Browse Source

Revert "Add a rule for model baselines."

The example was for a classification rather than regression problem.

This reverts commit 9ab0c8f289.

# Conflicts:
#	rules/models.smk
master
junos 1 month ago
parent
commit
9f5edf1c2b
  1. 12
      rules/models.smk
  2. 105
      src/models/model_baselines.py
  3. 164
      src/models/modelling_utils.py

12
rules/models.smk

@ -49,15 +49,3 @@ rule merge_features_and_targets_for_population_model:
script:
"../src/models/merge_features_and_targets_for_population_model.py"
rule model_individual_baselines:
input:
"data/processed/models/individual_model/{pid}/input.csv"
params:
cv_method = "{cv_method}",
colnames_demographic_features = config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["FEATURES"],
output:
"data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv"
log:
"data/processed/models/individual_model/{pid}/output_{cv_method}/baselines_notes.log"
script:
"../src/models/model_baselines.py"

105
src/models/model_baselines.py

@ -1,105 +0,0 @@
import numpy as np
import pandas as pd
from statistics import mean
from modelling_utils import getMetrics, createPipeline
from sklearn.model_selection import LeaveOneOut
# As we do not have probability of each category, use label to denote the probability directly.
# The probability will only be used to calculate the AUC value.
def baselineAccuracyOfMajorityClassClassifier(targets):
majority_class = targets["target"].value_counts().idxmax()
pred_y = [majority_class] * targets.shape[0]
pred_y_proba = pred_y
metrics = getMetrics(pred_y, pred_y_proba, targets["target"].values.ravel().tolist())
return metrics, majority_class
def baselineMetricsOfRandomWeightedClassifier(targets, majority_ratio, majority_class, iter_times):
metrics_all_iters = {"accuracy": [], "precision0":[], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "f1_macro": [], "auc": [], "kappa": []}
probabilities = [0, 0]
probabilities[majority_class], probabilities[1 - majority_class] = majority_ratio, 1 - majority_ratio
for i in range(iter_times):
pred_y = np.random.RandomState(i).multinomial(1, probabilities, targets.shape[0])[:,1].tolist()
pred_y_proba = pred_y
metrics = getMetrics(pred_y, pred_y_proba, targets["target"].values.ravel().tolist())
for key in metrics_all_iters.keys():
metrics_all_iters[key].append(metrics[key].item())
# Calculate average metrics across all iterations
avg_metrics = {}
for key in metrics_all_iters.keys():
avg_metrics[key] = mean(metrics_all_iters[key])
return avg_metrics
def baselineMetricsOfDTWithDemographicFeatures(cv_method, data_x, data_y, oversampler_type):
pred_y, true_y = [], []
for train_index, test_index in cv_method.split(data_x):
train_x, test_x = data_x.iloc[train_index], data_x.iloc[test_index]
train_y, test_y = data_y.iloc[train_index], data_y.iloc[test_index]
clf = createPipeline("DT", oversampler_type)
clf.fit(train_x, train_y.values.ravel())
pred_y = pred_y + clf.predict(test_x).ravel().tolist()
pred_y_proba = pred_y
true_y = true_y + test_y.values.ravel().tolist()
return getMetrics(pred_y, pred_y_proba, true_y)
cv_method = globals()[snakemake.params["cv_method"]]()
colnames_demographic_features = snakemake.params["colnames_demographic_features"]
data = pd.read_csv(snakemake.input[0])
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
if "pid" in data.columns:
index_columns.append("pid")
data.set_index(index_columns, inplace=True)
data_x, data_y = data.drop("target", axis=1), data[["target"]]
targets_value_counts = data_y["target"].value_counts()
baseline_metrics = pd.DataFrame(columns=["method", "fullMethodName", "accuracy", "precision0", "recall0", "f10", "precision1", "recall1", "f11", "f1_macro", "auc", "kappa"])
if len(targets_value_counts) < 2:
fout = open(snakemake.log[0], "w")
fout.write(targets_value_counts.to_string())
fout.close()
else:
if min(targets_value_counts) >= 6:
oversampler_type = "SMOTE"
else:
oversampler_type = "RandomOverSampler"
# Baseline 1: majority class classifier => predict every sample as majority class
baseline1_metrics, majority_class = baselineAccuracyOfMajorityClassClassifier(data_y)
majority_ratio = baseline1_metrics["accuracy"]
# Baseline 2: random weighted classifier => random classifier with binomial distribution
baseline2_metrics = baselineMetricsOfRandomWeightedClassifier(data_y, majority_ratio, majority_class, 1000)
if "pid" in index_columns:
# Baseline 3: decision tree with demographic features
baseline3_metrics = baselineMetricsOfDTWithDemographicFeatures(cv_method, data_x[colnames_demographic_features], data_y, oversampler_type)
baselines = [baseline1_metrics, baseline2_metrics, baseline3_metrics]
methods = ["majority", "rwc", "dt"]
fullMethodNames = ["MajorityClassClassifier", "RandomWeightedClassifier", "DecisionTreeWithDemographicFeatures"]
else:
# Only have 2 baselines
baselines = [baseline1_metrics, baseline2_metrics]
methods = ["majority", "rwc"]
fullMethodNames = ["MajorityClassClassifier", "RandomWeightedClassifier"]
baseline_metrics = pd.DataFrame({"method": methods,
"fullMethodName": fullMethodNames,
"accuracy": [baseline["accuracy"] for baseline in baselines],
"precision0": [baseline["precision0"] for baseline in baselines],
"recall0": [baseline["recall0"] for baseline in baselines],
"f10": [baseline["f10"] for baseline in baselines],
"precision1": [baseline["precision1"] for baseline in baselines],
"recall1": [baseline["recall1"] for baseline in baselines],
"f11": [baseline["f11"] for baseline in baselines],
"f1_macro": [baseline["f1_macro"] for baseline in baselines],
"auc": [baseline["auc"] for baseline in baselines],
"kappa": [baseline["kappa"] for baseline in baselines]})
baseline_metrics.to_csv(snakemake.output[0], index=False)

