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stress_at_work_analysis
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Use methods in helper.py.

master
junos 2023-04-21 21:41:00 +02:00
parent 48118f125d
commit c66e046014
2 changed files with 27 additions and 73 deletions
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66
exploration/ml_pipeline_regression.py
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@ -21,6 +21,7 @@ import sys
import numpy as np import numpy as np
import pandas as pd import pandas as pd
import xgboost as xg import xgboost as xg
from machine_learning.helper import prepare_regression_model_input
from sklearn import gaussian_process, kernel_ridge, linear_model, svm from sklearn import gaussian_process, kernel_ridge, linear_model, svm
from sklearn.dummy import DummyRegressor from sklearn.dummy import DummyRegressor
from sklearn.impute import SimpleImputer from sklearn.impute import SimpleImputer
@ -39,72 +40,9 @@ model_input = pd.read_csv(
) )
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": true}
index_columns = [
"local_segment",
"local_segment_label",
"local_segment_start_datetime",
"local_segment_end_datetime",
]
# if "pid" in model_input.columns:
# index_columns.append("pid")
model_input.set_index(index_columns, inplace=True)
cv_method = "half_logo" # logo, half_logo, 5kfold cv_method = "half_logo" # logo, half_logo, 5kfold
if cv_method == "logo":
data_x, data_y, data_groups = (
model_input.drop(["target", "pid"], axis=1),
model_input["target"],
model_input["pid"],
)
else:
model_input["pid_index"] = model_input.groupby("pid").cumcount()
model_input["pid_count"] = model_input.groupby("pid")["pid"].transform("count")
model_input["pid_index"] = (
model_input["pid_index"] / model_input["pid_count"] + 1
).round()
model_input["pid_half"] = (
model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
)
data_x, data_y, data_groups = (
model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1),
model_input["target"],
model_input["pid_half"],
)
# %% jupyter={"source_hidden": true}
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
# %% jupyter={"source_hidden": true}
categorical_features = data_x[categorical_feature_colnames].copy()
# %% jupyter={"source_hidden": true}
mode_categorical_features = categorical_features.mode().iloc[0]
# %% jupyter={"source_hidden": true}
# fillna with mode
categorical_features = categorical_features.fillna(mode_categorical_features)
# %% jupyter={"source_hidden": true}
# one-hot encoding
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
if not categorical_features.empty:
categorical_features = pd.get_dummies(categorical_features)
# %% jupyter={"source_hidden": true}
numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
# %% jupyter={"source_hidden": true}
train_x = pd.concat([numerical_features, categorical_features], axis=1)
# %% jupyter={"source_hidden": true}
train_x.dtypes
train_x, data_y, data_groups = prepare_regression_model_input(model_input, cv_method)
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": true}
logo = LeaveOneGroupOut() logo = LeaveOneGroupOut()
logo.get_n_splits( logo.get_n_splits(

24
machine_learning/helper.py
View File

@ -73,9 +73,7 @@ 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_regression_model_input(input_csv): def prepare_regression_model_input(model_input, cv_method="logo"):
model_input = pd.read_csv(input_csv)
index_columns = [ index_columns = [
"local_segment", "local_segment",
"local_segment_label", "local_segment_label",
@ -84,11 +82,28 @@ def prepare_regression_model_input(input_csv):
] ]
model_input.set_index(index_columns, inplace=True) model_input.set_index(index_columns, inplace=True)
if cv_method == "logo":
data_x, data_y, data_groups = ( data_x, data_y, data_groups = (
model_input.drop(["target", "pid"], axis=1), model_input.drop(["target", "pid"], axis=1),
model_input["target"], model_input["target"],
model_input["pid"], model_input["pid"],
) )
else:
model_input["pid_index"] = model_input.groupby("pid").cumcount()
model_input["pid_count"] = model_input.groupby("pid")["pid"].transform("count")
model_input["pid_index"] = (
model_input["pid_index"] / model_input["pid_count"] + 1
).round()
model_input["pid_half"] = (
model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
)
data_x, data_y, data_groups = (
model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1),
model_input["target"],
model_input["pid_half"],
)
categorical_feature_colnames = [ categorical_feature_colnames = [
"gender", "gender",
@ -101,8 +116,9 @@ def prepare_regression_model_input(input_csv):
if "mostcommonactivity" in col or "homelabel" in col if "mostcommonactivity" in col or "homelabel" in col
] ]
categorical_feature_colnames += additional_categorical_features categorical_feature_colnames += additional_categorical_features
# TODO: check whether limesurvey_demand_control_ratio_quartile NaNs could be replaced meaningfully
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
categorical_features = categorical_features.fillna(mode_categorical_features) categorical_features = categorical_features.fillna(mode_categorical_features)