In [1]:
import sys
sys.path.append('/home/jbourbeau/cr-composition')
print('Added to PYTHONPATH')
Added to PYTHONPATH
In [2]:
import argparse
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn.apionly as sns
from sklearn.metrics import accuracy_score
from sklearn.model_selection import validation_curve, GridSearchCV, cross_val_score, ParameterGrid
import composition as comp
import composition.analysis.plotting as plotting
%matplotlib inline
sns.set_palette('muted')
sns.set_color_codes()
/home/jbourbeau/.local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
In [5]:
df, cut_dict = comp.load_sim(return_cut_dict=True)
selection_mask = np.array([True] * len(df))
standard_cut_keys = ['lap_reco_success', 'lap_zenith', 'num_hits_1_30', 'IT_signal',
'StationDensity', 'max_qfrac_1_30', 'lap_containment', 'energy_range_lap']
for key in standard_cut_keys:
selection_mask *= cut_dict[key]
df = df[selection_mask]
feature_list, feature_labels = comp.get_training_features()
print('training features = {}'.format(feature_list))
X_train, X_test, y_train, y_test, le = comp.get_train_test_sets(
df, feature_list, train_he=True, test_he=True)
print('number training events = ' + str(y_train.shape[0]))
/home/jbourbeau/cr-composition/composition/load_sim.py:109: RuntimeWarning: divide by zero encountered in log10
df['log_NChannels_1_30'] = np.nan_to_num(np.log10(df['NChannels_1_30']))
training features = ['lap_log_energy', 'InIce_log_charge_1_30', 'lap_cos_zenith', 'NChannels_1_30', 'log_s125']
number training events = 109652
In [6]:
X_train[:50, 0], le.inverse_transform(y_train)[:50], le.inverse_transform(y_test)[:50]
Out[6]:
(array([ 7.7417034 , 6.94174106, 6.92239136, 6.7994609 , 6.3592159 ,
7.01689217, 6.20241984, 7.33218183, 6.58432856, 6.30932686,
6.91079565, 6.62044541, 7.78254541, 7.01548118, 7.51115526,
6.81478074, 7.30300632, 6.64523404, 6.5982895 , 7.07346219,
6.34401992, 7.45205403, 7.96788785, 6.86759537, 6.23306825,
6.23598255, 6.4456117 , 6.33864161, 6.84925221, 7.77331187,
7.76428228, 6.35577994, 6.34463349, 7.22775792, 6.39553151,
6.78271183, 6.54772948, 6.30137503, 6.3334442 , 6.74831893,
7.82633281, 6.36567126, 7.97586309, 6.64913243, 6.36961704,
7.03227123, 6.66874439, 6.72509692, 7.3877767 , 6.38695205]),
array(['He', 'Fe', 'Fe', 'Fe', 'P', 'Fe', 'Fe', 'He', 'P', 'P', 'Fe', 'Fe',
'He', 'P', 'Fe', 'Fe', 'P', 'He', 'P', 'P', 'He', 'Fe', 'Fe', 'Fe',
'He', 'He', 'He', 'P', 'He', 'P', 'P', 'P', 'Fe', 'P', 'P', 'He',
'P', 'P', 'P', 'P', 'P', 'He', 'Fe', 'P', 'He', 'P', 'He', 'He',
'P', 'He'], dtype=object),
array(['He', 'He', 'He', 'P', 'Fe', 'He', 'He', 'P', 'He', 'He', 'He',
'He', 'P', 'P', 'P', 'P', 'He', 'Fe', 'He', 'He', 'He', 'He', 'P',
'P', 'He', 'P', 'He', 'He', 'P', 'Fe', 'P', 'P', 'He', 'P', 'P',
'P', 'Fe', 'Fe', 'P', 'P', 'He', 'He', 'He', 'He', 'P', 'P', 'P',
'P', 'He', 'Fe'], dtype=object))
Get error in charge vs. energy distribution
In [8]:
fig, axarr = plt.subplots(3, 3)
for ax, max_depth in zip(axarr.flatten(), np.arange(1, 18, 2)):
pipeline = comp.get_pipeline('RF')
params = {'classifier__max_depth': max_depth}
pipeline.set_params(**params)
pipeline.fit(X_train, y_train)
scaler = pipeline.named_steps['scaler']
clf = pipeline.named_steps['classifier']
print(clf.estimators_)
# param_range = np.arange(1, 21)
# param_grid = {'classifier__max_depth': param_range,
# 'classifier__min_samples_leaf': np.arange(1, 100, 25)}
# gs = GridSearchCV(estimator=pipeline,
# param_grid=param_grid,
# scoring='accuracy',
# cv=5,
# verbose=1,
# n_jobs=20)
# gs = gs.fit(X_train, y_train)
# print('best GS CV score = {}'.format(gs.best_score_))
# print('best GS CV depths = {}'.format(gs.best_params_))
# print('Grid scores on development set:')
# means = gs.cv_results_['mean_test_score']
# stds = gs.cv_results_['std_test_score']
# for mean, std, params in zip(means, stds, gs.cv_results_['params']):
# print("%0.3f (+/-%0.03f) for %r"
# % (mean, std * 2, params))
# pipeline.set_params(**gs.best_params_)
# pipeline.fit(X_train, y_train)
# scaler = pipeline.named_steps['scaler']
# clf = pipeline.named_steps['classifier']
[DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1872583848, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=794921487, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=111352301, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1853453896, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
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max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
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max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
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max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
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max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
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max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=2017055832, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=22531780, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1275684142, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=280403398, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
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max_features='auto', max_leaf_nodes=None,