164
src/models/modelling_utils.py

@ -1,164 +0,0 @@
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import cohen_kappa_score, roc_auc_score
from imblearn.pipeline import Pipeline
from imblearn.over_sampling import SMOTE, RandomOverSampler
def getMatchingColNames(operators, features):
col_names = []
for col in features.columns:
if any(operator in col for operator in operators):
col_names.append(col)
return col_names
# drop columns with zero variance
def dropZeroVarianceCols(data):
if not data.empty:
var_df = data.var()
keep_col = []
for col in var_df.index:
if var_df.loc[col] > 0:
keep_col.append(col)
data_drop_cols_var = data.loc[:, keep_col]
else:
data_drop_cols_var = data
return data_drop_cols_var
# normalize based on all participants: return fitted scaler
def getNormAllParticipantsScaler(features, scaler_flag):
# MinMaxScaler
if scaler_flag == "minmaxscaler":
scaler = MinMaxScaler()
# StandardScaler
elif scaler_flag == "standardscaler":
scaler = StandardScaler()
# RobustScaler
elif scaler_flag == "robustscaler":
scaler = RobustScaler()
else:
# throw exception
raise ValueError("The normalization method is not predefined, please check if the PARAMS_FOR_ANALYSIS.NORMALIZED in config.yaml file is correct.")
scaler.fit(features)
return scaler
# get metrics: accuracy, precision0, recall0, f10, precision1, recall1, f11, f1_macro, auc, kappa
def getMetrics(pred_y, pred_y_proba, true_y):
metrics = {}
count = len(np.unique(true_y))
label= np.unique(true_y)[0]
# metrics for all categories
metrics["accuracy"] = accuracy_score(true_y, pred_y)
metrics["f1_macro"] = f1_score(true_y, pred_y, average="macro") # unweighted mean
metrics["auc"] = np.nan if count == 1 else roc_auc_score(true_y, pred_y_proba)
metrics["kappa"] = cohen_kappa_score(true_y, pred_y)
# metrics for label 0
metrics["precision0"] = np.nan if (count == 1 and label == 1) else precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[0]
metrics["recall0"] = np.nan if (count == 1 and label == 1) else recall_score(true_y, pred_y, average=None, labels=[0,1])[0]
metrics["f10"] = np.nan if (count == 1 and label == 1) else f1_score(true_y, pred_y, average=None, labels=[0,1])[0]
# metrics for label 1
metrics["precision1"] = np.nan if (count == 1 and label == 0) else precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[1]
metrics["recall1"] = np.nan if (count == 1 and label == 0) else recall_score(true_y, pred_y, average=None, labels=[0,1])[1]
metrics["f11"] = np.nan if (count == 1 and label == 0) else f1_score(true_y, pred_y, average=None, labels=[0,1])[1]
return metrics
# get feature importances
def getFeatureImportances(model, clf, cols):
if model == "LogReg":
# Extract the coefficient of the features in the decision function
# Calculate the absolute value
# Normalize it to sum 1
feature_importances = pd.DataFrame(zip(clf.coef_[0],cols), columns=["Value", "Feature"])
feature_importances["Value"] = feature_importances["Value"].abs()/feature_importances["Value"].abs().sum()
elif model == "kNN":
# Feature importance is not defined for the KNN Classification, return an empty dataframe
feature_importances = pd.DataFrame(columns=["Value", "Feature"])
