Return scores for classification.

master
junos 2023-05-10 23:49:56 +02:00
parent f58d20ffc2
commit 055e87dbac
2 changed files with 172 additions and 126 deletions

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@ -6,7 +6,7 @@
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.0
# jupytext_version: 1.14.5
# kernelspec:
# display_name: straw2analysis
# language: python
@ -15,57 +15,45 @@
# %% jupyter={"source_hidden": true}
# %matplotlib inline
import datetime
import importlib
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
from sklearn.model_selection import LeaveOneGroupOut, cross_validate
from sklearn.impute import SimpleImputer
from sklearn.dummy import DummyClassifier
from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
import xgboost as xg
from sklearn.cluster import KMeans
from sklearn.impute import SimpleImputer
from sklearn.model_selection import LeaveOneGroupOut, StratifiedKFold, cross_validate
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
from machine_learning.classification_models import ClassificationModels
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
import machine_learning.labels
import machine_learning.model
from machine_learning.classification_models import ClassificationModels
# %% [markdown]
# # RAPIDS models
# %% [markdown]
# %%
# ## Set script's parameters
n_clusters = 4 # Number of clusters (could be regarded as a hyperparameter)
cv_method_str = 'logo' # logo, halflogo, 5kfold # Cross-validation method (could be regarded as a hyperparameter)
n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs
N_CLUSTERS = 4 # Number of clusters (could be regarded as a hyperparameter)
CV_METHOD = "logo" # logo, halflogo, 5kfold
# Cross-validation method (could be regarded as a hyperparameter)
N_SL = 1 # Number of largest/smallest accuracies (of particular CV) outputs
# %% jupyter={"source_hidden": true}
model_input = pd.read_csv("../data/30min_all_target_inputs/input_JCQ_job_demand_mean.csv")
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
model_input = pd.read_csv(
"E:/STRAWresults/20230415/30_minutes_before/input_PANAS_negative_affect_mean.csv"
)
index_columns = [
"local_segment",
"local_segment_label",
"local_segment_start_datetime",
"local_segment_end_datetime",
]
clust_col = model_input.set_index(index_columns).var().idxmax() # age is a col with the highest variance
model_input.columns[list(model_input.columns).index('age'):-1]
lime_cols = [col for col in model_input if col.startswith('limesurvey')]
lime_cols
lime_col = 'limesurvey_demand_control_ratio_quartile'
lime_col = "limesurvey_demand_control_ratio_quartile"
clust_col = lime_col
model_input[clust_col].describe()
@ -76,18 +64,17 @@ model_input[clust_col].describe()
# Filter-out outlier rows by clust_col
# model_input = model_input[(np.abs(stats.zscore(model_input[clust_col])) < 3)]
uniq = model_input[[clust_col, 'pid']].drop_duplicates().reset_index(drop=True)
uniq = model_input[[clust_col, "pid"]].drop_duplicates().reset_index(drop=True)
uniq = uniq.dropna()
plt.bar(uniq['pid'], uniq[clust_col])
plt.bar(uniq["pid"], uniq[clust_col])
# %% jupyter={"source_hidden": true}
# Get clusters by cluster col & and merge the clusters to main df
km = KMeans(n_clusters=n_clusters).fit_predict(uniq.set_index('pid'))
km = KMeans(n_clusters=N_CLUSTERS).fit_predict(uniq.set_index("pid"))
np.unique(km, return_counts=True)
uniq['cluster'] = km
uniq
uniq["cluster"] = km
model_input = model_input.merge(uniq[['pid', 'cluster']])
model_input = model_input.merge(uniq[["pid", "cluster"]])
# %% jupyter={"source_hidden": true}
model_input.set_index(index_columns, inplace=True)
@ -98,31 +85,57 @@ cm = ClassificationModels()
cmodels = cm.get_cmodels()
# %% jupyter={"source_hidden": true}
for k in range(n_clusters):
for k in range(N_CLUSTERS):
model_input_subset = model_input[model_input["cluster"] == k].copy()
bins = [-10, -1, 1, 10] # bins for z-scored targets
model_input_subset.loc[:, 'target'] = \
pd.cut(model_input_subset.loc[:, 'target'], bins=bins, labels=['low', 'medium', 'high'], right=False) #['low', 'medium', 'high']
model_input_subset['target'].value_counts()
model_input_subset = model_input_subset[model_input_subset['target'] != "medium"]
model_input_subset['target'] = model_input_subset['target'].astype(str).apply(lambda x: 0 if x == "low" else 1)
model_input_subset.loc[:, "target"] = pd.cut(
model_input_subset.loc[:, "target"],
bins=bins,
labels=["low", "medium", "high"],
right=False,
) # ['low', 'medium', 'high']
