Return scores for classification.
junos
parent
f58d20ffc2
commit
055e87dbac
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@ -6,7 +6,7 @@
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.13.0
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# jupytext_version: 1.14.5
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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@ -15,57 +15,45 @@
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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 numpy as np
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import pandas as pd
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import seaborn as sns
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from scipy import stats
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from sklearn.model_selection import LeaveOneGroupOut, cross_validate
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from sklearn.impute import SimpleImputer
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from sklearn.dummy import DummyClassifier
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from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
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import xgboost as xg
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from sklearn.cluster import KMeans
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from sklearn.impute import SimpleImputer
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from sklearn.model_selection import LeaveOneGroupOut, StratifiedKFold, 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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from machine_learning.classification_models import ClassificationModels
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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.labels
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import machine_learning.model
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from machine_learning.classification_models import ClassificationModels
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# %% [markdown]
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# # RAPIDS models
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# %% [markdown]
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# %%
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# ## Set script's parameters
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n_clusters = 4 # Number of clusters (could be regarded as a hyperparameter)
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cv_method_str = 'logo' # logo, halflogo, 5kfold # Cross-validation method (could be regarded as a hyperparameter)
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n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs
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N_CLUSTERS = 4 # Number of clusters (could be regarded as a hyperparameter)
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CV_METHOD = "logo" # logo, halflogo, 5kfold
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# Cross-validation method (could be regarded as a hyperparameter)
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N_SL = 1 # Number of largest/smallest accuracies (of particular CV) outputs
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# %% jupyter={"source_hidden": true}
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model_input = pd.read_csv("../data/30min_all_target_inputs/input_JCQ_job_demand_mean.csv")
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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model_input = pd.read_csv(
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"E:/STRAWresults/20230415/30_minutes_before/input_PANAS_negative_affect_mean.csv"
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)
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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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clust_col = model_input.set_index(index_columns).var().idxmax() # age is a col with the highest variance
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model_input.columns[list(model_input.columns).index('age'):-1]
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lime_cols = [col for col in model_input if col.startswith('limesurvey')]
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lime_cols
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lime_col = 'limesurvey_demand_control_ratio_quartile'
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lime_col = "limesurvey_demand_control_ratio_quartile"
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clust_col = lime_col
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model_input[clust_col].describe()
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@ -73,21 +61,20 @@ model_input[clust_col].describe()
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# %% jupyter={"source_hidden": true}
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# Filter-out outlier rows by clust_col
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#model_input = model_input[(np.abs(stats.zscore(model_input[clust_col])) < 3)]
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# Filter-out outlier rows by clust_col
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# model_input = model_input[(np.abs(stats.zscore(model_input[clust_col])) < 3)]
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uniq = model_input[[clust_col, 'pid']].drop_duplicates().reset_index(drop=True)
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uniq = model_input[[clust_col, "pid"]].drop_duplicates().reset_index(drop=True)
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uniq = uniq.dropna()
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plt.bar(uniq['pid'], uniq[clust_col])
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plt.bar(uniq["pid"], uniq[clust_col])
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# %% jupyter={"source_hidden": true}
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# Get clusters by cluster col & and merge the clusters to main df
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km = KMeans(n_clusters=n_clusters).fit_predict(uniq.set_index('pid'))
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km = KMeans(n_clusters=N_CLUSTERS).fit_predict(uniq.set_index("pid"))
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np.unique(km, return_counts=True)
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uniq['cluster'] = km
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uniq
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uniq["cluster"] = km
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model_input = model_input.merge(uniq[['pid', 'cluster']])
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model_input = model_input.merge(uniq[["pid", "cluster"]])
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# %% jupyter={"source_hidden": true}
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model_input.set_index(index_columns, inplace=True)
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@ -98,31 +85,57 @@ cm = ClassificationModels()
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cmodels = cm.get_cmodels()
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# %% jupyter={"source_hidden": true}
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for k in range(n_clusters):
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for k in range(N_CLUSTERS):
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model_input_subset = model_input[model_input["cluster"] == k].copy()
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bins = [-10, -1, 1, 10] # bins for z-scored targets
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model_input_subset.loc[:, 'target'] = \
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pd.cut(model_input_subset.loc[:, 'target'], bins=bins, labels=['low', 'medium', 'high'], right=False) #['low', 'medium', 'high']
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model_input_subset['target'].value_counts()
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model_input_subset = model_input_subset[model_input_subset['target'] != "medium"]
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model_input_subset['target'] = model_input_subset['target'].astype(str).apply(lambda x: 0 if x == "low" else 1)
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bins = [-10, -1, 1, 10] # bins for z-scored targets
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model_input_subset.loc[:, "target"] = pd.cut(
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model_input_subset.loc[:, "target"],
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bins=bins,
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labels=["low", "medium", "high"],
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right=False,
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) # ['low', 'medium', 'high']
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model_input_subset["target"].value_counts()
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model_input_subset = model_input_subset[model_input_subset["target"] != "medium"]
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model_input_subset["target"] = (
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model_input_subset["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
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)
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model_input_subset['target'].value_counts()
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if cv_method_str == 'half_logo':
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model_input_subset['pid_index'] = model_input_subset.groupby('pid').cumcount()
