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11 Commits

Author SHA1 Message Date
junos c66e046014 Use methods in helper.py. 2023-04-21 21:41:00 +02:00
junos 48118f125d Reformat ml_pipeline_regression.py 2023-04-21 21:34:54 +02:00
junos 583ee82e80 Add xgboost to dependencies and reformat helper.py. 2023-04-21 21:33:06 +02:00
junos 59552c18a9 Update python to 3.11. 2023-04-21 18:08:54 +02:00
junos a4ad4c3200 Check the relationship between is_system_app and System category. 2023-04-19 11:06:33 +02:00
junos 7e565c34db Only reclassify apps not found on Play Store.
Update coding files for app categories.
2023-04-19 11:01:00 +02:00
junos d6eea0fc00 Completely classify unknown applications. 2023-04-19 10:54:46 +02:00
junos 711b451eff Start to better classify system apps. 2023-04-19 09:48:57 +02:00
junos 0e66a5a963 Completely remove PACKAGE_NAMES_HASHED and instead provide a differently structured file. 2023-04-19 09:29:40 +02:00
junos c88cecc063 Categorize applications in config.yaml. 2023-04-18 20:39:58 +02:00
junos 66754a24aa Create and save catalogue. 2023-04-18 16:10:11 +02:00
8 changed files with 1551 additions and 288 deletions

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@ -3,6 +3,5 @@
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
<mapping directory="$PROJECT_DIR$/rapids" vcs="Git" />
<mapping directory="$PROJECT_DIR$/rapids/calculatingfeatures" vcs="Git" />
</component>
</project>

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@ -1,9 +1,8 @@
name: straw2analysis
channels:
- defaults
- conda-forge
dependencies:
- python=3.9
- python=3.11
- black
- isort
- flake8
@ -24,3 +23,4 @@ dependencies:
- sqlalchemy
- statsmodels
- tabulate
- xgboost

