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stress_at_work_analysis
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Use stratified downsampling. 

And run all models with a method from machine_learning.helper.
ml_pipeline
junos 2023-01-04 21:25:42 +01:00
parent b0b9edccc4
commit 72fdd9c5ec
1 changed files with 27 additions and 7 deletions
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34
exploration/ml_pipeline_classification.py
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@ -36,6 +36,8 @@ nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path: if nb_dir not in sys.path:
sys.path.append(nb_dir) sys.path.append(nb_dir)
import machine_learning.helper
# %% [markdown] # %% [markdown]
# # RAPIDS models # # RAPIDS models
@ -70,14 +72,20 @@ model_input['target'].value_counts()
# %% jupyter={"source_hidden": false, "outputs_hidden": false} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
# UnderSampling # UnderSampling
if undersampling: if undersampling:
model_input.groupby("pid").count() model_input_new = pd.DataFrame(columns=model_input.columns)
no_stress = model_input[model_input['target'] == 0] for pid in model_input["pid"].unique():
stress = model_input[model_input['target'] == 1] stress = model_input[(model_input["pid"] == pid) & (model_input['target'] == 1)]
no_stress = model_input[(model_input["pid"] == pid) & (model_input['target'] == 0)]
if (len(stress) == 0):
continue
if (len(no_stress) == 0):
continue
model_input_new = pd.concat([model_input_new, stress], axis=0)
no_stress = no_stress.sample(n=len(stress)) no_stress = no_stress.sample(n=min(len(stress), len(no_stress)))
model_input = pd.concat([stress,no_stress], axis=0) # In case there are more stress samples than no_stress, take all instances of no_stress.
model_input_new = pd.concat([model_input_new, no_stress], axis=0)
model_input["target"].value_counts() model_input = model_input_new
# %% jupyter={"source_hidden": false, "outputs_hidden": false} # %% jupyter={"source_hidden": false, "outputs_hidden": false}
@ -152,6 +160,18 @@ print("F1", np.mean(dummy_classifier['test_f1']))
print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dummy_classifier['test_accuracy'], n_sl)[:n_sl])[::-1]) print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dummy_classifier['test_accuracy'], n_sl)[:n_sl])[::-1])
print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dummy_classifier['test_accuracy'], n_sl)[:n_sl])) print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dummy_classifier['test_accuracy'], n_sl)[:n_sl]))
# %% [markdown] nteract={"transient": {"deleting": false}}
# ### All models
# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
final_scores = machine_learning.helper.run_all_classification_models(imputer.fit_transform(train_x), data_y, data_groups, cv_method)
# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
# %%
final_scores.index.name = "metric"
final_scores = final_scores.set_index(["method", final_scores.index])
final_scores.to_csv("../presentation/event_stressful_detection_5fold.csv")
# %% [markdown] # %% [markdown]
# ### Logistic Regression # ### Logistic Regression