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max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=414597151, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=780511682, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=546147920, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=2122958388, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=415517934, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=282459084, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=970714162, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=952774788, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=459328218, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1148865855, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=946207951, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=332035633, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1502492455, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
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min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=720795647, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,
max_features='auto', max_leaf_nodes=None,
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min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=415517934, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=282459084, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=970714162, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=952774788, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=459328218, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1148865855, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=946207951, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=332035633, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1502492455, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=720795647, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=2009131950, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1578277512, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=866480562, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=2012937709, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=603042298, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=662568207, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=2074769129, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1258810861, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=22491528, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=700345079, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1661691281, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=485458838, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1261163256, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=2108193274, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=343615349, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=513183305, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=697067193, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=638080873, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=862224750, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1513035516, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1995231706, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=40913342, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=35923, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=933167200, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1672975915, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=2118562592, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1467219482, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1354192370, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=288381832, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1111250508, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1836295934, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1327897738, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1875815208, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1519024162, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1187812668, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=2009368201, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=152918774, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1177565157, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1948808006, splitter='best'), DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=150,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=1280760918, splitter='best')]
In [7]:
clf_name = clf.__class__.__name__
print('=' * 30)
print(clf_name)
test_predictions = pipeline.predict(X_test)
test_acc = accuracy_score(y_test, test_predictions)
print('Test accuracy: {:.4%}'.format(test_acc))
train_predictions = pipeline.predict(X_train)
train_acc = accuracy_score(y_train, train_predictions)
print('Train accuracy: {:.4%}'.format(train_acc))
scores = cross_val_score(
estimator=pipeline, X=X_test, y=y_test, cv=10, n_jobs=10)
print('CV score: {:.2%} (+/- {:.2%})'.format(scores.mean(), scores.std()))
print('=' * 30)
==============================
RandomForestClassifier
Test accuracy: 57.4244%
Train accuracy: 61.8739%
CV score: 57.24% (+/- 0.65%)
==============================
In [8]:
wah = clf.estimators_
In [9]:
decisiontree = clf.estimators_[0]
tree = decisiontree.tree_
# print(tree.decision_path(X_test.astype(np.float32)))
print(tree.node_count)
print(tree.n_node_samples)
print(tree.apply(X_test.astype(np.float32)))
print(tree.n_node_samples[tree.apply(X_test.astype(np.float32))])
print(tree.children_left)
leaf_samples = []
for tree in clf.estimators_:
leaf_samples.extend(tree.tree_.n_node_samples[tree.apply(X_test.astype(np.float32))])
print(np.min(leaf_samples))
print(np.max(leaf_samples))
2247
[69216 15988 11796 ..., 104 75 29]
[1860 2223 1860 ..., 1869 1860 2223]
[44 30 44 ..., 31 44 30]
[ 1 2 3 ..., 2245 -1 -1]
26
364
In [10]:
counts, bins, pathches = plt.hist(leaf_samples, bins=np.linspace(0, 200, 100), log=True)
In [16]:
# Using those arrays, we can parse the tree structure:
estimator = clf.estimators_[0]
