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.')
  1. Load simulation dataframe and apply specified quality cuts
  2. Extract desired features from dataframe
  3. Get separate testing and training datasets

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']


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            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=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.
	node=489 leaf node.
	node=493 leaf node.
	node=494 leaf node.
	node=496 leaf node.
	node=497 leaf node.
	node=501 leaf node.
	node=502 leaf node.
	node=504 leaf node.
	node=506 leaf node.
	node=507 leaf node.
	node=510 leaf node.
	node=512 leaf node.
	node=513 leaf node.
	node=514 leaf node.
	node=521 leaf node.
	node=522 leaf node.
	node=526 leaf node.
	node=527 leaf node.
	node=529 leaf node.
	node=531 leaf node.
	node=532 leaf node.
	node=534 leaf node.
	node=536 leaf node.
	node=537 leaf node.
	node=543 leaf node.
	node=544 leaf node.
	node=546 leaf node.
	node=547 leaf node.
	node=549 leaf node.
	node=550 leaf node.
	node=551 leaf node.
	node=553 leaf node.
	node=554 leaf node.
	node=557 leaf node.
	node=558 leaf node.
	node=559 leaf node.
	node=562 leaf node.
	node=565 leaf node.
	node=566 leaf node.
	node=567 leaf node.
	node=573 leaf node.
	node=575 leaf node.
	node=576 leaf node.
	node=578 leaf node.
	node=579 leaf node.
	node=580 leaf node.
	node=582 leaf node.
	node=585 leaf node.
	node=586 leaf node.
	node=587 leaf node.
	node=588 leaf node.
	node=594 leaf node.
	node=595 leaf node.
	node=598 leaf node.
	node=601 leaf node.
	node=602 leaf node.
	node=603 leaf node.
	node=605 leaf node.
	node=608 leaf node.
	node=609 leaf node.
	node=611 leaf node.
	node=612 leaf node.
	node=616 leaf node.
	node=617 leaf node.
	node=618 leaf node.
	node=619 leaf node.
	node=621 leaf node.
	node=624 leaf node.
	node=625 leaf node.
	node=629 leaf node.
	node=630 leaf node.
	node=631 leaf node.
	node=634 leaf node.
	node=635 leaf node.
	node=636 leaf node.
	node=644 leaf node.
	node=645 leaf node.
	node=646 leaf node.
	node=649 leaf node.
	node=650 leaf node.
	node=652 leaf node.
	node=653 leaf node.
	node=657 leaf node.
	node=658 leaf node.
	node=660 leaf node.
	node=661 leaf node.
	node=662 leaf node.
	node=663 leaf node.
	node=667 leaf node.
	node=668 leaf node.
	node=670 leaf node.
	node=671 leaf node.
	node=676 leaf node.
	node=677 leaf node.
	node=678 leaf node.
	node=680 leaf node.
	node=681 leaf node.
	node=685 leaf node.
	node=686 leaf node.
	node=687 leaf node.
	node=689 leaf node.
	node=690 leaf node.
	node=692 leaf node.
	node=694 leaf node.
	node=698 leaf node.
	node=700 leaf node.
	node=701 leaf node.
	node=704 leaf node.
	node=705 leaf node.
	node=707 leaf node.
	node=708 leaf node.
	node=709 leaf node.
	node=717 leaf node.
	node=721 leaf node.
	node=723 leaf node.
	node=724 leaf node.
	node=727 leaf node.
	node=728 leaf node.
	node=729 leaf node.
	node=732 leaf node.
	node=734 leaf node.
	node=735 leaf node.
	node=737 leaf node.
	node=738 leaf node.
	node=742 leaf node.
	node=743 leaf node.
	node=747 leaf node.
	node=748 leaf node.
	node=750 leaf node.
	node=751 leaf node.
	node=752 leaf node.
	node=754 leaf node.
	node=756 leaf node.
	node=757 leaf node.
	node=762 leaf node.
	node=763 leaf node.
	node=765 leaf node.
	node=766 leaf node.
	node=771 leaf node.
	node=772 leaf node.
	node=773 leaf node.
	node=776 leaf node.
	node=777 leaf node.
	node=779 leaf node.
	node=780 leaf node.