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@ -15,91 +15,34 @@
# %% jupyter={"source_hidden": true}
# %matplotlib inline
import datetime
import importlib
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import yaml
from pyprojroot import here
from sklearn import linear_model, svm, kernel_ridge, gaussian_process
from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, cross_validate
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.impute import SimpleImputer
from sklearn.dummy import DummyRegressor
import xgboost as xg
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
from machine_learning.helper import prepare_regression_model_input
from sklearn import gaussian_process, kernel_ridge, linear_model, svm
from sklearn.dummy import DummyRegressor
from sklearn.impute import SimpleImputer
from sklearn.model_selection import LeaveOneGroupOut, cross_validate
# from IPython.core.interactiveshell import InteractiveShell
# InteractiveShell.ast_node_interactivity = "all"
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
import machine_learning.features_sensor
import machine_learning.labels
import machine_learning.model
# %% [markdown]
# # RAPIDS models
# %% [markdown]
# ## PANAS negative affect
# %% jupyter={"source_hidden": true}
model_input = pd.read_csv(
"../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv"
)
# %% jupyter={"source_hidden": true}
model_input = pd.read_csv("../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv")
# %% 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
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
cv_method = "half_logo" # logo, half_logo, 5kfold
train_x, data_y, data_groups = prepare_regression_model_input(model_input, cv_method)
# %% jupyter={"source_hidden": true}
logo = LeaveOneGroupOut()
logo.get_n_splits(
@ -109,7 +52,7 @@ logo.get_n_splits(
)
# Defaults to 5 k folds in cross_validate method
if cv_method != 'logo' and cv_method != 'half_logo':
if cv_method != "logo" and cv_method != "half_logo":
logo = None
# %% jupyter={"source_hidden": true}
@ -120,7 +63,7 @@ sum(data_y.isna())
dummy_regr = DummyRegressor(strategy="mean")
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
# %% jupyter={"source_hidden": true}
dummy_regressor = cross_validate(
@ -130,12 +73,26 @@ dummy_regressor = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(dummy_regressor['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(dummy_regressor['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(dummy_regressor['test_neg_root_mean_squared_error']))
print("R2", np.median(dummy_regressor['test_r2']))
print(
"Negative Mean Squared Error",
np.median(dummy_regressor["test_neg_mean_squared_error"]),
)
print(
"Negative Mean Absolute Error",
np.median(dummy_regressor["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(dummy_regressor["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(dummy_regressor["test_r2"]))
# %% [markdown]
# ### Linear Regression
@ -143,7 +100,7 @@ print("R2", np.median(dummy_regressor['test_r2']))
# %% jupyter={"source_hidden": true}
lin_reg_rapids = linear_model.LinearRegression()
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
# %% jupyter={"source_hidden": true}
lin_reg_scores = cross_validate(
@ -153,19 +110,33 @@ lin_reg_scores = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(lin_reg_scores['test_r2']))
print(
"Negative Mean Squared Error",
np.median(lin_reg_scores["test_neg_mean_squared_error"]),
)
print(
"Negative Mean Absolute Error",
np.median(lin_reg_scores["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(lin_reg_scores["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(lin_reg_scores["test_r2"]))
# %% [markdown]
# ### XGBRegressor Linear Regression
# %% jupyter={"source_hidden": true}
xgb_r = xg.XGBRegressor(objective ='reg:squarederror', n_estimators = 10)
xgb_r = xg.XGBRegressor(objective="reg:squarederror", n_estimators=10)
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
# %% jupyter={"source_hidden": true}
xgb_reg_scores = cross_validate(
@ -175,19 +146,33 @@ xgb_reg_scores = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(xgb_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(xgb_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(xgb_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(xgb_reg_scores['test_r2']))
print(
"Negative Mean Squared Error",
np.median(xgb_reg_scores["test_neg_mean_squared_error"]),
)
print(
"Negative Mean Absolute Error",
np.median(xgb_reg_scores["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(xgb_reg_scores["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(xgb_reg_scores["test_r2"]))
# %% [markdown]
# ### XGBRegressor Pseudo Huber Error Regression
# %% jupyter={"source_hidden": true}
xgb_psuedo_huber_r = xg.XGBRegressor(objective ='reg:pseudohubererror', n_estimators = 10)
xgb_psuedo_huber_r = xg.XGBRegressor(objective="reg:pseudohubererror", n_estimators=10)
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
# %% jupyter={"source_hidden": true}
xgb_psuedo_huber_reg_scores = cross_validate(
@ -197,18 +182,32 @@ xgb_psuedo_huber_reg_scores = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(xgb_psuedo_huber_reg_scores['test_r2']))
print(
"Negative Mean Squared Error",
np.median(xgb_psuedo_huber_reg_scores["test_neg_mean_squared_error"]),
)
print(
"Negative Mean Absolute Error",