n_nodes = estimator.tree_.node_count
children_left = estimator.tree_.children_left
children_right = estimator.tree_.children_right
feature = estimator.tree_.feature
threshold = estimator.tree_.threshold
# The tree structure can be traversed to compute various properties such
# as the depth of each node and whether or not it is a leaf.
node_depth = np.zeros(shape=n_nodes)
is_leaves = np.zeros(shape=n_nodes, dtype=bool)
stack = [(0, -1)] # seed is the root node id and its parent depth
while len(stack) > 0:
node_id, parent_depth = stack.pop()
node_depth[node_id] = parent_depth + 1
# If we have a test node
if (children_left[node_id] != children_right[node_id]):
stack.append((children_left[node_id], parent_depth + 1))
stack.append((children_right[node_id], parent_depth + 1))
else:
is_leaves[node_id] = True
print("The binary tree structure has %s nodes and has "
"the following tree structure:"
% n_nodes)
for i in range(n_nodes):
if is_leaves[i]:
print("%snode=%s leaf node." % ("\t", i))
# print("%snode=%s leaf node." % (node_depth[i] * "\t", i))
# else:
# print("%snode=%s test node: go to node %s if X[:, %s] <= %ss else to "
# "node %s."
# % (node_depth[i] * "\t",
# i,
# children_left[i],
# feature[i],
# threshold[i],
# children_right[i],
# ))
print()
The binary tree structure has 2101 nodes and has the following tree structure:
node=8 leaf node.
node=11 leaf node.
node=14 leaf node.
node=15 leaf node.
node=16 leaf node.
node=17 leaf node.
node=19 leaf node.
node=21 leaf node.
node=22 leaf node.
node=27 leaf node.
node=28 leaf node.
node=30 leaf node.
node=32 leaf node.
node=34 leaf node.
node=35 leaf node.
node=40 leaf node.
node=41 leaf node.
node=42 leaf node.
node=43 leaf node.
node=46 leaf node.
node=47 leaf node.
node=48 leaf node.
node=54 leaf node.
node=55 leaf node.
node=57 leaf node.
node=58 leaf node.
node=61 leaf node.
node=62 leaf node.
node=64 leaf node.
node=65 leaf node.
node=68 leaf node.
node=69 leaf node.
node=71 leaf node.
node=72 leaf node.
node=74 leaf node.
node=78 leaf node.
node=79 leaf node.
node=80 leaf node.
node=82 leaf node.
node=84 leaf node.
node=85 leaf node.
node=90 leaf node.
node=92 leaf node.
node=93 leaf node.
node=95 leaf node.
node=96 leaf node.
node=99 leaf node.
node=100 leaf node.
node=102 leaf node.
node=104 leaf node.
node=105 leaf node.
node=108 leaf node.
node=110 leaf node.
node=111 leaf node.
node=115 leaf node.
node=118 leaf node.
node=119 leaf node.
node=121 leaf node.
node=122 leaf node.
node=124 leaf node.
node=125 leaf node.
node=129 leaf node.
node=131 leaf node.
node=132 leaf node.
node=135 leaf node.
node=136 leaf node.
node=138 leaf node.
node=139 leaf node.
node=140 leaf node.
node=148 leaf node.
node=149 leaf node.
node=151 leaf node.
node=152 leaf node.
node=153 leaf node.
node=157 leaf node.
node=158 leaf node.
node=161 leaf node.
node=162 leaf node.
node=163 leaf node.
node=164 leaf node.
node=170 leaf node.
node=171 leaf node.
node=173 leaf node.
node=174 leaf node.
node=175 leaf node.
node=179 leaf node.
node=180 leaf node.
node=182 leaf node.
node=183 leaf node.
node=184 leaf node.
node=189 leaf node.
node=190 leaf node.
