175 lines
6.8 KiB
Python
175 lines
6.8 KiB
Python
# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,py:percent
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# text_representation:
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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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# kernelspec:
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# display_name: straw2analysis
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# language: python
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# name: straw2analysis
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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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import numpy as np
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import matplotlib.pyplot as plt
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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 LeaveOneGroupOut, cross_validate, StratifiedKFold
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from sklearn.impute import SimpleImputer
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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 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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# ## Set script's parameters
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n_clusters = 3 # Number of clusters (could be regarded as a hyperparameter)
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cv_method_str = 'half_logo' # logo, half_logo, 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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# %% 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 = 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'
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clust_col = lime_col
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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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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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np.unique(km, return_counts=True)
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uniq['cluster'] = km
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uniq
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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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# %% jupyter={"source_hidden": true}
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# Create dict with classification ml models
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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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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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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["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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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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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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# 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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categorical_feature_colnames += additional_categorical_features
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categorical_features = data_x[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 = data_x.drop(categorical_feature_colnames, axis=1)
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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 = LeaveOneGroupOut()
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cv_method.get_n_splits(
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train_x,
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data_y,
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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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for model_title, model in cmodels.items():
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classifier = cross_validate(
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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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)
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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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# %% 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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