np.median(xgb_psuedo_huber_reg_scores["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(xgb_psuedo_huber_reg_scores["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(xgb_psuedo_huber_reg_scores["test_r2"]))
# %% [markdown]
# ### Ridge regression
# %% jupyter={"source_hidden": true}
ridge_reg = linear_model.Ridge(alpha=.5)
ridge_reg = linear_model.Ridge(alpha=0.5)
# %% tags=[] jupyter={"source_hidden": true}
ridge_reg_scores = cross_validate(
@ -218,12 +217,26 @@ ridge_reg_scores = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(ridge_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(ridge_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(ridge_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(ridge_reg_scores['test_r2']))
print(
"Negative Mean Squared Error",
np.median(ridge_reg_scores["test_neg_mean_squared_error"]),
)
print(
"Negative Mean Absolute Error",
np.median(ridge_reg_scores["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(ridge_reg_scores["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(ridge_reg_scores["test_r2"]))
# %% [markdown]
# ### Lasso
@ -239,12 +252,26 @@ lasso_reg_score = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(lasso_reg_score['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(lasso_reg_score['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(lasso_reg_score['test_neg_root_mean_squared_error']))
print("R2", np.median(lasso_reg_score['test_r2']))
print(
"Negative Mean Squared Error",
np.median(lasso_reg_score["test_neg_mean_squared_error"]),
)
print(
"Negative Mean Absolute Error",
np.median(lasso_reg_score["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(lasso_reg_score["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(lasso_reg_score["test_r2"]))
# %% [markdown]
# ### Bayesian Ridge
@ -260,12 +287,26 @@ bayesian_ridge_reg_score = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(bayesian_ridge_reg_score['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(bayesian_ridge_reg_score['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(bayesian_ridge_reg_score['test_neg_root_mean_squared_error']))
print("R2", np.median(bayesian_ridge_reg_score['test_r2']))
print(
"Negative Mean Squared Error",
np.median(bayesian_ridge_reg_score["test_neg_mean_squared_error"]),
)
print(
"Negative Mean Absolute Error",
np.median(bayesian_ridge_reg_score["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(bayesian_ridge_reg_score["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(bayesian_ridge_reg_score["test_r2"]))
# %% [markdown]
# ### RANSAC (outlier robust regression)
@ -281,12 +322,26 @@ ransac_reg_scores = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(ransac_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(ransac_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(ransac_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(ransac_reg_scores['test_r2']))
print(
"Negative Mean Squared Error",
np.median(ransac_reg_scores["test_neg_mean_squared_error"]),
)
print(
"Negative Mean Absolute Error",
np.median(ransac_reg_scores["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(ransac_reg_scores["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(ransac_reg_scores["test_r2"]))
# %% [markdown]
# ### Support vector regression
@ -302,12 +357,25 @@ svr_scores = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(svr_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(svr_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(svr_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(svr_scores['test_r2']))
print(
"Negative Mean Squared Error", np.median(svr_scores["test_neg_mean_squared_error"])
)
print(
"Negative Mean Absolute Error",
np.median(svr_scores["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(svr_scores["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(svr_scores["test_r2"]))
# %% [markdown]
# ### Kernel Ridge regression
@ -323,12 +391,26 @@ kridge_scores = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(kridge_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(kridge_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(kridge_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(kridge_scores['test_r2']))
print(
"Negative Mean Squared Error",
np.median(kridge_scores["test_neg_mean_squared_error"]),
)
print(
"Negative Mean Absolute Error",
np.median(kridge_scores["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(kridge_scores["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(kridge_scores["test_r2"]))
# %% [markdown]
# ### Gaussian Process Regression
@ -345,11 +427,24 @@ gpr_scores = cross_validate(
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
scoring=(
"r2",
"neg_mean_squared_error",
"neg_mean_absolute_error",
"neg_root_mean_squared_error",
),
)
print("Negative Mean Squared Error", np.median(gpr_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(gpr_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(gpr_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(gpr_scores['test_r2']))
print(
"Negative Mean Squared Error", np.median(gpr_scores["test_neg_mean_squared_error"])
)
print(
"Negative Mean Absolute Error",
np.median(gpr_scores["test_neg_mean_absolute_error"]),
)
print(
"Negative Root Mean Squared Error",
np.median(gpr_scores["test_neg_root_mean_squared_error"]),
)
print("R2", np.median(gpr_scores["test_r2"]))
# %%

