Reformat ml_pipeline_regression.py
junos
parent
583ee82e80
commit
48118f125d
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@ -15,64 +15,69 @@
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# %% jupyter={"source_hidden": true}
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# %matplotlib inline
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import datetime
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import importlib
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import os
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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import yaml
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from pyprojroot import here
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from sklearn import linear_model, svm, kernel_ridge, gaussian_process
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from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, cross_validate
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.impute import SimpleImputer
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from sklearn.dummy import DummyRegressor
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import xgboost as xg
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from IPython.core.interactiveshell import InteractiveShell
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InteractiveShell.ast_node_interactivity = "all"
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from sklearn import gaussian_process, kernel_ridge, linear_model, svm
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from sklearn.dummy import DummyRegressor
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from sklearn.impute import SimpleImputer
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from sklearn.model_selection import LeaveOneGroupOut, cross_validate
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# from IPython.core.interactiveshell import InteractiveShell
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# InteractiveShell.ast_node_interactivity = "all"
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nb_dir = os.path.split(os.getcwd())[0]
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if nb_dir not in sys.path:
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sys.path.append(nb_dir)
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import machine_learning.features_sensor
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import machine_learning.labels
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import machine_learning.model
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# %% [markdown]
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# # RAPIDS models
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# %% [markdown]
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# ## PANAS negative affect
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# %% jupyter={"source_hidden": true}
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model_input = pd.read_csv(
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"../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv"
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)
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# %% jupyter={"source_hidden": true}
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model_input = pd.read_csv("../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv")
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# %% jupyter={"source_hidden": true}
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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#if "pid" in model_input.columns:
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index_columns = [
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"local_segment",
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"local_segment_label",
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"local_segment_start_datetime",
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"local_segment_end_datetime",
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]
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# if "pid" in model_input.columns:
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# index_columns.append("pid")
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model_input.set_index(index_columns, inplace=True)
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cv_method = 'half_logo' # logo, half_logo, 5kfold
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if cv_method == 'logo':
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data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
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cv_method = "half_logo" # logo, half_logo, 5kfold
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if cv_method == "logo":
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data_x, data_y, data_groups = (
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model_input.drop(["target", "pid"], axis=1),
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model_input["target"],
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model_input["pid"],
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)
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else:
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model_input['pid_index'] = model_input.groupby('pid').cumcount()
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model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count')
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model_input["pid_index"] = model_input.groupby("pid").cumcount()
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model_input["pid_count"] = model_input.groupby("pid")["pid"].transform("count")
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model_input["pid_index"] = (model_input['pid_index'] / model_input['pid_count'] + 1).round()
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model_input["pid_half"] = model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
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model_input["pid_index"] = (
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model_input["pid_index"] / model_input["pid_count"] + 1
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).round()
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model_input["pid_half"] = (
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model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
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)
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data_x, data_y, data_groups = model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1), model_input["target"], model_input["pid_half"]
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data_x, data_y, data_groups = (
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model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1),
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model_input["target"],
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model_input["pid_half"],
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)
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# %% jupyter={"source_hidden": true}
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categorical_feature_colnames = ["gender", "startlanguage"]
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additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
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additional_categorical_features = [
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col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col
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]
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categorical_feature_colnames += additional_categorical_features
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# %% jupyter={"source_hidden": true}
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@ -109,7 +114,7 @@ logo.get_n_splits(
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)
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# Defaults to 5 k folds in cross_validate method
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if cv_method != 'logo' and cv_method != 'half_logo':
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if cv_method != "logo" and cv_method != "half_logo":
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logo = None
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# %% jupyter={"source_hidden": true}
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@ -120,7 +125,7 @@ sum(data_y.isna())
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dummy_regr = DummyRegressor(strategy="mean")
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# %% jupyter={"source_hidden": true}
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imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
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imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
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# %% jupyter={"source_hidden": true}
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dummy_regressor = cross_validate(
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@ -130,12 +135,26 @@ dummy_regressor = cross_validate(
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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scoring=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print("Negative Mean Squared Error", np.median(dummy_regressor['test_neg_mean_squared_error']))
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print("Negative Mean Absolute Error", np.median(dummy_regressor['test_neg_mean_absolute_error']))
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print("Negative Root Mean Squared Error", np.median(dummy_regressor['test_neg_root_mean_squared_error']))
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print("R2", np.median(dummy_regressor['test_r2']))
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print(
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"Negative Mean Squared Error",
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np.median(dummy_regressor["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(dummy_regressor["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(dummy_regressor["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(dummy_regressor["test_r2"]))
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# %% [markdown]
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# ### Linear Regression
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@ -143,7 +162,7 @@ print("R2", np.median(dummy_regressor['test_r2']))
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# %% jupyter={"source_hidden": true}
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lin_reg_rapids = linear_model.LinearRegression()
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# %% jupyter={"source_hidden": true}
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imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
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imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
