junos
/
stress_at_work_analysis
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Include more metrics in regression helper methods.

ml_pipeline
junos 2022-12-07 21:25:05 +01:00
parent 95ab66fd81
commit 8131626c4a
1 changed files with 120 additions and 66 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

186
machine_learning/helper.py
View File

@ -1,6 +1,6 @@
from pathlib import Path from pathlib import Path
from sklearn import linear_model, svm, kernel_ridge, gaussian_process, ensemble from sklearn import linear_model, svm, kernel_ridge, gaussian_process, ensemble
from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, cross_validate from sklearn.model_selection import LeaveOneGroupOut, cross_validate, cross_validate
from sklearn.metrics import mean_squared_error, r2_score from sklearn.metrics import mean_squared_error, r2_score
from sklearn.impute import SimpleImputer from sklearn.impute import SimpleImputer
from sklearn.dummy import DummyRegressor from sklearn.dummy import DummyRegressor
@ -66,7 +66,7 @@ def construct_full_path(folder: Path, filename_prefix: str, data_type: str) -> P
def insert_row(df, row): def insert_row(df, row):
return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True) return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True)
def prepare_model_input(input_csv): def prepare_regression_model_input(input_csv):
model_input = pd.read_csv(input_csv) model_input = pd.read_csv(input_csv)
@ -76,10 +76,9 @@ def prepare_model_input(input_csv):
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"] data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
categorical_feature_colnames = ["gender", "startlanguage", "limesurvey_demand_control_ratio_quartile"] categorical_feature_colnames = ["gender", "startlanguage", "limesurvey_demand_control_ratio_quartile"]
additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
categorical_feature_colnames += additional_categorical_features
#TODO: check whether limesurvey_demand_control_ratio_quartile NaNs could be replaced meaningfully #TODO: check whether limesurvey_demand_control_ratio_quartile NaNs could be replaced meaningfully
#additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
#TODO: check if mostcommonactivity is indeed a categorical features after aggregating
#categorical_feature_colnames += additional_categorical_features
categorical_features = data_x[categorical_feature_colnames].copy() categorical_features = data_x[categorical_feature_colnames].copy()
mode_categorical_features = categorical_features.mode().iloc[0] mode_categorical_features = categorical_features.mode().iloc[0]
# fillna with mode # fillna with mode
@ -96,172 +95,227 @@ def prepare_model_input(input_csv):
return train_x, data_y, data_groups return train_x, data_y, data_groups
def run_all_models(input_csv): def run_all_regression_models(input_csv):
# Prepare data # Prepare data
train_x, data_y, data_groups = prepare_model_input(input_csv) data_x, data_y, data_groups = prepare_regression_model_input(input_csv)
# Prepare cross validation # Prepare cross validation
logo = LeaveOneGroupOut() logo = LeaveOneGroupOut()
logo.get_n_splits( logo.get_n_splits(
train_x, data_x,
data_y, data_y,
groups=data_groups, groups=data_groups,
) )
scores = pd.DataFrame(columns=["method", "median", "max"]) metrics = ['r2', 'neg_mean_absolute_error', 'neg_root_mean_squared_error']
test_metrics = ["test_" + metric for metric in metrics]
scores = pd.DataFrame(columns=["method", "max", "nanmedian"])
# Validate models # Validate models
lin_reg_rapids = linear_model.LinearRegression() dummy_regr = DummyRegressor(strategy="mean")
lin_reg_scores = cross_val_score( dummy_regr_scores = cross_validate(
lin_reg_rapids, dummy_regr,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring='r2' scoring=metrics
)
print("Dummy model:")
print("R^2: ", np.nanmedian(dummy_regr_scores['test_r2']))
scores_df = pd.DataFrame(dummy_regr_scores)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "dummy"
scores = pd.concat([scores, scores_df])
lin_reg_rapids = linear_model.LinearRegression()
lin_reg_scores = cross_validate(
lin_reg_rapids,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
) )
print("Linear regression:") print("Linear regression:")
print(np.median(lin_reg_scores)) print("R^2: ", np.nanmedian(lin_reg_scores['test_r2']))
scores = insert_row(scores, ["Linear regression",np.median(lin_reg_scores),np.max(lin_reg_scores)])
scores_df = pd.DataFrame(lin_reg_scores)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "linear_reg"
scores = pd.concat([scores, scores_df])
ridge_reg = linear_model.Ridge(alpha=.5) ridge_reg = linear_model.Ridge(alpha=.5)
ridge_reg_scores = cross_val_score( ridge_reg_scores = cross_validate(
ridge_reg, ridge_reg,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring="r2" scoring=metrics
) )
print("Ridge regression") print("Ridge regression")
print(np.median(ridge_reg_scores))
scores = insert_row(scores, ["Ridge regression",np.median(ridge_reg_scores),np.max(ridge_reg_scores)]) scores_df = pd.DataFrame(ridge_reg_scores)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "ridge_reg"
scores = pd.concat([scores, scores_df])
lasso_reg = linear_model.Lasso(alpha=0.1) lasso_reg = linear_model.Lasso(alpha=0.1)
