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stress_at_work_analysis
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Update function with imputation already handled.

ml_pipeline
junos 2022-11-16 18:18:12 +01:00
parent a5c09a292f
commit c462d55096
1 changed files with 13 additions and 15 deletions
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28
machine_learning/helper.py
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@ -69,8 +69,6 @@ def insert_row(df, row):
def run_all_models(input_csv):
# Prepare data
model_input = pd.read_csv(input_csv)
model_input.dropna(axis=1, how="all", inplace=True)
model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
model_input.set_index(index_columns, inplace=True)
@ -78,6 +76,8 @@ def run_all_models(input_csv):
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
categorical_feature_colnames = ["gender", "startlanguage"]
additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
categorical_feature_colnames += additional_categorical_features
categorical_features = data_x[categorical_feature_colnames].copy()
mode_categorical_features = categorical_features.mode().iloc[0]
# fillna with mode
@ -90,8 +90,6 @@ def run_all_models(input_csv):
numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
train_x = pd.concat([numerical_features, categorical_features], axis=1)
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
train_x_imputed = imputer.fit_transform(train_x)
# Prepare cross validation
logo = LeaveOneGroupOut()
@ -106,7 +104,7 @@ def run_all_models(input_csv):
lin_reg_rapids = linear_model.LinearRegression()
lin_reg_scores = cross_val_score(
lin_reg_rapids,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,
@ -120,7 +118,7 @@ def run_all_models(input_csv):
ridge_reg = linear_model.Ridge(alpha=.5)
ridge_reg_scores = cross_val_score(
ridge_reg,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,
@ -134,7 +132,7 @@ def run_all_models(input_csv):
lasso_reg = linear_model.Lasso(alpha=0.1)
lasso_reg_score = cross_val_score(
lasso_reg,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,
@ -148,7 +146,7 @@ def run_all_models(input_csv):
bayesian_ridge_reg = linear_model.BayesianRidge()
bayesian_ridge_reg_score = cross_val_score(
bayesian_ridge_reg,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,
@ -162,7 +160,7 @@ def run_all_models(input_csv):
ransac_reg = linear_model.RANSACRegressor()
ransac_reg_score = cross_val_score(
ransac_reg,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,
@ -176,7 +174,7 @@ def run_all_models(input_csv):
svr = svm.SVR()
svr_score = cross_val_score(
svr,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,
@ -190,7 +188,7 @@ def run_all_models(input_csv):
kridge = kernel_ridge.KernelRidge()
kridge_score = cross_val_score(
kridge,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,
@ -204,7 +202,7 @@ def run_all_models(input_csv):
gpr = gaussian_process.GaussianProcessRegressor()
gpr_score = cross_val_score(
gpr,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,
@ -218,7 +216,7 @@ def run_all_models(input_csv):
rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
rfr_score = cross_val_score(
rfr,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,
@ -232,7 +230,7 @@ def run_all_models(input_csv):
xgb = XGBRegressor()
xgb_score = cross_val_score(
xgb,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,
@ -246,7 +244,7 @@ def run_all_models(input_csv):
ada = ensemble.AdaBoostRegressor()
ada_score = cross_val_score(
ada,
X=train_x_imputed,
X=train_x,
y=data_y,
groups=data_groups,
cv=logo,