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# %% jupyter={"source_hidden": true}
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lin_reg_scores = cross_validate(
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@ -153,19 +172,33 @@ lin_reg_scores = cross_validate(
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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scoring=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
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print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
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print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
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print("R2", np.median(lin_reg_scores['test_r2']))
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print(
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"Negative Mean Squared Error",
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np.median(lin_reg_scores["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(lin_reg_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(lin_reg_scores["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(lin_reg_scores["test_r2"]))
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# %% [markdown]
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# ### XGBRegressor Linear Regression
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# %% jupyter={"source_hidden": true}
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xgb_r = xg.XGBRegressor(objective ='reg:squarederror', n_estimators = 10)
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xgb_r = xg.XGBRegressor(objective="reg:squarederror", n_estimators=10)
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# %% jupyter={"source_hidden": true}
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imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
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imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
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# %% jupyter={"source_hidden": true}
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xgb_reg_scores = cross_validate(
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@ -175,19 +208,33 @@ xgb_reg_scores = cross_validate(
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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scoring=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print("Negative Mean Squared Error", np.median(xgb_reg_scores['test_neg_mean_squared_error']))
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print("Negative Mean Absolute Error", np.median(xgb_reg_scores['test_neg_mean_absolute_error']))
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print("Negative Root Mean Squared Error", np.median(xgb_reg_scores['test_neg_root_mean_squared_error']))
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print("R2", np.median(xgb_reg_scores['test_r2']))
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print(
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"Negative Mean Squared Error",
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np.median(xgb_reg_scores["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(xgb_reg_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(xgb_reg_scores["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(xgb_reg_scores["test_r2"]))
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# %% [markdown]
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# ### XGBRegressor Pseudo Huber Error Regression
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# %% jupyter={"source_hidden": true}
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xgb_psuedo_huber_r = xg.XGBRegressor(objective ='reg:pseudohubererror', n_estimators = 10)
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xgb_psuedo_huber_r = xg.XGBRegressor(objective="reg:pseudohubererror", n_estimators=10)
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# %% jupyter={"source_hidden": true}
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imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
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imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
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# %% jupyter={"source_hidden": true}
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xgb_psuedo_huber_reg_scores = cross_validate(
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@ -197,18 +244,32 @@ xgb_psuedo_huber_reg_scores = cross_validate(
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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scoring=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print("Negative Mean Squared Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_mean_squared_error']))
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print("Negative Mean Absolute Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_mean_absolute_error']))
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print("Negative Root Mean Squared Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_root_mean_squared_error']))
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print("R2", np.median(xgb_psuedo_huber_reg_scores['test_r2']))
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print(
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"Negative Mean Squared Error",
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np.median(xgb_psuedo_huber_reg_scores["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(xgb_psuedo_huber_reg_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(xgb_psuedo_huber_reg_scores["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(xgb_psuedo_huber_reg_scores["test_r2"]))
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# %% [markdown]
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# ### Ridge regression
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# %% jupyter={"source_hidden": true}
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ridge_reg = linear_model.Ridge(alpha=.5)
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ridge_reg = linear_model.Ridge(alpha=0.5)
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# %% tags=[] jupyter={"source_hidden": true}
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ridge_reg_scores = cross_validate(
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@ -218,12 +279,26 @@ ridge_reg_scores = cross_validate(
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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scoring=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print("Negative Mean Squared Error", np.median(ridge_reg_scores['test_neg_mean_squared_error']))
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print("Negative Mean Absolute Error", np.median(ridge_reg_scores['test_neg_mean_absolute_error']))
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print("Negative Root Mean Squared Error", np.median(ridge_reg_scores['test_neg_root_mean_squared_error']))
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print("R2", np.median(ridge_reg_scores['test_r2']))
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print(
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"Negative Mean Squared Error",
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np.median(ridge_reg_scores["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(ridge_reg_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(ridge_reg_scores["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(ridge_reg_scores["test_r2"]))
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# %% [markdown]
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# ### Lasso
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@ -239,12 +314,26 @@ lasso_reg_score = cross_validate(
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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scoring=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print("Negative Mean Squared Error", np.median(lasso_reg_score['test_neg_mean_squared_error']))
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print("Negative Mean Absolute Error", np.median(lasso_reg_score['test_neg_mean_absolute_error']))
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print("Negative Root Mean Squared Error", np.median(lasso_reg_score['test_neg_root_mean_squared_error']))
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print("R2", np.median(lasso_reg_score['test_r2']))
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print(
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"Negative Mean Squared Error",
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np.median(lasso_reg_score["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(lasso_reg_score["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(lasso_reg_score["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(lasso_reg_score["test_r2"]))
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# %% [markdown]
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# ### Bayesian Ridge
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@ -260,12 +349,26 @@ bayesian_ridge_reg_score = cross_validate(
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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scoring=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print("Negative Mean Squared Error", np.median(bayesian_ridge_reg_score['test_neg_mean_squared_error']))
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print("Negative Mean Absolute Error", np.median(bayesian_ridge_reg_score['test_neg_mean_absolute_error']))
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print("Negative Root Mean Squared Error", np.median(bayesian_ridge_reg_score['test_neg_root_mean_squared_error']))
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print("R2", np.median(bayesian_ridge_reg_score['test_r2']))
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print(
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"Negative Mean Squared Error",
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np.median(bayesian_ridge_reg_score["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(bayesian_ridge_reg_score["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(bayesian_ridge_reg_score["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(bayesian_ridge_reg_score["test_r2"]))
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# %% [markdown]
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# ### RANSAC (outlier robust regression)
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@ -281,12 +384,26 @@ ransac_reg_scores = cross_validate(
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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scoring=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
|
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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 +419,25 @@ 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 +453,26 @@ 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 +489,24 @@ 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"]))
|
||||
|
||||
# %%
|
||||
|
|
|
|||
Loading…
Reference in New Issue