node=192 leaf node.
node=193 leaf node.
node=195 leaf node.
node=196 leaf node.
node=200 leaf node.
node=201 leaf node.
node=203 leaf node.
node=204 leaf node.
node=205 leaf node.
node=210 leaf node.
node=211 leaf node.
node=214 leaf node.
node=216 leaf node.
node=217 leaf node.
node=220 leaf node.
node=221 leaf node.
node=223 leaf node.
node=224 leaf node.
node=226 leaf node.
node=227 leaf node.
node=230 leaf node.
node=231 leaf node.
node=235 leaf node.
node=236 leaf node.
node=237 leaf node.
node=241 leaf node.
node=242 leaf node.
node=244 leaf node.
node=245 leaf node.
node=246 leaf node.
node=252 leaf node.
node=253 leaf node.
node=255 leaf node.
node=256 leaf node.
node=260 leaf node.
node=261 leaf node.
node=263 leaf node.
node=264 leaf node.
node=266 leaf node.
node=267 leaf node.
node=269 leaf node.
node=271 leaf node.
node=273 leaf node.
node=274 leaf node.
node=277 leaf node.
node=278 leaf node.
node=280 leaf node.
node=281 leaf node.
node=287 leaf node.
node=290 leaf node.
node=291 leaf node.
node=293 leaf node.
node=294 leaf node.
node=297 leaf node.
node=299 leaf node.
node=300 leaf node.
node=305 leaf node.
node=306 leaf node.
node=307 leaf node.
node=308 leaf node.
node=310 leaf node.
node=311 leaf node.
node=314 leaf node.
node=316 leaf node.
node=317 leaf node.
node=319 leaf node.
node=320 leaf node.
node=327 leaf node.
node=328 leaf node.
node=331 leaf node.
node=332 leaf node.
node=333 leaf node.
node=336 leaf node.
node=337 leaf node.
node=340 leaf node.
node=341 leaf node.
node=342 leaf node.
node=343 leaf node.
node=346 leaf node.
node=347 leaf node.
node=349 leaf node.
node=353 leaf node.
node=354 leaf node.
node=356 leaf node.
node=357 leaf node.
node=358 leaf node.
node=364 leaf node.
node=365 leaf node.
node=366 leaf node.
node=367 leaf node.
node=369 leaf node.
node=370 leaf node.
node=374 leaf node.
node=375 leaf node.
node=379 leaf node.
node=380 leaf node.
node=382 leaf node.
node=383 leaf node.
node=386 leaf node.
node=387 leaf node.
node=389 leaf node.
node=390 leaf node.
node=394 leaf node.
node=395 leaf node.
node=398 leaf node.
node=399 leaf node.
node=401 leaf node.
node=402 leaf node.
node=406 leaf node.
node=407 leaf node.
node=409 leaf node.
node=410 leaf node.
node=412 leaf node.
node=414 leaf node.
node=415 leaf node.
node=420 leaf node.
node=421 leaf node.
node=422 leaf node.
node=424 leaf node.
node=427 leaf node.
node=430 leaf node.
node=431 leaf node.
node=432 leaf node.
node=435 leaf node.
node=437 leaf node.
node=439 leaf node.
node=440 leaf node.
node=442 leaf node.
node=443 leaf node.
node=445 leaf node.
node=449 leaf node.
node=450 leaf node.
node=454 leaf node.
node=455 leaf node.
node=457 leaf node.
node=459 leaf node.
node=460 leaf node.
node=464 leaf node.
node=465 leaf node.
node=466 leaf node.
node=468 leaf node.
node=469 leaf node.
node=471 leaf node.
node=473 leaf node.
node=475 leaf node.
node=476 leaf node.
node=480 leaf node.
node=484 leaf node.
node=485 leaf node.
node=486 leaf node.
node=488 leaf node.