View File

@ -1,15 +1,18 @@
from pathlib import Path
from sklearn import linear_model, svm, kernel_ridge, gaussian_process, ensemble, naive_bayes, neighbors, tree
from sklearn.model_selection import LeaveOneGroupOut, cross_validate, cross_validate
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.impute import SimpleImputer
from sklearn.dummy import DummyRegressor, DummyClassifier
from xgboost import XGBRegressor, XGBClassifier
import xgboost as xg
import pandas as pd
import numpy as np
import pandas as pd
from sklearn import (
ensemble,
gaussian_process,
kernel_ridge,
linear_model,
naive_bayes,
svm,
)
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.model_selection import LeaveOneGroupOut, cross_validate
from xgboost import XGBClassifier, XGBRegressor
def safe_outer_merge_on_index(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
@ -65,28 +68,64 @@ def construct_full_path(folder: Path, filename_prefix: str, data_type: str) -> P
full_path = folder / export_filename
return full_path
def insert_row(df, row):
return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True)
def prepare_regression_model_input(input_csv):
model_input = pd.read_csv(input_csv)
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
def prepare_regression_model_input(model_input, cv_method="logo"):
index_columns = [
"local_segment",
"local_segment_label",
"local_segment_start_datetime",
"local_segment_end_datetime",
]
model_input.set_index(index_columns, inplace=True)
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
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")
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]
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 = [
"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
categorical_features = data_x[categorical_feature_colnames].copy()
mode_categorical_features = categorical_features.mode().iloc[0]
# fillna with mode
categorical_features = categorical_features.fillna(mode_categorical_features)
# one-hot encoding
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
categorical_features = categorical_features.apply(
lambda col: col.astype("category")
)
if not categorical_features.empty:
categorical_features = pd.get_dummies(categorical_features)
@ -108,7 +147,7 @@ def run_all_regression_models(input_csv):
data_y,
groups=data_groups,
)
metrics = ['r2', 'neg_mean_absolute_error', 'neg_root_mean_squared_error']
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"])
@ -121,13 +160,13 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Dummy model:")
print("R^2: ", np.nanmedian(dummy_regr_scores['test_r2']))
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 = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "dummy"
scores = pd.concat([scores, scores_df])
@ -139,17 +178,17 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Linear regression:")
print("R^2: ", np.nanmedian(lin_reg_scores['test_r2']))
print("R^2: ", np.nanmedian(lin_reg_scores["test_r2"]))
scores_df = pd.DataFrame(lin_reg_scores)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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=0.5)
ridge_reg_scores = cross_validate(
ridge_reg,
X=data_x,
@ -157,16 +196,15 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Ridge regression")
scores_df = pd.DataFrame(ridge_reg_scores)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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_score = cross_validate(
lasso_reg,
@ -175,12 +213,12 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Lasso regression")
scores_df = pd.DataFrame(lasso_reg_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "lasso_reg"
scores = pd.concat([scores, scores_df])
@ -192,12 +230,12 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Bayesian Ridge")
scores_df = pd.DataFrame(bayesian_ridge_reg_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "bayesian_ridge"
scores = pd.concat([scores, scores_df])
@ -209,29 +247,23 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("RANSAC (outlier robust regression)")
scores_df = pd.DataFrame(ransac_reg_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "RANSAC"
scores = pd.concat([scores, scores_df])
svr = svm.SVR()
svr_score = cross_validate(
svr,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
svr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
)
print("Support vector regression")
scores_df = pd.DataFrame(svr_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "SVR"
scores = pd.concat([scores, scores_df])
@ -243,80 +275,56 @@ def run_all_regression_models(input_csv):
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
scoring=metrics,
)
print("Kernel Ridge regression")
scores_df = pd.DataFrame(kridge_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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_score = cross_validate(
gpr,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
gpr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
)
print("Gaussian Process Regression")
scores_df = pd.DataFrame(gpr_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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_score = cross_validate(
rfr,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
rfr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
)
print("Random Forest Regression")
scores_df = pd.DataFrame(rfr_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "random_forest"
scores = pd.concat([scores, scores_df])
xgb = XGBRegressor()
xgb_score = cross_validate(
xgb,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
xgb, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
)
print("XGBoost Regressor")
scores_df = pd.DataFrame(xgb_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "XGBoost"
scores = pd.concat([scores, scores_df])
ada = ensemble.AdaBoostRegressor()
ada_score = cross_validate(
ada,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=metrics
ada, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
)
print("ADA Boost Regressor")
scores_df = pd.DataFrame(ada_score)[test_metrics]
scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "ADA_boost"
scores = pd.concat([scores, scores_df])
@ -324,7 +332,7 @@ def run_all_regression_models(input_csv):
def run_all_classification_models(data_x, data_y, data_groups, cv_method):
metrics = ['accuracy', 'average_precision', 'recall', 'f1']
metrics = ["accuracy", "average_precision", "recall", "f1"]
test_metrics = ["test_" + metric for metric in metrics]
scores = pd.DataFrame(columns=["method", "max", "mean"])
@ -332,127 +340,127 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
dummy_class = DummyClassifier(strategy="most_frequent")
dummy_score = cross_validate(
dummy_class,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
error_score='raise',
scoring=metrics
dummy_class,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
error_score="raise",
scoring=metrics,
)
print("Dummy")
scores_df = pd.DataFrame(dummy_score)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "Dummy"
scores = pd.concat([scores, scores_df])
logistic_regression = linear_model.LogisticRegression()
log_reg_scores = cross_validate(
logistic_regression,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
logistic_regression,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("Logistic regression")
scores_df = pd.DataFrame(log_reg_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "logistic_reg"
scores = pd.concat([scores, scores_df])
svc = svm.SVC()
svc_scores = cross_validate(
svc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
svc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("Support Vector Machine")
scores_df = pd.DataFrame(svc_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "svc"
scores = pd.concat([scores, scores_df])
gaussian_nb = naive_bayes.GaussianNB()
gaussian_nb_scores = cross_validate(
gaussian_nb,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
gaussian_nb,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("Gaussian Naive Bayes")
scores_df = pd.DataFrame(gaussian_nb_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "gaussian_naive_bayes"
scores = pd.concat([scores, scores_df])
sgdc = linear_model.SGDClassifier()
sgdc_scores = cross_validate(
sgdc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
sgdc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("Stochastic Gradient Descent")
scores_df = pd.DataFrame(sgdc_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "stochastic_gradient_descent"
scores = pd.concat([scores, scores_df])
rfc = ensemble.RandomForestClassifier()
rfc_scores = cross_validate(
rfc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
rfc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("Random Forest")
scores_df = pd.DataFrame(rfc_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "random_forest"
scores = pd.concat([scores, scores_df])
xgb_classifier = XGBClassifier()
xgb_scores = cross_validate(
xgb_classifier,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics
xgb_classifier,
X=data_x,
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
scoring=metrics,
)
print("XGBoost")
scores_df = pd.DataFrame(xgb_scores)[test_metrics]
scores_df = scores_df.agg(['max', 'mean']).transpose()
scores_df = scores_df.agg(["max", "mean"]).transpose()
scores_df["method"] = "xgboost"
scores = pd.concat([scores, scores_df])

