Return scores for classification.
junos
parent
f58d20ffc2
commit
055e87dbac
|
|
@ -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()
|
||||
|
|
@ -74,20 +62,19 @@ model_input[clust_col].describe()
|
|||
# %% jupyter={"source_hidden": true}
|
||||
|
||||
# Filter-out outlier rows by clust_col
|
||||
#model_input = model_input[(np.abs(stats.zscore(model_input[clust_col])) < 3)]
|
||||
# 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)
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
Loading…
Reference in New Issue