Handle clustering classification the same as other classification models.
junos
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a2401b5e36
commit
c51e0da0f7
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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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@ -14,92 +14,83 @@
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# ---
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# %% jupyter={"source_hidden": true}
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# %matplotlib inline
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import os
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import sys
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from pathlib import Path
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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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from scipy import stats
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from sklearn.model_selection import train_test_split
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from sklearn.impute import SimpleImputer
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
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from sklearn.cluster import KMeans
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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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from sklearn.impute import SimpleImputer
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
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from sklearn.model_selection import train_test_split
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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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# # Useful method
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def treat_categorical_features(input_set):
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categorical_feature_colnames = ["gender", "startlanguage"]
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additional_categorical_features = [col for col in input_set.columns if "mostcommonactivity" in col or "homelabel" in col]
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categorical_feature_colnames += additional_categorical_features
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categorical_features = input_set[categorical_feature_colnames].copy()
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mode_categorical_features = categorical_features.mode().iloc[0]
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# fillna with mode
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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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if not categorical_features.empty:
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categorical_features = pd.get_dummies(categorical_features)
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numerical_features = input_set.drop(categorical_feature_colnames, axis=1)
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return pd.concat([numerical_features, categorical_features], axis=1)
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from machine_learning.helper import impute_encode_categorical_features
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# %% [markdown]
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# ## Set script's parameters
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n_clusters = 3 # Number of clusters (could be regarded as a hyperparameter)
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n_sl = 3 # Number of largest/smallest accuracies (of particular CV) outputs
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#
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# %%
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n_clusters = 3 # Number of clusters (could be regarded as a hyperparameter)
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n_sl = 3 # Number of largest/smallest accuracies (of particular CV) outputs
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# %%
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PATH_BASE = Path("E:/STRAWresults/20230415")
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SEGMENT_TYPE = "period"
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print("SEGMENT_TYPE: " + SEGMENT_TYPE)
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SEGMENT_LENGTH = "30_minutes_before"
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print("SEGMENT_LENGTH: " + SEGMENT_LENGTH)
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TARGET_VARIABLE = "appraisal_stressfulness"
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print("TARGET_VARIABLE: " + TARGET_VARIABLE)
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if ("appraisal" in TARGET_VARIABLE) and ("stressfulness" in TARGET_VARIABLE):
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TARGET_VARIABLE += "_"
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TARGET_VARIABLE += SEGMENT_TYPE
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PATH_FULL = PATH_BASE / SEGMENT_LENGTH / ("input_" + TARGET_VARIABLE + "_mean.csv")
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model_input = pd.read_csv(PATH_FULL)
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if SEGMENT_LENGTH == "daily":
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DAY_LENGTH = "daily" # or "working"
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print(DAY_LENGTH)
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model_input = model_input[model_input["local_segment"].str.contains(DAY_LENGTH)]
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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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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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CLUST_COL = "limesurvey_demand_control_ratio"
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print("CLUST_COL: " + CLUST_COL)
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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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BINS = [-1, 0, 4]
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print("BINS: " + str(BINS))
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model_input.columns[list(model_input.columns).index('age'):-1]
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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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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'
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clust_col = lime_col
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model_input[clust_col].describe()
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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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plt.bar(uniq['pid'], uniq[clust_col])
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uniq = model_input[[CLUST_COL, "pid"]].drop_duplicates().reset_index(drop=True)
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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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print(uniq)
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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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@ -109,50 +100,64 @@ model_input.set_index(index_columns, inplace=True)
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cm = ClassificationModels()
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cmodels = cm.get_cmodels()
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# %%
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model_input["target"].value_counts()
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# %% jupyter={"source_hidden": true}
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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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# Takes 10th percentile and above 90th percentile as the test set -> the rest for the training set. Only two classes, seperated by z-score of 0.