View File

@ -34,18 +34,114 @@ df_app_categories <- tbl(con, "app_categories") %>%
head(df_app_categories)
table(df_app_categories$play_store_genre)
# Correct some mistakes
df_app_categories %<>% mutate(
play_store_genre = {
function(x) {
df_app_categories %>%
filter(play_store_genre == "not_found") %>%
group_by(play_store_response) %>%
count()
# All "not_found" have an HTTP status of 404.
df_app_categories %>%
filter(play_store_genre == "not_found") %>%
group_by(package_name) %>%
count() %>%
arrange(desc(n))
# All "not_found" apps are unique.
# Exclude phone manufacturers, custom ROM names and similar.
manufacturers <- c(
"samsung",
"oneplus",
"huawei",
"xiaomi",
"lge",
"motorola",
"miui",
"lenovo",
"oppo",
"mediatek"
)
custom_rom <- c("coloros", "lineageos", "myos", "cyanogenmod", "foundation.e")
other <- c("android", "wssyncmldm")
grep_pattern <- paste(c(manufacturers, custom_rom, other), collapse = "|")
rows_os_manufacturer <- grepl(grep_pattern, df_app_categories$package_name)
# Explore what remains after excluding above.
df_app_categories[!rows_os_manufacturer, ] %>%
filter(play_store_genre == "not_found")
# Also check the relationship between is_system_app and System category.
tbl(con, "applications") %>%
filter(is_system_app, play_store_genre != "System") %>%
count()
# They are perfectly correlated.
# Manually classify apps
df_app_categories[df_app_categories$play_store_genre == "not_found",] <-
df_app_categories %>%
filter(play_store_genre == "not_found") %>%
mutate(
play_store_genre =
case_when(
x == "Education,Education" ~ "Education",
x == "EducationEducation" ~ "Education",
x == "not_found" ~ "System",
.default = x
str_detect(str_to_lower(package_name), grep_pattern) ~ "System",
str_detect(str_to_lower(package_name), "straw") ~ "STRAW",
str_detect(str_to_lower(package_name), "chromium") ~ "Communication", # Same as chrome.
str_detect(str_to_lower(package_name), "skype") ~ "Communication", # Skype Lite not classified.
str_detect(str_to_lower(package_name), "imsservice") ~ "Communication", # IP Multimedia Subsystem
str_detect(str_to_lower(package_name), paste(c("covid", "empatica"), collapse = "|")) ~ "Medical",
str_detect(str_to_lower(package_name), paste(c("libri", "tachiyomi"), collapse = "|")) ~ "Books & Reference",
str_detect(str_to_lower(package_name), paste(c("bricks", "chess"), collapse = "|")) ~ "Casual",
str_detect(str_to_lower(package_name), "weather") ~ "Weather",
str_detect(str_to_lower(package_name), "excel") ~ "Productivity",
str_detect(str_to_lower(package_name), paste(c("qr", "barcode", "archimedes", "mixplorer", "winrar", "filemanager", "shot", "faceunlock", "signin", "milink"), collapse = "|")) ~ "Tools",
str_detect(str_to_lower(package_name), "stupeflix") ~ "Photography",
str_detect(str_to_lower(package_name), "anyme") ~ "Entertainment",
str_detect(str_to_lower(package_name), "vanced") ~ "Video Players & Editors",
str_detect(str_to_lower(package_name), paste(c("music", "radio", "dolby"), collapse = "|")) ~ "Music & Audio",
str_detect(str_to_lower(package_name), paste(c("tensorflow", "object_detection"), collapse = "|")) ~ "Education",
.default = play_store_genre
)
}
}(play_store_genre)
)
# Explore what remains after classifying above.
df_app_categories %>%
filter(play_store_genre == "not_found")
# After this, 13 applications remain, which I will classify as "Other".
# Correct some mistakes
# And classify 'not_found'
df_app_categories %<>%
mutate(
play_store_genre = {
function(x) {
case_when(
x == "Education,Education" ~ "Education",
x == "EducationEducation" ~ "Education",
x == "not_found" ~ "Other",
.default = x
)
}
}(play_store_genre)
) %>%
select(-package_name) %>%
rename(
genre = play_store_genre,
package_name = package_hash
)
table(df_app_categories$genre)
df_app_categories %>%
group_by(genre) %>%
count() %>%
arrange(desc(n)) %>%
write_csv("play_store_categories_count.csv")
write_csv(
x = select(df_app_categories, c(package_name, genre)),
file = "play_store_application_genre_catalogue.csv"
)
dbDisconnect(con)