	node=783 leaf node.
	node=784 leaf node.
	node=785 leaf node.
	node=790 leaf node.
	node=792 leaf node.
	node=793 leaf node.
	node=794 leaf node.
	node=797 leaf node.
	node=798 leaf node.
	node=800 leaf node.
	node=801 leaf node.
	node=806 leaf node.
	node=807 leaf node.
	node=808 leaf node.
	node=810 leaf node.
	node=812 leaf node.
	node=813 leaf node.
	node=816 leaf node.
	node=818 leaf node.
	node=819 leaf node.
	node=822 leaf node.
	node=823 leaf node.
	node=825 leaf node.
	node=826 leaf node.
	node=834 leaf node.
	node=835 leaf node.
	node=836 leaf node.
	node=839 leaf node.
	node=840 leaf node.
	node=842 leaf node.
	node=843 leaf node.
	node=847 leaf node.
	node=848 leaf node.
	node=850 leaf node.
	node=851 leaf node.
	node=854 leaf node.
	node=855 leaf node.
	node=857 leaf node.
	node=858 leaf node.
	node=862 leaf node.
	node=863 leaf node.
	node=866 leaf node.
	node=867 leaf node.
	node=869 leaf node.
	node=870 leaf node.
	node=873 leaf node.
	node=875 leaf node.
	node=876 leaf node.
	node=879 leaf node.
	node=880 leaf node.
	node=881 leaf node.
	node=887 leaf node.
	node=888 leaf node.
	node=890 leaf node.
	node=891 leaf node.
	node=894 leaf node.
	node=895 leaf node.
	node=897 leaf node.
	node=898 leaf node.
	node=902 leaf node.
	node=903 leaf node.
	node=905 leaf node.
	node=906 leaf node.
	node=909 leaf node.
	node=910 leaf node.
	node=911 leaf node.
	node=916 leaf node.
	node=917 leaf node.
	node=919 leaf node.
	node=920 leaf node.
	node=923 leaf node.
	node=924 leaf node.
	node=925 leaf node.
	node=929 leaf node.
	node=930 leaf node.
	node=932 leaf node.
	node=933 leaf node.
	node=936 leaf node.
	node=937 leaf node.
	node=938 leaf node.
	node=943 leaf node.
	node=945 leaf node.
	node=946 leaf node.
	node=947 leaf node.
	node=952 leaf node.
	node=953 leaf node.
	node=954 leaf node.
	node=956 leaf node.
	node=958 leaf node.
	node=959 leaf node.
	node=963 leaf node.
	node=964 leaf node.
	node=965 leaf node.
	node=968 leaf node.
	node=969 leaf node.
	node=971 leaf node.
	node=972 leaf node.
	node=978 leaf node.
	node=979 leaf node.
	node=981 leaf node.
	node=982 leaf node.
	node=983 leaf node.
	node=986 leaf node.
	node=987 leaf node.
	node=990 leaf node.
	node=991 leaf node.
	node=992 leaf node.
	node=993 leaf node.
	node=998 leaf node.
	node=999 leaf node.
	node=1002 leaf node.
	node=1005 leaf node.
	node=1006 leaf node.
	node=1008 leaf node.
	node=1009 leaf node.
	node=1014 leaf node.
	node=1015 leaf node.
	node=1017 leaf node.
	node=1018 leaf node.
	node=1020 leaf node.
	node=1021 leaf node.
	node=1025 leaf node.
	node=1026 leaf node.
	node=1027 leaf node.
	node=1029 leaf node.
	node=1030 leaf node.
	node=1035 leaf node.
	node=1036 leaf node.
	node=1038 leaf node.
	node=1041 leaf node.
	node=1042 leaf node.
	node=1044 leaf node.
	node=1045 leaf node.
	node=1050 leaf node.
	node=1051 leaf node.
	node=1053 leaf node.
	node=1054 leaf node.
	node=1055 leaf node.
	node=1057 leaf node.
	node=1059 leaf node.
	node=1060 leaf node.
	node=1063 leaf node.
	node=1064 leaf node.
	node=1065 leaf node.
	node=1072 leaf node.
	node=1073 leaf node.
	node=1074 leaf node.
	node=1076 leaf node.