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model_input_subset['pid_count'] = model_input_subset.groupby('pid')['pid'].transform('count')
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model_input_subset["target"].value_counts()
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model_input_subset["pid_index"] = (model_input_subset['pid_index'] / model_input_subset['pid_count'] + 1).round()
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model_input_subset["pid_half"] = model_input_subset["pid"] + "_" + model_input_subset["pid_index"].astype(int).astype(str)
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if CV_METHOD == "half_logo":
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model_input_subset["pid_index"] = model_input_subset.groupby("pid").cumcount()
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model_input_subset["pid_count"] = model_input_subset.groupby("pid")[
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"pid"
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].transform("count")
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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"]
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model_input_subset["pid_index"] = (
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model_input_subset["pid_index"] / model_input_subset["pid_count"] + 1
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).round()
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model_input_subset["pid_half"] = (
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model_input_subset["pid"]
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+ "_"
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+ model_input_subset["pid_index"].astype(int).astype(str)
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)
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data_x, data_y, data_groups = (
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model_input_subset.drop(["target", "pid", "pid_index", "pid_half"], axis=1),
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model_input_subset["target"],
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model_input_subset["pid_half"],
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)
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else:
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data_x, data_y, data_groups = model_input_subset.drop(["target", "pid"], axis=1), model_input_subset["target"], model_input_subset["pid"]
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data_x, data_y, data_groups = (
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model_input_subset.drop(["target", "pid"], axis=1),
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model_input_subset["target"],
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model_input_subset["pid"],
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)
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# Treat categorical features
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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
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for col in data_x.columns
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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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categorical_features = data_x[categorical_feature_colnames].copy()
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@ -132,7 +145,9 @@ for k in range(n_clusters):
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categorical_features = categorical_features.fillna(mode_categorical_features)
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# one-hot encoding
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categorical_features = categorical_features.apply(lambda col: col.astype("category"))
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categorical_features = categorical_features.apply(
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lambda col: col.astype("category")
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)
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if not categorical_features.empty:
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categorical_features = pd.get_dummies(categorical_features)
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@ -140,8 +155,10 @@ for k in range(n_clusters):
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train_x = pd.concat([numerical_features, categorical_features], axis=1)
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# Establish cv method
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cv_method = StratifiedKFold(n_splits=5, shuffle=True) # Defaults to 5 k-folds in cross_validate method
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if cv_method_str == 'logo' or cv_method_str == 'half_logo':
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cv_method = StratifiedKFold(
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n_splits=5, shuffle=True
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) # Defaults to 5 k-folds in cross_validate method
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if CV_METHOD == "logo" or CV_METHOD == "half_logo":
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cv_method = LeaveOneGroupOut()
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cv_method.get_n_splits(
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train_x,
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@ -149,36 +166,41 @@ for k in range(n_clusters):
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groups=data_groups,
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)
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imputer = SimpleImputer(missing_values=np.nan, strategy='median')
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imputer = SimpleImputer(missing_values=np.nan, strategy="median")
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for model_title, model in cmodels.items():
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classifier = cross_validate(
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model['model'],
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model["model"],
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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scoring=('accuracy', 'precision', 'recall', 'f1')
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error_score="raise",
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scoring=("accuracy", "precision", "recall", "f1"),
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)
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print("\n-------------------------------------\n")
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print("Current cluster:", k, end="\n")
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print("Current model:", model_title, end="\n")
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print("Acc", np.mean(classifier['test_accuracy']))
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print("Precision", np.mean(classifier['test_precision']))
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print("Recall", np.mean(classifier['test_recall']))
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print("F1", np.mean(classifier['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-classifier['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(classifier['test_accuracy'], n_sl)[:n_sl]))
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cmodels[model_title]['metrics'][0] += np.mean(classifier['test_accuracy'])
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cmodels[model_title]['metrics'][1] += np.mean(classifier['test_precision'])
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cmodels[model_title]['metrics'][2] += np.mean(classifier['test_recall'])
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cmodels[model_title]['metrics'][3] += np.mean(classifier['test_f1'])
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print("Acc", np.mean(classifier["test_accuracy"]))
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print("Precision", np.mean(classifier["test_precision"]))
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print("Recall", np.mean(classifier["test_recall"]))
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print("F1", np.mean(classifier["test_f1"]))
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print(
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f"Largest {N_SL} ACC:",
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np.sort(-np.partition(-classifier["test_accuracy"], N_SL)[:N_SL])[::-1],
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)
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print(
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f"Smallest {N_SL} ACC:",
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np.sort(np.partition(classifier["test_accuracy"], N_SL)[:N_SL]),
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)
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cmodels[model_title]["metrics"][0] += np.mean(classifier["test_accuracy"])
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cmodels[model_title]["metrics"][1] += np.mean(classifier["test_precision"])
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cmodels[model_title]["metrics"][2] += np.mean(classifier["test_recall"])
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cmodels[model_title]["metrics"][3] += np.mean(classifier["test_f1"])
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# %% jupyter={"source_hidden": true}
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# Get overall results
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cm.get_total_models_scores(n_clusters=n_clusters)
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scores = cm.get_total_models_scores(n_clusters=N_CLUSTERS)