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()
In [11]:
comp_list = ['P', 'He', 'Fe']
# comp_list = ['P', 'Fe']
# comp_list = le.inverse_transform(np.unique(y_test))
correctly_identified_mask = (test_predictions == y_test)
# Energy-related variables
energy_bin_width = 0.1
energy_bins = np.arange(6.2, 8.1, energy_bin_width)
# energy_bins = np.arange(6.2, 9.51, energy_bin_width)
energy_midpoints = (energy_bins[1:] + energy_bins[:-1]) / 2
log_energy = X_test[:, 0]
# Construct MC composition masks
MC_comp_mask = {}
for composition in comp_list:
MC_comp_mask[composition] = (le.inverse_transform(y_test) == composition)
# Get number of MC comp in each reco energy bin
num_MC_energy, num_MC_energy_err = {}, {}
for composition in comp_list:
num_MC_energy[composition] = np.histogram(log_energy[MC_comp_mask[composition]],
bins=energy_bins)[0]
num_MC_energy_err[composition] = np.sqrt(num_MC_energy[composition])
num_MC_energy['total'] = np.histogram(log_energy, bins=energy_bins)[0]
num_MC_energy_err['total'] = np.sqrt(num_MC_energy['total'])
# Get number of correctly identified comp in each reco energy bin
num_reco_energy, num_reco_energy_err = {}, {}
for composition in comp_list:
num_reco_energy[composition] = np.histogram(
log_energy[MC_comp_mask[composition] & correctly_identified_mask],
bins=energy_bins)[0]
num_reco_energy_err[composition] = np.sqrt(num_reco_energy[composition])
num_reco_energy['total'] = np.histogram(log_energy[correctly_identified_mask], bins=energy_bins)[0]
num_reco_energy_err['total'] = np.sqrt(num_reco_energy['total'])
# Calculate correctly identified fractions as a function of MC energy
reco_frac, reco_frac_err = {}, {}
for composition in comp_list:
print(composition)
reco_frac[composition], reco_frac_err[composition] = comp.ratio_error(
num_reco_energy[composition], num_reco_energy_err[composition],
num_MC_energy[composition], num_MC_energy_err[composition])
reco_frac['total'], reco_frac_err['total'] = comp.ratio_error(
num_reco_energy['total'], num_reco_energy_err['total'],
num_MC_energy['total'], num_MC_energy_err['total'])
P
He
Fe
In [12]:
# Plot fraction of events vs energy
fig, ax = plt.subplots()
for composition in comp_list:
ebar = ax.errorbar(energy_midpoints, reco_frac[composition],
yerr=reco_frac_err[composition],
# xerr=energy_bin_width / 2,
marker=None, markersize=5,
alpha=0.8)
step_x = energy_midpoints
step_x = np.append(step_x[0]-energy_bin_width/2, step_x)
step_x = np.append(step_x, step_x[-1]+energy_bin_width/2)
step_y = reco_frac[composition]
step_y = np.append(step_y[0], step_y)
step_y = np.append(step_y, step_y[-1])
ax.step(step_x, step_y, where='mid',
marker=None, color=ebar[0].get_color(), linewidth=0.9,
linestyle='-', label=composition, alpha=0.8)
ebar = ax.errorbar(energy_midpoints, reco_frac['total'],
yerr=reco_frac_err['total'],
# xerr=energy_bin_width / 2,
marker=None, markersize=5,
alpha=0.8)
ax.step(energy_midpoints, reco_frac['total'], where='mid', marker=None,
color=ebar[0].get_color(), linewidth=0.9, label='Total', alpha=0.8)
plt.xlabel('$\log_{10}(E_{\mathrm{reco}}/\mathrm{GeV})$')
ax.set_ylabel('Fraction correctly identified')
ax.set_ylim([0.0, 1.0])
ax.set_xlim([6.2, 8.0])
# ax.set_xlim([6.2, 9.5])
plt.grid()
# leg = plt.legend(loc=3)
leg = plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05),
ncol=len(comp_list)+1, fancybox=False, shadow=False)
# set the linewidth of each legend object
for legobj in leg.legendHandles:
legobj.set_linewidth(3.0)
