear_model.LogisticRegression(),
'metrics': [0, 0, 0, 0]
},
'support_vector_machine': {
'model': svm.SVC(),
'metrics': [0, 0, 0, 0]
},
'gaussian_naive_bayes': {
'model': naive_bayes.GaussianNB(),
'metrics': [0, 0, 0, 0]
},
'stochastic_gradient_descent_classifier': {
'model': linear_model.SGDClassifier(),
'metrics': [0, 0, 0, 0]
},
'knn': {
'model': neighbors.KNeighborsClassifier(),
'metrics': [0, 0, 0, 0]
},
'decision_tree': {
'model': tree.DecisionTreeClassifier(),
'metrics': [0, 0, 0, 0]
},
'random_forest_classifier': {
'model': ensemble.RandomForestClassifier(),
'metrics': [0, 0, 0, 0]
},
'gradient_boosting_classifier': {
'model': ensemble.GradientBoostingClassifier(),
'metrics': [0, 0, 0, 0]
},
'lgbm_classifier': {
'model': LGBMClassifier(),
'metrics': [0, 0, 0, 0]
},
'XGBoost_classifier': {
'model': xg.sklearn.XGBClassifier(),
'metrics': [0, 0, 0, 0]
}
}
return cmodels
def get_total_models_scores(self, n_clusters=1):
for model_title, model in self.cmodels.items():
print("\n************************************\n")
print("Current model:", model_title, end="\n")
print("Acc:", model['metrics'][0]/n_clusters)
print("Precision:", model['metrics'][1]/n_clusters)
print("Recall:", model['metrics'][2]/n_clusters)
print("F1:", model['metrics'][3]/n_clusters)