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model_input_subset['numerical_target'] = model_input_subset['target']
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bins = [-10, 0, 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=[0, 1], right=True)
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p15 = np.percentile(model_input_subset['numerical_target'], 15)
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p85 = np.percentile(model_input_subset['numerical_target'], 85)
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# Treat categorical features
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model_input_subset = treat_categorical_features(model_input_subset)
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# Split to train, validate, and test subsets
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train_set = model_input_subset[(model_input_subset['numerical_target'] > p15) & (model_input_subset['numerical_target'] < p85)].drop(['numerical_target'], axis=1)
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test_set = model_input_subset[(model_input_subset['numerical_target'] <= p15) | (model_input_subset['numerical_target'] >= p85)].drop(['numerical_target'], axis=1)
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train_set['target'].value_counts()
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test_set['target'].value_counts()
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# Takes 10th percentile and above 90th percentile as the test set -> the rest for the training set. Only two classes, seperated by z-score of 0.
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# model_input_subset['numerical_target'] = model_input_subset['target']
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model_input_subset.loc[:, "target"] = pd.cut(
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model_input_subset.loc[:, "target"], bins=BINS, labels=[0, 1], right=True
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)
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# p15 = np.percentile(model_input_subset['numerical_target'], 15)
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# p85 = np.percentile(model_input_subset['numerical_target'], 85)
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# Treat categorical features
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model_input_subset = impute_encode_categorical_features(model_input_subset)
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# Split to train, validate, and test subsets
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# train_set = model_input_subset[(model_input_subset['numerical_target'] > p15) & (model_input_subset['numerical_target'] < p85)].drop(['numerical_target'], axis=1)
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# test_set = model_input_subset[(model_input_subset['numerical_target'] <= p15) | (model_input_subset['numerical_target'] >= p85)].drop(['numerical_target'], axis=1)
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train_set, test_set = train_test_split(
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model_input_subset,
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test_size=0.3,
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stratify=model_input_subset["pid"],
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random_state=42,
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)
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print(train_set["target"].value_counts())
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print(test_set["target"].value_counts())
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train_x, train_y = train_set.drop(["target", "pid"], axis=1), train_set["target"]
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validate_x, test_x, validate_y, test_y = \
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train_test_split(test_set.drop(["target", "pid"], axis=1), test_set["target"], test_size=0.50, random_state=42)
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validate_x, test_x, validate_y, test_y = train_test_split(
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test_set.drop(["target", "pid"], axis=1),
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test_set["target"],
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test_size=0.50,
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random_state=42,
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)
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# Impute missing values
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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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train_x = imputer.fit_transform(train_x)
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validate_x = imputer.fit_transform(validate_x)
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test_x = imputer.fit_transform(test_x)
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for model_title, model in cmodels.items():
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model['model'].fit(train_x, train_y)
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y_pred = model['model'].predict(validate_x)
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model["model"].fit(train_x, train_y)
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y_pred = model["model"].predict(validate_x)
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acc = accuracy_score(validate_y, y_pred)
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prec = precision_score(validate_y, y_pred)
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rec = recall_score(validate_y, y_pred)
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f1 = f1_score(validate_y, y_pred)
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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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@ -160,12 +165,30 @@ for k in range(n_clusters):
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print("Precision", prec)
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print("Recall", rec)
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print("F1", f1)
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cmodels[model_title]['metrics'][0] += acc
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cmodels[model_title]['metrics'][1] += prec
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cmodels[model_title]['metrics'][2] += rec
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cmodels[model_title]['metrics'][3] += f1
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cmodels[model_title]["metrics"][0] += acc
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cmodels[model_title]["metrics"][1] += prec
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cmodels[model_title]["metrics"][2] += rec
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cmodels[model_title]["metrics"][3] += 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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# %%
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print(scores)
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# %%
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PATH_OUTPUT = Path("..") / Path("presentation/results")
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path_output_full = PATH_OUTPUT / (
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TARGET_VARIABLE
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+ "_"
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+ SEGMENT_LENGTH
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+ "_classification"
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+ str(BINS)
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+ "_CLUST_"
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+ CLUST_COL
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+ +str(n_clusters)
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+ ".csv"
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)
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scores.to_csv(path_output_full, index=False)
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