	node=1077 leaf node.
	node=1079 leaf node.
	node=1080 leaf node.
	node=1084 leaf node.
	node=1087 leaf node.
	node=1088 leaf node.
	node=1090 leaf node.
	node=1091 leaf node.
	node=1093 leaf node.
	node=1094 leaf node.
	node=1097 leaf node.
	node=1098 leaf node.
	node=1102 leaf node.
	node=1103 leaf node.
	node=1105 leaf node.
	node=1106 leaf node.
	node=1108 leaf node.
	node=1110 leaf node.
	node=1111 leaf node.
	node=1114 leaf node.
	node=1116 leaf node.
	node=1118 leaf node.
	node=1119 leaf node.
	node=1122 leaf node.
	node=1125 leaf node.
	node=1126 leaf node.
	node=1128 leaf node.
	node=1130 leaf node.
	node=1131 leaf node.
	node=1132 leaf node.
	node=1142 leaf node.
	node=1143 leaf node.
	node=1145 leaf node.
	node=1146 leaf node.
	node=1148 leaf node.
	node=1150 leaf node.
	node=1151 leaf node.
	node=1155 leaf node.
	node=1156 leaf node.
	node=1158 leaf node.
	node=1159 leaf node.
	node=1162 leaf node.
	node=1163 leaf node.
	node=1165 leaf node.
	node=1166 leaf node.
	node=1171 leaf node.
	node=1172 leaf node.
	node=1174 leaf node.
	node=1175 leaf node.
	node=1177 leaf node.
	node=1179 leaf node.
	node=1180 leaf node.
	node=1184 leaf node.
	node=1185 leaf node.
	node=1187 leaf node.
	node=1188 leaf node.
	node=1190 leaf node.
	node=1192 leaf node.
	node=1193 leaf node.
	node=1198 leaf node.
	node=1200 leaf node.
	node=1201 leaf node.
	node=1204 leaf node.
	node=1205 leaf node.
	node=1206 leaf node.
	node=1208 leaf node.
	node=1209 leaf node.
	node=1214 leaf node.
	node=1215 leaf node.
	node=1217 leaf node.
	node=1218 leaf node.
	node=1221 leaf node.
	node=1222 leaf node.
	node=1224 leaf node.
	node=1225 leaf node.
	node=1229 leaf node.
	node=1230 leaf node.
	node=1232 leaf node.
	node=1233 leaf node.
	node=1234 leaf node.
	node=1240 leaf node.
	node=1241 leaf node.
	node=1244 leaf node.
	node=1245 leaf node.
	node=1247 leaf node.
	node=1248 leaf node.
	node=1252 leaf node.
	node=1253 leaf node.
	node=1255 leaf node.
	node=1256 leaf node.
	node=1257 leaf node.
	node=1262 leaf node.
	node=1263 leaf node.
	node=1265 leaf node.
	node=1266 leaf node.
	node=1269 leaf node.
	node=1270 leaf node.
	node=1272 leaf node.
	node=1273 leaf node.
	node=1277 leaf node.
	node=1278 leaf node.
	node=1280 leaf node.
	node=1281 leaf node.
	node=1284 leaf node.
	node=1285 leaf node.
	node=1286 leaf node.
	node=1291 leaf node.
	node=1292 leaf node.
	node=1294 leaf node.
	node=1295 leaf node.
	node=1299 leaf node.
	node=1300 leaf node.
	node=1301 leaf node.
	node=1304 leaf node.
	node=1305 leaf node.
	node=1307 leaf node.
	node=1308 leaf node.
	node=1312 leaf node.
	node=1314 leaf node.
	node=1315 leaf node.
	node=1316 leaf node.
	node=1320 leaf node.
	node=1321 leaf node.
	node=1323 leaf node.
	node=1324 leaf node.
	node=1326 leaf node.
	node=1327 leaf node.
	node=1335 leaf node.
	node=1336 leaf node.
	node=1338 leaf node.
	node=1339 leaf node.
	node=1342 leaf node.
	node=1343 leaf node.
	node=1345 leaf node.
	node=1346 leaf node.
	node=1350 leaf node.
	node=1351 leaf node.
	node=1353 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 [ ]: