In [1]:
import sys
sys.path.append('/home/jbourbeau/cr-composition')
print('Added to PYTHONPATH')
Added to PYTHONPATH
In [2]:
from __future__ import division
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.neighbors import KNeighborsClassifier
from sklearn.model_selection import validation_curve, GridSearchCV, cross_val_score, ParameterGrid
import composition as comp
%matplotlib inline
/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 [3]:
sns.set_palette('muted')
sns.set_color_codes()
In [4]:
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 [5]:
pipeline = comp.get_pipeline('AB')
param_range = np.linspace(0.2, 1.0, 20)
# param_range = np.arange(1, 20)
train_scores, test_scores = validation_curve(
estimator=pipeline,
X=X_train,
y=y_train,
param_name='classifier__learning_rate',
# param_name='classifier__max_depth',
param_range=param_range,
cv=10,
verbose=3,
n_jobs=20)
train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
plt.plot(param_range, train_mean,
color='b', marker='o',
markersize=5, label='training accuracy')
plt.fill_between(param_range, train_mean + train_std,
train_mean - train_std, alpha=0.15,
color='b')
plt.plot(param_range, test_mean,
color='g', linestyle='None',
marker='s', markersize=5,
label='validation accuracy')
plt.fill_between(param_range,
test_mean + test_std,
test_mean - test_std,
alpha=0.15, color='g')
plt.grid()
# plt.xscale('log')
plt.legend()
# plt.legend(loc='lower right')
plt.xlabel('Learning rate')
plt.ylabel('Accuracy [\%]')
# plt.ylim([0.8, 1.0])
# plt.tight_layout()
plt.savefig('/home/jbourbeau/public_html/figures/composition/parameter-tuning/AdaBoost-validation_curve_learning_rate.png', dpi=300)
# plt.show()
[CV] classifier__learning_rate=0.2 ...................................
[CV] classifier__learning_rate=0.242105263158 ........................
[CV] classifier__learning_rate=0.284210526316 ........................
[CV] classifier__learning_rate=0.326315789474 ........................
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[CV] classifier__learning_rate=0.410526315789 ........................
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[CV] classifier__learning_rate=0.831578947368 ........................
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[CV] classifier__learning_rate=0.957894736842 ........................
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[CV] classifier__learning_rate=0.410526315789, score=0.544866 - 0.2s
[CV] classifier__learning_rate=0.242105263158 ........................
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[CV] classifier__learning_rate=0.410526315789 ........................
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[CV] classifier__learning_rate=0.494736842105 ........................
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[CV] classifier__learning_rate=0.957894736842 ........................
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[Parallel(n_jobs=20)]: Done 67 out of 200 | elapsed: 1.3min remaining: 2.6min
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[CV] classifier__learning_rate=0.536842105263, score=0.538896 - 0.3s
[CV] classifier__learning_rate=0.452631578947 ........................
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[CV] classifier__learning_rate=0.536842105263 ........................
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[CV] classifier__learning_rate=0.621052631579 ........................
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[Parallel(n_jobs=20)]: Done 134 out of 200 | elapsed: 2.7min remaining: 1.3min
[CV] classifier__learning_rate=0.747368421053, score=0.539079 - 0.3s
[CV] classifier__learning_rate=0.789473684211 ........................
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[CV] classifier__learning_rate=1.0 ...................................
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[CV] classifier__learning_rate=0.452631578947, score=0.541591 - 0.3s
[CV] classifier__learning_rate=0.536842105263, score=0.543597 - 0.4s
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[CV] classifier__learning_rate=0.663157894737, score=0.544692 - 0.3s
[CV] classifier__learning_rate=0.621052631579, score=0.541408 - 0.4s
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[CV] classifier__learning_rate=0.705263157895, score=0.544327 - 0.2s
[CV] classifier__learning_rate=0.957894736842, score=0.540679 - 0.2s
[CV] classifier__learning_rate=0.789473684211, score=0.542594 - 0.2s
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[Parallel(n_jobs=20)]: Done 200 out of 200 | elapsed: 4.0min finished
In [4]:
max_depth_list = [2, 8, 10, 20]
fig, axarr = plt.subplots(2,2)
for depth, ax in zip(max_depth_list, axarr.flatten()):
pipeline = get_pipeline('RF')
pipeline.named_steps['classifier'].set_params(max_depth=depth)
pipeline.fit(X_train, y_train)
scaler = pipeline.named_steps['scaler']
clf = pipeline.named_steps['classifier']
X_test_std = scaler.transform(X_test)
plot_decision_regions(X_test_std, y_test, clf, scatter_fraction=None, ax=ax)
ax.set_xlabel('Scaled energy')
ax.set_ylabel('Scaled charge')
ax.set_title('Max depth = {}'.format(depth))
ax.legend()
plt.tight_layout()
plt.savefig('/home/jbourbeau/public_html/figures/composition/parameter-tuning/RF-decision-regions.png')
/home/jbourbeau/.local/lib/python2.7/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.
warnings.warn("No labelled objects found. "
In [5]:
pipeline = get_pipeline('RF')
param_range = np.arange(1, 20)
param_grid = {'classifier__max_depth': param_range}
gs = GridSearchCV(estimator=pipeline,
param_grid=param_grid,
scoring='accuracy',
cv=10,
n_jobs=10)
gs = gs.fit(X_train, y_train)
print(gs.best_score_)
print(gs.best_params_)
0.7872226199
{'classifier__max_depth': 10}
In [ ]:
Content source: jrbourbeau/cr-composition
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