elif model == "SVM":
# Coefficient of the features are only available for linear kernel
try:
# For linear kernel
# Extract the coefficient of the features in the decision function
# Calculate the absolute value
# Normalize it to sum 1
feature_importances = pd.DataFrame(zip(clf.coef_[0],cols), columns=["Value", "Feature"])
feature_importances["Value"] = feature_importances["Value"].abs()/feature_importances["Value"].abs().sum()
except:
# For nonlinear kernel, return an empty dataframe directly
feature_importances = pd.DataFrame(columns=["Value", "Feature"])
elif model == "LightGBM":
# Extract feature_importances_ and normalize it to sum 1
feature_importances = pd.DataFrame(zip(clf.feature_importances_,cols), columns=["Value", "Feature"])
feature_importances["Value"] = feature_importances["Value"]/feature_importances["Value"].sum()
else:
# For DT, RF, GB, XGBoost classifier, extract feature_importances_. This field has already been normalized.
feature_importances = pd.DataFrame(zip(clf.feature_importances_,cols), columns=["Value", "Feature"])
feature_importances = feature_importances.set_index(["Feature"]).T
return feature_importances
def createPipeline(model, oversampler_type):
if oversampler_type == "SMOTE":
oversampler = SMOTE(sampling_strategy="minority", random_state=0)
elif oversampler_type == "RandomOverSampler":
oversampler = RandomOverSampler(sampling_strategy="minority", random_state=0)
else:
raise ValueError("RAPIDS pipeline only support 'SMOTE' and 'RandomOverSampler' oversampling methods.")
if model == "LogReg":
from sklearn.linear_model import LogisticRegression
pipeline = Pipeline([
("sampling", oversampler),
("clf", LogisticRegression(random_state=0))
])
elif model == "kNN":
from sklearn.neighbors import KNeighborsClassifier
pipeline = Pipeline([
("sampling", oversampler),
("clf", KNeighborsClassifier())
])
elif model == "SVM":
from sklearn.svm import SVC
pipeline = Pipeline([
("sampling", oversampler),
("clf", SVC(random_state=0, probability=True))
])
elif model == "DT":
from sklearn.tree import DecisionTreeClassifier
pipeline = Pipeline([
("sampling", oversampler),
("clf", DecisionTreeClassifier(random_state=0))
])
elif model == "RF":
from sklearn.ensemble import RandomForestClassifier
pipeline = Pipeline([
("sampling", oversampler),
("clf", RandomForestClassifier(random_state=0))
])
elif model == "GB":
from sklearn.ensemble import GradientBoostingClassifier
pipeline = Pipeline([
("sampling", oversampler),
("clf", GradientBoostingClassifier(random_state=0))
])
elif model == "XGBoost":
from xgboost import XGBClassifier
pipeline = Pipeline([
("sampling", oversampler),
("clf", XGBClassifier(random_state=0, n_jobs=36))
])
elif model == "LightGBM":
from lightgbm import LGBMClassifier
pipeline = Pipeline([
("sampling", oversampler),
("clf", LGBMClassifier(random_state=0, n_jobs=36))
])
else:
raise ValueError("RAPIDS pipeline only support LogReg, kNN, SVM, DT, RF, GB, XGBoost, and LightGBM algorithms for classification problems.")
return pipeline
Loading…
Cancel
Save