File diff suppressed because it is too large Load Diff

View File

@ -0,0 +1,45 @@
genre,n
System,261
Tools,96
Productivity,71
Health & Fitness,60
Finance,54
Communication,39
Music & Audio,39
Shopping,38
Lifestyle,33
Education,28
News & Magazines,24
Maps & Navigation,23
Entertainment,21
Business,18
Travel & Local,18
Books & Reference,16
Social,16
Weather,16
Food & Drink,14
Sports,14
Other,13
Photography,13
Puzzle,13
Video Players & Editors,12
Card,9
Casual,9
Personalization,8
Medical,7
Board,5
Strategy,4
House & Home,3
Trivia,3
Word,3
Adventure,2
Art & Design,2
Auto & Vehicles,2
Dating,2
Role Playing,2
STRAW,2
Simulation,2
"Board,Brain Games",1
"Entertainment,Music & Video",1
Parenting,1
Racing,1
1 genre n
2 System 261
3 Tools 96
4 Productivity 71
5 Health & Fitness 60
6 Finance 54
7 Communication 39
8 Music & Audio 39
9 Shopping 38
10 Lifestyle 33
11 Education 28
12 News & Magazines 24
13 Maps & Navigation 23
14 Entertainment 21
15 Business 18
16 Travel & Local 18
17 Books & Reference 16
18 Social 16
19 Weather 16
20 Food & Drink 14
21 Sports 14
22 Other 13
23 Photography 13
24 Puzzle 13
25 Video Players & Editors 12
26 Card 9
27 Casual 9
28 Personalization 8
29 Medical 7
30 Board 5
31 Strategy 4
32 House & Home 3
33 Trivia 3
34 Word 3
35 Adventure 2
36 Art & Design 2
37 Auto & Vehicles 2
38 Dating 2
39 Role Playing 2
40 STRAW 2
41 Simulation 2
42 Board,Brain Games 1
43 Entertainment,Music & Video 1
44 Parenting 1
45 Racing 1

2
rapids

@ -1 +1 @@
Subproject commit 03687a1ac204f0a4347eb758dada8005f68b0bb1
Subproject commit 63f5a526fce4d288499168e1701adadb8b885d82