# place a text box in upper left in axes coords
textstr = 'Training features: \n'
for i, label in enumerate(feature_labels):
if (i == len(feature_labels)-1):
textstr += '{}) '.format(i+1) + label
else:
textstr += '{}) '.format(i+1) + label + '\n'
# print(textstr)
props = dict(facecolor='white')
# ax.text(0.0, 0.0, textstr, transform=ax.transAxes, fontsize=8,
# verticalalignment='top', bbox=props)
ax.text(1.025, 1.0, textstr, transform=ax.transAxes, fontsize=8,
verticalalignment='top', bbox=props)
outfile = '/home/jbourbeau/public_html/figures/composition' + \
'/fraction-reco-correct_vs_reco-energy_RF.png'
plt.savefig(outfile)
plt.show()
In [12]:
a = pd.DataFrame([np.sum(df.MC_comp == composition) for composition in comp_list],
index=comp_list, columns=['MC Compositions'])
print(a)
a.plot.pie(subplots=True, figsize=(4,4), legend=False, autopct='%.2f')
a = pd.DataFrame([np.sum(le.inverse_transform(test_predictions) == composition) for composition in comp_list], index=comp_list, columns=['after'])
print(a)
a.plot.pie(subplots=True, figsize=(2,2))
MC Compositions
P 53093
He 53210
Fe 50343
after
P 16936
He 14728
Fe 15330
Out[12]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0xd6fa650>], dtype=object)
In [13]:
num_features = len(feature_list)
importances = pipeline.named_steps['classifier'].feature_importances_
indices = np.argsort(importances)[::-1]
fig, ax = plt.subplots()
# feature_labels = np.array(['$\\log_{10}({\mathrm{E/GeV})$', 'InIce charge',
# '$\cos(\\theta)$', '$\mathrm{Laputop}\ \chi^2/\mathrm{n.d.f.}$', 'NChannels'])
for f in range(num_features):
print('{}) {}'.format(f + 1, importances[indices[f]]))
plt.ylabel('Feature Importances')
plt.bar(range(num_features),
importances[indices],
align='center')
plt.xticks(range(num_features),
feature_labels[indices], rotation=90)
plt.xlim([-1, len(feature_list)])
# plt.ylim([0, .40])
plt.show()
1) 0.33064538357
2) 0.187678165573
3) 0.174925540871
4) 0.159199898412
5) 0.147551011574
In [50]:
probs = pipeline.named_steps['classifier'].predict_proba(X_test)
prob_1 = probs[:, 0][MC_iron_mask]
prob_2 = probs[:, 1][MC_iron_mask]
# print(min(prob_1-prob_2))
# print(max(prob_1-prob_2))
# plt.hist(prob_1-prob_2, bins=30, log=True)
plt.hist(prob_1, bins=np.linspace(0, 1, 50), log=True)
plt.hist(prob_2, bins=np.linspace(0, 1, 50), log=True)
NameErrorTraceback (most recent call last)
<ipython-input-50-b87560b6ea5f> in <module>()
1 probs = pipeline.named_steps['classifier'].predict_proba(X_test)
----> 2 prob_1 = probs[:, 0][MC_iron_mask]
3 prob_2 = probs[:, 1][MC_iron_mask]
4 # print(min(prob_1-prob_2))
5 # print(max(prob_1-prob_2))
NameError: name 'MC_iron_mask' is not defined
In [ ]:
probs = pipeline.named_steps['classifier'].predict_proba(X_test)
dp1 = (probs[:, 0]-probs[:, 1])[MC_proton_mask]
print(min(dp1))
print(max(dp1))
dp2 = (probs[:, 0]-probs[:, 1])[MC_iron_mask]
print(min(dp2))
print(max(dp2))
fig, ax = plt.subplots()
# plt.hist(prob_1-prob_2, bins=30, log=True)
counts, edges, pathes = plt.hist(dp1, bins=np.linspace(-1, 1, 100), log=True, label='Proton', alpha=0.75)
counts, edges, pathes = plt.hist(dp2, bins=np.linspace(-1, 1, 100), log=True, label='Iron', alpha=0.75)
plt.legend(loc=2)
plt.show()
pipeline.named_steps['classifier'].classes_
In [ ]:
print(pipeline.named_steps['classifier'].classes_)
le.inverse_transform(pipeline.named_steps['classifier'].classes_)
In [ ]:
pipeline.named_steps['classifier'].decision_path(X_test)
In [ ]:
Content source: jrbourbeau/cr-composition
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