model_input_subset["target"].value_counts()
model_input_subset = model_input_subset[model_input_subset["target"] != "medium"]
model_input_subset["target"] = (
model_input_subset["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
)
model_input_subset['target'].value_counts()
model_input_subset["target"].value_counts()
if cv_method_str == 'half_logo':
model_input_subset['pid_index'] = model_input_subset.groupby('pid').cumcount()
model_input_subset['pid_count'] = model_input_subset.groupby('pid')['pid'].transform('count')
if CV_METHOD == "half_logo":
model_input_subset["pid_index"] = model_input_subset.groupby("pid").cumcount()
model_input_subset["pid_count"] = model_input_subset.groupby("pid")[
"pid"
].transform("count")
model_input_subset["pid_index"] = (model_input_subset['pid_index'] / model_input_subset['pid_count'] + 1).round()
model_input_subset["pid_half"] = model_input_subset["pid"] + "_" + model_input_subset["pid_index"].astype(int).astype(str)
model_input_subset["pid_index"] = (
model_input_subset["pid_index"] / model_input_subset["pid_count"] + 1
).round()
model_input_subset["pid_half"] = (
model_input_subset["pid"]
+ "_"
+ model_input_subset["pid_index"].astype(int).astype(str)
)
data_x, data_y, data_groups = model_input_subset.drop(["target", "pid", "pid_index", "pid_half"], axis=1), model_input_subset["target"], model_input_subset["pid_half"]
data_x, data_y, data_groups = (
model_input_subset.drop(["target", "pid", "pid_index", "pid_half"], axis=1),
model_input_subset["target"],
model_input_subset["pid_half"],
)
else:
data_x, data_y, data_groups = model_input_subset.drop(["target", "pid"], axis=1), model_input_subset["target"], model_input_subset["pid"]
data_x, data_y, data_groups = (
model_input_subset.drop(["target", "pid"], axis=1),
model_input_subset["target"],
model_input_subset["pid"],
)
# Treat categorical features
categorical_feature_colnames = ["gender", "startlanguage"]
additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
additional_categorical_features = [
col
for col in data_x.columns
if "mostcommonactivity" in col or "homelabel" in col
]
categorical_feature_colnames += additional_categorical_features
categorical_features = data_x[categorical_feature_colnames].copy()
@ -132,7 +145,9 @@ for k in range(n_clusters):
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)
@ -140,8 +155,10 @@ for k in range(n_clusters):
train_x = pd.concat([numerical_features, categorical_features], axis=1)
# Establish cv method
cv_method = StratifiedKFold(n_splits=5, shuffle=True) # Defaults to 5 k-folds in cross_validate method
if cv_method_str == 'logo' or cv_method_str == 'half_logo':
cv_method = StratifiedKFold(
n_splits=5, shuffle=True
) # Defaults to 5 k-folds in cross_validate method
if CV_METHOD == "logo" or CV_METHOD == "half_logo":
cv_method = LeaveOneGroupOut()
cv_method.get_n_splits(
train_x,
@ -149,36 +166,41 @@ for k in range(n_clusters):
groups=data_groups,
)
imputer = SimpleImputer(missing_values=np.nan, strategy='median')
imputer = SimpleImputer(missing_values=np.nan, strategy="median")
for model_title, model in cmodels.items():
classifier = cross_validate(
model['model'],
model["model"],
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
error_score='raise',
scoring=('accuracy', 'precision', 'recall', 'f1')
error_score="raise",
scoring=("accuracy", "precision", "recall", "f1"),
)
print("\n-------------------------------------\n")
print("Current cluster:", k, end="\n")
print("Current model:", model_title, end="\n")
print("Acc", np.mean(classifier['test_accuracy']))
print("Precision", np.mean(classifier['test_precision']))
print("Recall", np.mean(classifier['test_recall']))
print("F1", np.mean(classifier['test_f1']))
print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-classifier['test_accuracy'], n_sl)[:n_sl])[::-1])
print(f"Smallest {n_sl} ACC:", np.sort(np.partition(classifier['test_accuracy'], n_sl)[:n_sl]))
print("Acc", np.mean(classifier["test_accuracy"]))
print("Precision", np.mean(classifier["test_precision"]))
print("Recall", np.mean(classifier["test_recall"]))
print("F1", np.mean(classifier["test_f1"]))
print(
f"Largest {N_SL} ACC:",
np.sort(-np.partition(-classifier["test_accuracy"], N_SL)[:N_SL])[::-1],
)
print(
f"Smallest {N_SL} ACC:",
np.sort(np.partition(classifier["test_accuracy"], N_SL)[:N_SL]),
)
cmodels[model_title]['metrics'][0] += np.mean(classifier['test_accuracy'])
cmodels[model_title]['metrics'][1] += np.mean(classifier['test_precision'])