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@ -1,71 +1,95 @@
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from sklearn.dummy import DummyClassifier
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from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
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import pandas as pd
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import xgboost as xg
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from lightgbm import LGBMClassifier
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import xgboost as xg
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from sklearn import ensemble, linear_model, naive_bayes, neighbors, svm, tree
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from sklearn.dummy import DummyClassifier
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class ClassificationModels():
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class ClassificationModels:
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def __init__(self):
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self.cmodels = self.init_classification_models()
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def get_cmodels(self):
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return self.cmodels
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def init_classification_models(self):
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cmodels = {
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'dummy_classifier': {
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'model': DummyClassifier(strategy="most_frequent"),
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'metrics': [0, 0, 0, 0]
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"dummy_classifier": {
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"model": DummyClassifier(strategy="most_frequent"),
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"metrics": [0, 0, 0, 0],
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},
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'logistic_regression': {
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'model': linear_model.LogisticRegression(max_iter=1000),
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'metrics': [0, 0, 0, 0]
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"logistic_regression": {
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"model": linear_model.LogisticRegression(max_iter=1000),
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"metrics": [0, 0, 0, 0],
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},
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'support_vector_machine': {
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'model': svm.SVC(),
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'metrics': [0, 0, 0, 0]
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"support_vector_machine": {"model": svm.SVC(), "metrics": [0, 0, 0, 0]},
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"gaussian_naive_bayes": {
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"model": naive_bayes.GaussianNB(),
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"metrics": [0, 0, 0, 0],
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},
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'gaussian_naive_bayes': {
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'model': naive_bayes.GaussianNB(),
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'metrics': [0, 0, 0, 0]
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"stochastic_gradient_descent_classifier": {
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"model": linear_model.SGDClassifier(),
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"metrics": [0, 0, 0, 0],
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},
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'stochastic_gradient_descent_classifier': {
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'model': linear_model.SGDClassifier(),
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'metrics': [0, 0, 0, 0]
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"knn": {"model": neighbors.KNeighborsClassifier(), "metrics": [0, 0, 0, 0]},
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"decision_tree": {
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"model": tree.DecisionTreeClassifier(),
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"metrics": [0, 0, 0, 0],
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},
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'knn': {
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'model': neighbors.KNeighborsClassifier(),
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'metrics': [0, 0, 0, 0]
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"random_forest_classifier": {
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"model": ensemble.RandomForestClassifier(),
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"metrics": [0, 0, 0, 0],
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},
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'decision_tree': {
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'model': tree.DecisionTreeClassifier(),
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'metrics': [0, 0, 0, 0]
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"gradient_boosting_classifier": {
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"model": ensemble.GradientBoostingClassifier(),
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"metrics": [0, 0, 0, 0],
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},
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'random_forest_classifier': {
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'model': ensemble.RandomForestClassifier(),
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'metrics': [0, 0, 0, 0]
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"lgbm_classifier": {"model": LGBMClassifier(), "metrics": [0, 0, 0, 0]},
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"XGBoost_classifier": {
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"model": xg.sklearn.XGBClassifier(),
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"metrics": [0, 0, 0, 0],
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},
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'gradient_boosting_classifier': {
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'model': ensemble.GradientBoostingClassifier(),
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'metrics': [0, 0, 0, 0]
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},
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'lgbm_classifier': {
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'model': LGBMClassifier(),
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'metrics': [0, 0, 0, 0]
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},
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'XGBoost_classifier': {
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'model': xg.sklearn.XGBClassifier(),
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'metrics': [0, 0, 0, 0]
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}
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}
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return cmodels
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def get_total_models_scores(self, n_clusters=1):
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scores = pd.DataFrame(columns=["method", "metric", "mean"])
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for model_title, model in self.cmodels.items():
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scores_df = pd.DataFrame(columns=["method", "metric", "mean"])
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print("\n************************************\n")
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print("Current model:", model_title, end="\n")
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print("Acc:", model['metrics'][0]/n_clusters)
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print("Precision:", model['metrics'][1]/n_clusters)
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print("Recall:", model['metrics'][2]/n_clusters)
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print("F1:", model['metrics'][3]/n_clusters)
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print("Acc:", model["metrics"][0] / n_clusters)
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scores_df.append(
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{
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"method": model_title,
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"metric": "test_accuracy",
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"mean": model["metrics"][0] / n_clusters,
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}
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)
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print("Precision:", model["metrics"][1] / n_clusters)
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scores_df.append(
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{
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"method": model_title,
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"metric": "test_precision",
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"mean": model["metrics"][1] / n_clusters,
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}
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)
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print("Recall:", model["metrics"][2] / n_clusters)
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scores_df.append(
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{
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"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