lasso_reg_score = cross_val_score( lasso_reg_score = cross_validate(
lasso_reg, lasso_reg,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring="r2" scoring=metrics
) )
print("Lasso regression") print("Lasso regression")
print(np.median(lasso_reg_score))
scores = insert_row(scores, ["Lasso regression",np.median(lasso_reg_score),np.max(lasso_reg_score)]) scores_df = pd.DataFrame(lasso_reg_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "lasso_reg"
scores = pd.concat([scores, scores_df])
bayesian_ridge_reg = linear_model.BayesianRidge() bayesian_ridge_reg = linear_model.BayesianRidge()
bayesian_ridge_reg_score = cross_val_score( bayesian_ridge_reg_score = cross_validate(
bayesian_ridge_reg, bayesian_ridge_reg,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring="r2" scoring=metrics
) )
print("Bayesian Ridge") print("Bayesian Ridge")
print(np.median(bayesian_ridge_reg_score))
scores = insert_row(scores, ["Bayesian Ridge",np.median(bayesian_ridge_reg_score),np.max(bayesian_ridge_reg_score)]) scores_df = pd.DataFrame(bayesian_ridge_reg_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "bayesian_ridge"
scores = pd.concat([scores, scores_df])
ransac_reg = linear_model.RANSACRegressor() ransac_reg = linear_model.RANSACRegressor()
ransac_reg_score = cross_val_score( ransac_reg_score = cross_validate(
ransac_reg, ransac_reg,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring="r2" scoring=metrics
) )
print("RANSAC (outlier robust regression)") print("RANSAC (outlier robust regression)")
print(np.median(ransac_reg_score))
scores = insert_row(scores, ["RANSAC",np.median(ransac_reg_score),np.max(ransac_reg_score)]) scores_df = pd.DataFrame(ransac_reg_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "RANSAC"
scores = pd.concat([scores, scores_df])
svr = svm.SVR() svr = svm.SVR()
svr_score = cross_val_score( svr_score = cross_validate(
svr, svr,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring="r2" scoring=metrics
) )
print("Support vector regression") print("Support vector regression")
print(np.median(svr_score))
scores = insert_row(scores, ["Support vector regression",np.median(svr_score),np.max(svr_score)]) scores_df = pd.DataFrame(svr_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "SVR"
scores = pd.concat([scores, scores_df])
kridge = kernel_ridge.KernelRidge() kridge = kernel_ridge.KernelRidge()
kridge_score = cross_val_score( kridge_score = cross_validate(
kridge, kridge,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring="r2" scoring=metrics
) )
print("Kernel Ridge regression") print("Kernel Ridge regression")
print(np.median(kridge_score))
scores = insert_row(scores, ["Kernel Ridge regression",np.median(kridge_score),np.max(kridge_score)]) scores_df = pd.DataFrame(kridge_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "kernel_ridge"
scores = pd.concat([scores, scores_df])
gpr = gaussian_process.GaussianProcessRegressor() gpr = gaussian_process.GaussianProcessRegressor()
gpr_score = cross_val_score( gpr_score = cross_validate(
gpr, gpr,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring="r2" scoring=metrics
) )
print("Gaussian Process Regression") print("Gaussian Process Regression")
print(np.median(gpr_score))
scores = insert_row(scores, ["Gaussian Process Regression",np.median(gpr_score),np.max(gpr_score)]) scores_df = pd.DataFrame(gpr_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "gaussian_proc"
scores = pd.concat([scores, scores_df])
rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1) rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
rfr_score = cross_val_score( rfr_score = cross_validate(
rfr, rfr,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring="r2" scoring=metrics
) )
print("Random Forest Regression") print("Random Forest Regression")
print(np.median(rfr_score))
scores = insert_row(scores, ["Random Forest Regression",np.median(rfr_score),np.max(rfr_score)]) scores_df = pd.DataFrame(rfr_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "random_forest"
scores = pd.concat([scores, scores_df])
xgb = XGBRegressor() xgb = XGBRegressor()
xgb_score = cross_val_score( xgb_score = cross_validate(
xgb, xgb,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring="r2" scoring=metrics
) )
print("XGBoost Regressor") print("XGBoost Regressor")
print(np.median(xgb_score))
scores = insert_row(scores, ["XGBoost Regressor",np.median(xgb_score),np.max(xgb_score)]) scores_df = pd.DataFrame(xgb_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "XGBoost"
scores = pd.concat([scores, scores_df])
ada = ensemble.AdaBoostRegressor() ada = ensemble.AdaBoostRegressor()
ada_score = cross_val_score( ada_score = cross_validate(
ada, ada,
X=train_x, X=data_x,
y=data_y, y=data_y,
groups=data_groups, groups=data_groups,
cv=logo, cv=logo,
n_jobs=-1, n_jobs=-1,
scoring="r2" scoring=metrics
) )
print("ADA Boost Regressor") print("ADA Boost Regressor")
print(np.median(ada_score))
scores = insert_row(scores, ["ADA Boost Regressor",np.median(ada_score),np.max(ada_score)]) scores_df = pd.DataFrame(ada_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df["method"] = "ADA_boost"
scores = pd.concat([scores, scores_df])
return scores return scores