cmodels[model_title]['metrics'][2] += np.mean(classifier['test_recall'])
cmodels[model_title]['metrics'][3] += np.mean(classifier['test_f1'])
cmodels[model_title]["metrics"][0] += np.mean(classifier["test_accuracy"])
cmodels[model_title]["metrics"][1] += np.mean(classifier["test_precision"])
cmodels[model_title]["metrics"][2] += np.mean(classifier["test_recall"])
cmodels[model_title]["metrics"][3] += np.mean(classifier["test_f1"])
# %% jupyter={"source_hidden": true}
# Get overall results
cm.get_total_models_scores(n_clusters=n_clusters)
scores = cm.get_total_models_scores(n_clusters=N_CLUSTERS)

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@ -1,10 +1,11 @@
from sklearn.dummy import DummyClassifier
from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
from lightgbm import LGBMClassifier
import pandas as pd
import xgboost as xg
from lightgbm import LGBMClassifier
from sklearn import ensemble, linear_model, naive_bayes, neighbors, svm, tree
from sklearn.dummy import DummyClassifier
class ClassificationModels():
class ClassificationModels:
def __init__(self):
self.cmodels = self.init_classification_models()
@ -13,59 +14,82 @@ class ClassificationModels():
def init_classification_models(self):
cmodels = {
'dummy_classifier': {
'model': DummyClassifier(strategy="most_frequent"),
'metrics': [0, 0, 0, 0]
"dummy_classifier": {
"model": DummyClassifier(strategy="most_frequent"),
"metrics": [0, 0, 0, 0],
},
'logistic_regression': {
'model': linear_model.LogisticRegression(max_iter=1000),
'metrics': [0, 0, 0, 0]
"logistic_regression": {
"model": linear_model.LogisticRegression(max_iter=1000),
"metrics": [0, 0, 0, 0],
},
'support_vector_machine': {
'model': svm.SVC(),
'metrics': [0, 0, 0, 0]
"support_vector_machine": {"model": svm.SVC(), "metrics": [0, 0, 0, 0]},
"gaussian_naive_bayes": {
"model": naive_bayes.GaussianNB(),
"metrics": [0, 0, 0, 0],
},
'gaussian_naive_bayes': {
'model': naive_bayes.GaussianNB(),
'metrics': [0, 0, 0, 0]
"stochastic_gradient_descent_classifier": {
"model": linear_model.SGDClassifier(),
"metrics": [0, 0, 0, 0],
},
'stochastic_gradient_descent_classifier': {
'model': linear_model.SGDClassifier(),
'metrics': [0, 0, 0, 0]
"knn": {"model": neighbors.KNeighborsClassifier(), "metrics": [0, 0, 0, 0]},
"decision_tree": {
"model": tree.DecisionTreeClassifier(),
"metrics": [0, 0, 0, 0],
},
'knn': {
'model': neighbors.KNeighborsClassifier(),
'metrics': [0, 0, 0, 0]
"random_forest_classifier": {
"model": ensemble.RandomForestClassifier(),
"metrics": [0, 0, 0, 0],
},
'decision_tree': {
'model': tree.DecisionTreeClassifier(),
'metrics': [0, 0, 0, 0]
"gradient_boosting_classifier": {
"model": ensemble.GradientBoostingClassifier(),
"metrics": [0, 0, 0, 0],
},
'random_forest_classifier': {
'model': ensemble.RandomForestClassifier(),
'metrics': [0, 0, 0, 0]
"lgbm_classifier": {"model": LGBMClassifier(), "metrics": [0, 0, 0, 0]},
"XGBoost_classifier": {
"model": xg.sklearn.XGBClassifier(),
"metrics": [0, 0, 0, 0],
},
'gradient_boosting_classifier': {
'model': ensemble.GradientBoostingClassifier(),
'metrics': [0, 0, 0, 0]
},
'lgbm_classifier': {
'model': LGBMClassifier(),
'metrics': [0, 0, 0, 0]
},
'XGBoost_classifier': {
'model': xg.sklearn.XGBClassifier(),
'metrics': [0, 0, 0, 0]
}
}
return cmodels
def get_total_models_scores(self, n_clusters=1):
scores = pd.DataFrame(columns=["method", "metric", "mean"])
for model_title, model in self.cmodels.items():
scores_df = pd.DataFrame(columns=["method", "metric", "mean"])
print("\n************************************\n")
print("Current model:", model_title, end="\n")
print("Acc:", model['metrics'][0]/n_clusters)
print("Precision:", model['metrics'][1]/n_clusters)
print("Recall:", model['metrics'][2]/n_clusters)
print("F1:", model['metrics'][3]/n_clusters)
print("Acc:", model["metrics"][0] / n_clusters)
scores_df.append(
{
"method": model_title,
"metric": "test_accuracy",
"mean": model["metrics"][0] / n_clusters,
}
)
print("Precision:", model["metrics"][1] / n_clusters)
scores_df.append(
{
"method": model_title,
"metric": "test_precision",
"mean": model["metrics"][1] / n_clusters,
}
)
print("Recall:", model["metrics"][2] / n_clusters)
scores_df.append(
{
"method": model_title,
"metric": "test_recall",
"mean": model["metrics"][2] / n_clusters,
}
)
print("F1:", model["metrics"][3] / n_clusters)
scores_df.append(
{
"method": model_title,
"metric": "test_f1",
"mean": model["metrics"][3] / n_clusters,
}
)
scores = pd.concat([scores, scores_df])
return scores